Copyright 2011–2012, University of Glasgow noleaders

# Managing Research Data in Big Science

Norman Gray  Tobia Carozzi and Graham Woan
SUPA School of Physics and Astronomy  University of Glasgow

(2011 July 14, v1.1)

Version | v1.1
---|---
URL | http://purl.org/nxg/projects/mrd-gw/report
Distribution | Public

This report was prepared as part of the RDMP strand of the JISC programme
Managing Research Data.

## Abstract

The project which led to this report was funded by JISC in 2010–2011 as part
of its ‘Managing Research Data’ programme, to examine the way in which Big
Science data is managed, and produce any recommendations which may be
appropriate.

Big science data is different: it comes in large volumes, and it is shared and
exploited in ways which may differ from other disciplines. This project has
explored these differences using as a case-study Gravitational Wave data
generated by the LSC, and has produced recommendations intended to be useful
variously to JISC, the funding council (STFC) and the LSC community.

In Sect. 1 we define what we mean by ‘big science’, describe the overall data
culture there, laying stress on how it necessarily or contingently differs
from other disciplines.

In Sect. 2 we discuss the benefits of a formal data-preservation strategy, and
the cases for open data and for well-preserved data that follow from that.
This leads to our recommendations that, in essence, funders should adopt
rather light-touch prescriptions regarding data preservation planning: normal
data management practice, in the areas under study, corresponds to notably
good practice in most other areas, so that the only change we suggest is to
make this planning more formal, which makes it more easily auditable, and more
amenable to constructive criticism.

In Sect. 3 we briefly discuss the LIGO data management plan, and pull together
whatever information is available on the estimation of digital preservation
costs.

The report is informed, throughout, by the OAIS reference model for an open
archive. Some of the report’s findings and conclusions were summarised in [1].

See the document history on page About this document.

This report was prepared for, and funded by, the RDMP strand of the JISC
programme Managing Research Data.

Release: 7b021525c2d7, 2012-07-17 09:55 +0100

. This work is licensed under the Creative Commons Attribution-Share Alike 2.5
UK: Scotland Licence. To view a copy of this licence, visit
http://creativecommons.org/licenses/by-sa/2.5/scotland/.

###### Contents

  1. 0 Introduction
    1. 0.1 Project Background
    2. 0.2 How to read this document
    3. 0.3 Working with communities – pragmatics
  2. 1 Data management in Big Science
    1. 1.1 LIGO in perspective: LIGO, big science, and astronomy
    2. 1.2 Data volumes
    3. 1.3 Data management styles in the physical sciences
    4. 1.4 Astronomy data
      1. 1.4.1 Strasbourg Data Centre (CDS) as a disciplinary repository
      2. 1.4.2 Collaborations in astronomy
    5. 1.5 High Energy Physics data
    6. 1.6 Gravitational wave physics
      1. 1.6.1 Gravitational wave consortia
      2. 1.6.2 GW data
      3. 1.6.3 Gravitational wave data releases
      4. 1.6.4 Summary: big-science preservation challenges
    7. 1.7 A contrast: social science data
    8. 1.8 Babylonian data management (less contrast than you’d think)
    9. 1.9 Bibliographic repositories
    10. 1.10 Virtual Observatories
    11. 1.11 Data products and proprietary periods: reifying data management and release
  3. 2 The responsibilities for data preservation
    1. 2.1 Visualising benefits
    2. 2.2 The case for open data
    3. 2.3 The case for data preservation
    4. 2.4 Should raw data be preserved?
    5. 2.5 OAIS: suitability and motivation
    6. 2.6 What should big-science funders require, or provide?
  4. 3 The practicalities of data preservation
    1. 3.1 Modelling the archive
      1. 3.1.1 The OAIS model
      2. 3.1.2 The DCC Curation Lifecycle model
    2. 3.2 Software preservation
    3. 3.3 Data management planning
      1. 3.3.1 DMP in space
      2. 3.3.2 Current and future DMP in the LSC
    4. 3.4 Data preservation costs
    5. 3.5 The GW community and the AIDA toolkit
  5. 4 Conclusions and recommendations
  6. A Case study
  7. B AIDA assessment
  8. About this document

##  0 Introduction

Astronomy is as old as human culture. Early agricultural civilisations
required reliable predictions of the positions and motions of the Sun and
Moon, in order to predict in turn seasons, tides, and river risings. Even in
the absence of an extensive scientific model, these predictions relied on
careful observations, preserved in the form of almanacs or ephemerides.
Documents such as these associate astronomy with not only the first data
archives but, since these artifacts still exist, also the oldest data archives
in the world. Long-term digital preservation in astronomy is nothing new. We
cannot resist saying more about this, in Sect. 1.8.

Astronomical archiving does however evolve, and in the last few decades both
astronomy and particle physics have had to become leaders in large-scale data
management.

Although astronomical images (now all born digital) have always been
substantial in size, they have generally been reasonably manageable. Newer
astronomical techniques – and we are thinking of 21st century radio astronomy
and gravitational astronomy – are capable of generating truly challenging
quantities of data; and particle physics has been generating, and addressing,
intimidating data problems for decades. These problems cover both the
management and preservation of large data volumes, as technical problems, and
the preservation of the data’s information content, on substantially varying
timescales.

###  0.1 Project Background

The Managing Research Data/Gravitational Waves project (MRD-GW) is concerned
with the data management arrangements of the LIGO Scientific Collaboration
(LSC), and of the broader Gravitational Wave (GW) community. It is one of the
six projects in the RDMP strand of the JISC Managing Research Data (MRD)
programme [2].

The GW community was selected by the Science and Technology Facilities Council
(STFC), at JISC’s invitation, as a representative example of big-science data
management practice – as we elaborate below, it has features of both the
traditional astronomy and HEP communities, without being identifiable with
either of them. While many of the specifics, below, relate to this community,
we believe much of the discussion is relevant to the other disciplines. Here,
we are focusing on the big-science projects which receive strategic support
from STFC, rather than the smaller-scale projects funded by specific research
grants, since it is these large-scale projects that are distinctive about
STFC-funded research. We assume that the outputs of the smaller projects will
be managed through disciplinary repositories, in a manner which more closely
resembles that of other research councils.

The MRD-GW project exists to inform three sets of stakeholders:

  * •

Although the Joint Information Systems Committee (JISC) and the Digital
Curation Centre (DCC) have extensive experience with digital libraries and
digital curation in general, there are problems specific to ‘big science’ data
which JISC would like to better understand.

  * •

The Research Councils have recently started to require bidders to include a
‘data management plan’ within project proposals. However there is no consensus
on what such a plan should look like for science funded by the STFC. The US
National Science Foundation (NSF) has recently placed binding requirements on
projects to produce data management plans [3].

  * •

The LSC community has considerable internal software and administration
experience, and has solved a large number of data management problems focused
on large-scale data storage and transport. However there is an awareness that
(partly because there have been no immediate imperatives to do so) there was
until recently no published plan for a long-term data archive.

The existence of these three groups is reflected in the overall structure of
the document.

This project’s context also includes the broad Virtual Observatory (VO)
movement, which aims to develop standards and areas of consensus which help
scientists have ready access to astronomical data across sub-disciplines and
wavelengths. All the stakeholder groups have interests in the success of the
VO movement.

The project aims to bring together two sets of practice, namely the long-term
digital preservation perspectives represented by the OAIS reference model in
the abstract and the DCC in particular, and the very considerable experience
of practical large-scale data management, embedded within the LSC
community.††margin:  For OAIS, see [4] and Sect. 3.1.1; in this report the
‘DCC’ is the JISC Digital Curation Centre, not the LIGO Document Control
Center. ††margin:  For OAIS, see [4] and Sect. 3.1.1; in this report the ‘DCC’
is the JISC Digital Curation Centre, not the LIGO Document Control Center.

###  0.2 How to read this document

This document is organised into three main sections, broadly corresponding to
the three audiences we are addressing.

Sect. 1 is about data management in big science. It is addressed to the JISC
and to the data preservation community in general, and is intended to
illuminate the ways in which scientists in these areas have distinctive data
management requirements, and a distinctive data culture, which contrasts
informatively with other disciplines.

Sect. 2 is primarily addressed to STFC and other similar funders of this type
of science. It is concerned with the responsibilities which are imposed on
funders by the wider society, and which are passed on to the funded through
requirements on the governance of projects and the availability of data. The
recommendations here are concerned with how best to express these
responsibilities.

Finally, Sect. 3 is primarily addressed to the LSC, as a proxy for similar
big-science projects. The explicit recommendations here are intended to be of
as much interest to projects, as actions they may wish to take, as to funders,
as behaviour it may be prudent or productive to require.

###  0.3 Working with communities – pragmatics

This report is the result of a fruitful collaboration with the GW community.
It may be useful to note some of the features of the project, and the
community, which contributed to this.

  * •

The project team, as part of Glasgow University, has current involvement in
the community, and the project director (Woan) is a senior figure there.

  * •

The LIGO community is already aware of the general need for data management,
and the specific need for preservation (see [5]).

  * •

The project personnel have relevant scientific background, and are to some
extent in the position of being informatics-for-astronomy specialists (ie
we’re ‘insiders’).

  * •

The community is large and (via studies such as [6]) has some experience of
being ‘studied’.

  * •

The existing LVC workshop series meant that we could contact relevant people
easily in a context where newcomers were expected, and we didn’t have to add
our own data management workshop.

##  1 Data management in Big Science

###  1.1 LIGO in perspective: LIGO, big science, and astronomy

What is ‘big science’?

Big science projects tend to share many features which distinguish them from
the way that experimental science has worked in the past. Such projects share
(non-independent) features such as:

big discoveries

These projects are expected to be amongst the most important ones of their
generation. Although there is very high confidence that their headline science
goals (for example the Higgs and GW searches) will be successful, they are
also expected to produce long lists of unexpected results, and a broad range
of engineering spinoffs.

big money

These are decades-long projects, supported by country-scale funders and
billion-Euro budgets (the total budget for the LHC is around three billion
Euros††margin:
http://askanexpert.web.cern.ch/AskAnExpert/en/Accelerators/LHCgeneral-en.html
††margin:
http://askanexpert.web.cern.ch/AskAnExpert/en/Accelerators/LHCgeneral-en.html
, not including the detectors, nor the personnel and hardware costs directly
supported by country funders, which cost between one and two times that sum).

big author lists

The projects involve collaborations of hundreds of people (the LSC author list
runs at around 600 people (cf Sect. 1.6.1), and the LHC’s ATLAS detector
author list is around 3000).

big data

Enhanced- and Advanced-LIGO (for example) will produce of order
$1\text{\,}\mathrm{PB}\text{\,}{\mathrm{yr}}^{-1}$, comparable to the ATLAS
detector’s $10\text{\,}\mathrm{PB}\text{\,}{\mathrm{yr}}^{-1}$; the eventual
SKA data volumes will dwarf these.

big admin

MOUs, councils, workshop series.

big careers

Individuals may make the journey from PhD to chair on a single project.

There is a discussion of the features of ‘big science’, and LIGO’s progress
towards that style of working, in [7], with an extended history of the sub-
discipline in [6].

Because of the large costs involved and because there is usually little
immediate commercial value in this research (though of course there are
substantial long-term economic payoffs for the investing countries), these
large projects are funded at the national or international level, so that
taxpayers are the ultimate stakeholders. Even putting aside the scientific and
scholarly need for adequate data preservation, these national investments make
it necessary for funders both to demonstrate that projects are being
efficiently exploited to produce macro-economic value, and to make the data
products available for public use. We discuss open data in Sect. 2.2

###  1.2 Data volumes

The most immediate problem with data curation and sharing in these scientific
areas – though in the end not the most significant one – is the data volumes
involved. The current volume of LIGO data is of the order of hundreds of
terabytes, and the data rates is expected to grow, over the course of the
project, from its current $100\text{\,}\mathrm{TB}\text{\,}{\mathrm{yr}}^{-1}$
to around $1\text{\,}\mathrm{PB}\text{\,}{\mathrm{yr}}^{-1}$ (see Table 1,
which shows the variation in data size for science runs three to six††margin:
In the context of larger-scale projects, a ‘science run’ is a period when the
equipment is run more-or-less continuously, gathering scientifically useful
data. Between science runs, the experiment will either be down for
maintenance, or on a planned ‘engineering run’; data from engineering runs is
generally stored, but is not expected to be useful to scientists. ††margin:
In the context of larger-scale projects, a ‘science run’ is a period when the
equipment is run more-or-less continuously, gathering scientifically useful
data. Between science runs, the experiment will either be down for
maintenance, or on a planned ‘engineering run’; data from engineering runs is
generally stored, but is not expected to be useful to scientists. ).

| S3 | S4 | S5 | S6
---|---|---|---|---
L0 | $57$ | $32$ | $816$ | $261$
L1 | $8.24$ | $4.04$ | $119$ | $76$
L2 | $1.55$ |  |  |
L3 | $0.97$ | $0.86$ | $9.70$ | $3$
(duration/day) | $70$ | $29$ | $695$ | $482$
Table 1: LIGO data set size estimates in TB, and run lengths in days, for
science runs three to six (‘S$n$’), and various data types (size data taken
from [8]; there were a total of six science runs in LIGO; L0 is the run’s raw
dataset, and L1 to L3 are progressively reduced).

LIGO is just one of several other existing or planned big physics projects,
including the LHC, the Square Kilometre Array (SKA), and various European
Space Agency (ESA)/NASA space missions. In comparison with these projects,
LIGO’s data handling requirements are relatively modest. The LHC will have
data volumes of tens of $\mathrm{PB}\text{\,}{\mathrm{yr}}^{-1}$††margin:
ATLAS, one of the two larger LHC detectors, stores
$3\text{\,}\mathrm{PB}\text{\,}{\mathrm{yr}}^{-1}$ by itself; see
http://atlas.ch/pdf/atlas_factsheet_4.pdf for some entertaining numbers.
††margin:  ATLAS, one of the two larger LHC detectors, stores
$3\text{\,}\mathrm{PB}\text{\,}{\mathrm{yr}}^{-1}$ by itself; see
http://atlas.ch/pdf/atlas_factsheet_4.pdf for some entertaining numbers.
Further in the future, the SKA (which is due to be commissioned around 2020)
has predicted requirements up to
$1\text{\,}\mathrm{Tbit}\text{\,}{\mathrm{s}}^{-1}$ locally and
$100\text{\,}\mathrm{Gbit}\text{\,}{\mathrm{s}}^{-1}$ intercontinentally; this
involves transporting, though not necessarily storing, around
$1\text{\,}\mathrm{TB}\text{\,}{\mathrm{min}}^{-1}$ or
$0.5\text{\,}\mathrm{EB}\text{\,}{\mathrm{yr}}^{-1}$ [9]. This is 0.05% of the
predicted $1\text{\,}\mathrm{ZB}\text{\,}{\mathrm{yr}}^{-1}$ _total_ worldwide
IP traffic for 2015 [10].††margin:  $1\text{\,}\mathrm{PB}$ is
$1000\text{\,}\mathrm{TB}$; $1\text{\,}\mathrm{EB}$ is
$1000\text{\,}\mathrm{PB}$; $1\text{\,}\mathrm{ZB}$ is
$1000\text{\,}\mathrm{EB}$; note that the unit B refers to bytes, not bits
††margin:  $1\text{\,}\mathrm{PB}$ is $1000\text{\,}\mathrm{TB}$;
$1\text{\,}\mathrm{EB}$ is $1000\text{\,}\mathrm{PB}$; $1\text{\,}\mathrm{ZB}$
is $1000\text{\,}\mathrm{EB}$; note that the unit B refers to bytes, not bits

Large-scale physical science experiments have long produced significant data
volumes, but in recent years datasets appear to be increasing in volume and in
complexity at an overwhelming rate, and this may present a qualitatively
different data management problem. This is sometimes described in rather
apocalyptic terms – as a ‘data deluge’ or the like – and some of the
challenges and opportunities are described in [11].

###  1.3 Data management styles in the physical sciences

It seems useful to discuss, here, some of the distinctive features of data
collection and management in the experimental physical sciences, since these
have an impact on both the expectations for, and the problems with, the data.

Big-science research projects have a number of relevant common features:

Large data sets

Such projects’ data sets are ‘large’ in the objective sense that the projects
are typically so greedy for data storage, that their holdings are near the
edge of what it is technically feasible to store and transport.

Innovative data management

As part of the response to their need for large data volumes, big-science
projects are often extremely innovative in their solutions to data management
problems, to the extent that they are willing to work with experimental
filesystem types, or adapt and extend operating system software or network
transport protocols (see http://lcg.web.cern.ch/ to get an impression of the
scope of development efforts here).

Specialised software

Because the instruments and their data sets are so complicated, these projects
typically generate large custom data analysis software suites. These may
require specialised and unwritten knowledge to use, and therefore appear to
represent a significant software preservation challenge.

Beyond the substantial software engineering challenges described above, the
physical sciences tend to have few ‘IT’ problems, since the communities
contain plenty of people with sufficient technological nous to address
essentially all day-to-day computing-related problems, and these communities
are therefore generally reasonably well-organised with regard to backups,
storage, and basic file sharing (see also the discussion of technological
readiness in Sect. 3.5). At the same time, however, the communities are rather
conservative from the point of view of a computer scientist, and sometimes
rather informal from the point of view of a software engineer. That is, the
attitude to custom computing solutions is very similar to the attitude to
custom lab hardware: it may need to be creative and experimental, but never
for its own sake; it must be stable, but is never frozen; it is accurately
made, but rarely polished. The analogy with lab hardware and software holds to
the extent that, in the LHC community, data management groups are regarded as
detector subsystem groups; that is, they have the same general status as the
magnet or accelerator engineers, and expected to produce agile and innovative
computing services very different from the more routine, lab-wide, provision
of CERN IT services.††margin:  One of the DCC researchers, commenting on this
report, quoted a GridPP survey respondent commenting that the LHC computing
task: ‘‘Providing resilient services that maintain access to data for the
experiment users 24/7 – services are complex, bleeding edge, and are
constantly being updated. Controlling that process, whilst also maintaining
service up-time is very challenging’’. ††margin:  One of the DCC researchers,
commenting on this report, quoted a GridPP survey respondent commenting that
the LHC computing task: ‘‘Providing resilient services that maintain access to
data for the experiment users 24/7 – services are complex, bleeding edge, and
are constantly being updated. Controlling that process, whilst also
maintaining service up-time is very challenging’’.

The result of this is that lab software represents functional solutions to
immediate-term problems, generally with flexibility enough to respond to
medium-term problems, but without much attention being given to the
imponderables of the long term, after the experiment has completed. It is
precisely these Long Term preservation questions, in the OAIS sense of more
than one technology generation, that are the concern of this report.

There is plenty of prior art in this area. See reference [12] for a review of
data management practice in a variety of scholarly areas, which additionally
covers several proposed life-cycle models, and analysis techniques. There is a
similar overview in the PARSE.Insight case-studies report [13], which examines
data management practice in HEP, earth observation, and social science and
humanities. These case-studies were conducted via interviews, and
participation in ongoing efforts within the communities. The same project
produced a gap analysis and roadmap, which make valuable reading.

This is a good place to stress that ‘big science’ generally handles its data
well, and can even be regarded as exemplary (compare Sect. 3.5). There are a
few features which naturally encourage good data management practice in the
large-scale physical sciences.

  * •

These are often relatively well-resourced projects, with plenty of computing
experience and lots of engineering management. There is lots of obvious
infrastructure in the development of a large collaborative experiment, which
gives data management an obvious budgetary home, where it is not competing
with funding which directly supports researchers.

  * •

Astronomy and HEP projects have always produced ‘large’ data volumes: this
makes _ad hoc_ data management manifestly unattractive, and encourages
explicit data management planning and discipline.

  * •

The scale of these experiments means that they tend to be shared facilities
providing documented services to their users, so that documented interfaces
and SLAs are natural.

  * •

These projects rarely if ever produce commercially sensitive data, so that the
confidentiality concerns are well circumscribed, concerning professional
priority rather than IPR or other financial worries.

Although these features are to a greater or lesser extent specific to this
type of science, they have given rise to the notions of _data products_ and
explicit _proprietary periods_ , which we believe would be useful in other
areas, and which we discuss in Sect. 1.11.

Although it is GW data which is our nominal focus in this report, it is
convenient to first describe general astronomy data, then distinguish that
from High Energy Physics (HEP) data, which has a somewhat different data
culture, and then describe how the GW community, which is in many ways
intermediate between the two, handles its data.

###  1.4 Astronomy data

Astronomy (excluding GW astronomy for the moment) has probably the most
straightforward data management practices in the physical sciences. When an
optical telescope takes an image (or a spectrum, which for our present
purposes is technically equivalent to an image), either as part of a
systematic survey of the whole sky or as a pointed observation requested by an
astronomer, the image is typically moved from the telescope’s detector
straight into its archive, from where it can be later retrieved by the
astronomer, accompanied by automatic or manually-added metadata.

Non-optical astronomers (covering the rest of the spectrum, from radio to
gamma rays), and most satellite missions, have a somewhat more complicated
route from observation to image, and a broader set of data products, but have
essentially the same model, and the same discipline and expectations around
archives. From the point of view of data management therefore, we can elide
the differences between the various branches of astronomy. Gravitational wave
and neutrino astronomy, in contrast, are not studying the electromagnetic
spectrum, and partly as a consequence their study more closely resembles
particle physics (see Sects. 1.5 and 1.6 below).

Most large telescopes, satellites and instruments††margin:  An ‘instrument’ in
this context is the light-sensitive detector attached to the telescope or
satellite optics (the camera, in effect). It is replaceable or swappable, and
regarded as a separate piece of engineering from the telescope. The days when
observers would travel to the telescope carrying their own instrument are now
largely past. ††margin:  An ‘instrument’ in this context is the light-
sensitive detector attached to the telescope or satellite optics (the camera,
in effect). It is replaceable or swappable, and regarded as a separate piece
of engineering from the telescope. The days when observers would travel to the
telescope carrying their own instrument are now largely past. operate partly
or exclusively according to a model in which astronomers are awarded
‘telescope time’, ranging from a few hours to a few nights, as the results of
competitive bids closely analogous to grant bids. The resulting data generally
has a proprietary period, extending for perhaps 12, 18 or 24 months after the
data is taken, during which only the observer who requested it can retrieve
it, but after which it automatically becomes retrievable by anyone (‘embargo’
would be a better term, though unconventional). Similarly, instruments built
by consortia generally have proprietary periods during which the data is only
available to consortium members. The proprietary periods are partly for the
benefit of the consortium individuals – it is their reward for the initiative
and possibly decadal effort of building the instrument – but they are also a
pragmatic reflection of the length of time it may take to calibrate and
validate acquired data, ready for deposit in an open archive. As a result, the
lengths and terms of proprietary periods are the subject of negotiations
between the instrument builders and their ultimate funders, though the
negotiations are always about the length of the delay before a general data
release, and never question the necessity for the release itself.

NASA missions now typically have 12-month proprietary periods, but this has
varied historically, and for example the 1990 COBE mission, which included
significant technological novelty, and whose performance was therefore rather
unpredictable, had a 36-month proprietary period.

Not all instruments have formal release plans, and the proprietary periods
that exist may be adjusted informally. Caltech is one of the few private
institutions which is rich enough to own, or have a significant share in,
world-class telescopes (Palomar and Keck). It has no declared policy on data
management or data sharing, beyond a broad tacit expectation that data will be
published as appropriate for normal scientific practice. As a second example,
during the ‘science demonstration phase’ of the commissioning of the Herschel
telescope (that is, the last commissioning phase, verifying that the science
goals were achievable), the instrument team invited††margin:  See ‘Herschel
Observers’ Manual’ §1.1.4,
http://herschel.esac.esa.int/Docs/Herschel/html/ch01.html; thanks to Haley
Gomez for bringing this to our attention. ††margin:  See ‘Herschel Observers’
Manual’ §1.1.4, http://herschel.esac.esa.int/Docs/Herschel/html/ch01.html;
thanks to Haley Gomez for bringing this to our attention. observers to
nominate part of their scheduled observations to be performed early, during
this still-experimental commissioning phase. When the observations proved
successful (as they generally were), the observing teams were given the choice
either of making the data immediately public, in time for the opening of the
Herschel archive and a journal special issue, and having the observation time
re-credited to them; or else retaining the 12 month proprietary period without
the re-credit.

Image data is the archetypal astronomy data, and is generally stored as files,
but another important category is the astronomical ‘catalogue’, of object
positions, spectra and other properties, usually stored in relational
databases. Astronomical archives range from quite small ones (at one extreme,
a small specialised instrument may have its ‘archive’ consisting of a file
server looked after by a graduate student) to very large professionally
managed archives which are both the primary sources of some data sets, and
mirrors of others.

Astronomy data is potentially very long-lived. Although astronomers are
naturally drawn to the newest instruments with the greatest sensitivity, it is
not unusual to draw on relatively old archive data. In most cases, this will
be still be born-digital data, but digitised versions of century-old
astronomical plates are used in precise astrometry, and to identify the
precursors of supernovae and other one-off events (see for example the
Edinburgh SSA††margin:  http://surveys.roe.ac.uk/ssa/ ††margin:
http://surveys.roe.ac.uk/ssa/ , further discussed, with background, at [14];
and a more discursive account of plate scanning, including discussion of some
of the archival challenges, in [15, 16]). Even babylonian and ancient chinese
astronomical data has been used for contemporary science, helping measure the
rate at which the earth’s spin rate, and thus the length of day, is changing
[17]; similarly, 3 000-year-old egyptian data has been used to measure the
change in the orbital behaviour of the three stars in the Algol system [18].
The cosmos changes slowly on our timescales, so that the great majority of
astronomical observations are repeatable; the exceptions are those cases where
long time-bases are necessary (precise astrometry) or where the object of
study is a one-off, and therefore unrepeatable, event such as a recent or
historical supernova.

Astronomy data is also intelligible in the long term: although untranscribed
babylonian tablets can only be read by specialists, contemporary astronomers
can basically understand the data published in Kepler’s 1627 _Rudolphine
Tables_ , and with some assistance can understand the content of the 11th- to
12th-century Toledan Tables [19]. Although biologists might be able to make
similar claims with respect to, for example, Linnæus’s observations, it is
hard to find equally long-lived data in the physical sciences, or born-
analogue physics data where there is a similar contemporary pressure for
digitisation.

There is essentially no file-format problem in (electromagnetic) astronomy,
since the Flexible Image Transport System (FITS) format is universal [20, 21].
Though not perfect, this is a relatively simple and well-defined format,
combining binary or table data with keyword-value metadata.

Astronomical data also has a well-developed notion of _data products_. These
are datasets which contain, not raw data, but data which has been processed to
a greater or lesser extent. We can distinguish at least three levels of data
in this context; most large instruments will have more than one level of
derived products.

Raw data

This is the lowest-level data, consisting of the direct output of a detector
or other instrument, or the raw satellite telemetry. This data is made
meaningful only by processing with software which is to some extent specific
to the equipment in question. Though it will be preserved as a matter of
course, it is rarely published, nor used by, nor useful to, other researchers,
except in unusual circumstances. In the case of a particularly subtle effect –
or less commonly, a debate over a theoretical analysis or calibration – a
researcher might return to the raw data, but this will generally be done with
the collaboration of the instrument scientists, and may be otherwise
infeasible, to the extent that any results obtained without such insider
knowledge might not be believable by the broader community.

Data products

After it is gathered, (raw) data must be processed (‘reduced’) to turn it into
scientifically meaningful numbers (interpreting engineering or telemetry data
streams, and calibration) and to remove various instrumental and observational
artefacts. Data products are usually made available in standard formats (in
astronomy, generally FITS files), whereas raw data, if it is made available at
all, may well be in an instrument-specific form.

Publications

Sitting above the data products is a class of high-level outputs, including
scientific papers, and other peer-reviewed outputs such as published catalogue
s. Journal articles are curated at publisher sites and the Astrophysical Data
Service (ADS), and article preprints at the arXiv (cf Sect. 1.9). Modest
volumes of data can be published as digital appendices to journal articles in,
for example, Astronomy and Astrophysics Supplements; these are curated at the
journal and at VizieR.

It is the data products which are the outputs which are sufficiently free from
observational artefacts to be the starting point for scientific analysis
(high-level products are sometimes referred to, informally, as ‘science
data’), and which represent the class of data which is naturally archived,
most carefully documented, and which will eventually be made public. There may
be multiple levels of data products, with lower-level products carrying more
information, but using which requires more detailed knowledge of the
subtleties of the instrument and its processing pipeline. To a much greater
extent than is true for HEP data, for example, the highest level astronomy
data products are both useful and generally intelligible – everyone is, after
all, looking at the same sky – but researchers will often use intermediate-
level products, if they can invest the time to learn about them, or have
collaborators who have experience with them. Those researchers who are more
intimately involved with an instrument will be comfortable using lower-level
data products, because they will have the knowledge which enables them to run,
or experimentally re-run, the pipelines in a scientifically meaningful way.
That said, in OAIS terms, astronomical data can be characterised as having a
broad Designated Community and well understood Representation Information.

Publications are in the province of libraries and similar repositories, and
are not considered further in this report.

Optical astronomy (that is, with observations made using visible light) has
the most straightforward data, so that the distinction between raw data and
data products is slight to the point of being rather artificial: astronomers
reusing optical data would expect to recalibrate the raw or nearly raw data,
and would not anticipate having difficulty doing so.

We conclude with some examples: A typical telescope archive is the UKIRT
archive at http://archive.ast.cam.ac.uk/ukirt_arch/; there are several image
and spectrum archives at the Royal Observatory Edinburgh’s Wide Field Archive
Unit††margin:  http://www.roe.ac.uk/ifa/wfau/ ††margin:
http://www.roe.ac.uk/ifa/wfau/ ; and there is a large collection of catalogue
s available at Strasbourg Data Centre (CDS) (see Sect. 1.4.1 below).

The ESA Hipparcos astrometry mission††margin:
http://www.esa.int/science/hipparcos ††margin:
http://www.esa.int/science/hipparcos flew between 1989 and 1993, and produced
a high-precision catalogue of 100 000 stars [22]. The catalogue is available
online as queriable databases at ESA††margin:
http://www.rssd.esa.int/index.php?project=HIPPARCOS ††margin:
http://www.rssd.esa.int/index.php?project=HIPPARCOS and CDS, as CDs, and as
PDFs which match the catalogue’s 17-volume printed version. The printed
version is an interesting case: as discussed in the catalogue (vol. 1,
§2.11.3), the printed pages are designed with a per-page checksum, to help
with re-scanning the catalogue from paper, in the presumed-likely future case
that the digital version becomes unreadable and only copies of the paper book
survive. The Tycho catalogue, from the same mission, comprises around 20 times
the number of stars, at lower precision, and is only available online.

There is some discussion of preservation costs in Sect. 3.4.

####  1.4.1 Strasbourg Data Centre (CDS) as a disciplinary repository

CDS is a large disciplinary repository for astronomy [23]. It stores a broad
range of catalogues, of various sizes, in its VizieR service (see [24] and
http://vizier.u-strasbg.fr/) and provides a large librarian-curated collection
of data from, measurements of, and references to, individual astronomical
objects. It cooperates closely with ADS.

CDS was created, and is supported, by the french agency in charge of ground-
based astronomy – first CNRS/INAG then CNRS/INSU – as a joint venture with
Strasbourg University. The main support is through permanent positions from
the CNRS/INSU and the University (researchers, computer engineers, and
specialised librarians), with additional contracts supported by funding from
various sources.

CDS is administratively located within a research structure, Strasbourg
Observatory, providing an active research environment for CDS astronomers. The
preservation aspects have never been separated from the provision of services
and the maintenance of local expertise on data management and
preservation.††margin:  We are most grateful to Françoise Genova, of CDS, for
this discussion of CDS’s history and support. ††margin:  We are most grateful
to Françoise Genova, of CDS, for this discussion of CDS’s history and support.

This can be seen as an example of a very successful disciplinary repository.
There appear to be several key features of this success.

  * •

CDS has established, and actively maintains, international leadership in the
curation of astronomical data, by virtue of collaborating widely and investing
effort in projects (such as the International Virtual Observatory Alliance
(IVOA)) which support and promote data sharing.

  * •

As a result of the intimate relationship between the repository, the
observatory and the university (to the extent that the boundaries between the
three can seem rather vague to outsiders), CDS personnel have practical
knowledge of how their data is used, and what researchers need.

  * •

The core funding for CDS comes from the french state, but it is conceived as
an internationally visible project.

####  1.4.2 Collaborations in astronomy

The most visible collaborations in astronomy are large terrestrial and
satellite-borne telescopes and other instruments. At the risk of
oversimplifying, these are generally not the many-person collaborations usual
in GW or HEP physics, but are instead facilities created by space agencies or
consortia of national funders. Although they are highly innovative leading-
edge facilities, they are not seen as _experiments_ in the same way as LIGO or
the LHC are (massive) items of specialised hardware built to answer a
delimited set of scientific questions. They are instead _observatories_ : the
data management in these facilities is part of their general operating
infrastructure, and the research and research data they produce is ‘owned’ (at
least in an academic rather than a legal sense) by the scientist _users_ of
the facilities, rather than the facility itself.

Astronomy does however have a variety of data-analysis collaborations. These
are semi-formal collaborations concerned, mostly, with multi-wavelength
studies of multiple archives, and include for example UKIDSS-UDS, the Herschel
Atlas collaboration, HerMES and GAMA††margin:
http://www.nottingham.ac.uk/astronomy/UDS/, http://www.h-atlas.org/,
http://hermes.sussex.ac.uk/, http://www.gama-survey.org/ ††margin:
http://www.nottingham.ac.uk/astronomy/UDS/, http://www.h-atlas.org/,
http://hermes.sussex.ac.uk/, http://www.gama-survey.org/ . These have between
20 and 60 collaborating members scattered over perhaps a dozen institutions
but, crucially, no ‘corporate’ existence, and little or no direct funding.
Instead, they are funded indirectly via individual fellowships or rolling
grants: participation in the collaboration might be a strong feature of a
grant application, but it is not the collaboration as such that receives the
direct support. They do have governance structures, but these tend not to be
particularly formal, because they remain small enough that there is little
perceived need. These collaborations exist to derive high-value derived data
products from the lower-level data products of the archives they are analysing
(for example Herschel is an ESA observatory mission: this means that
individuals can bid for observations, but that ESA does not have it as part of
its remit to provide more than minimally reduced science data).

The collaborations distribute their results in papers, and associated
datasets; they typically build archives to support and distribute their work,
but there’s no expectation (beyond the usual cooperative academic norms) that
they will help others work on the data, or release it. It is hard to see how
there could be such an expectation, much less an obligation, since they
receive little direct funding, and their indirect funding comes from a
multinational set of entities with potentially very different Data Management
and Preservation (DMP) policies.

###  1.5 High Energy Physics data

Astronomy is essentially an observational science: telescopes, their optics,
and the detectors which hang off them, are constructed to create a path from
nature to data which is as nearly as possible unmediated. This means that it
is both reasonably obvious what things are to be archived, and that the nature
and processing of observational artefacts are well and commonly understood.
This means that astronomy, unusual in the physical sciences for needing to
preserve data long-term, is in the happy position of having its data readily
preservable.

HEP data is different. HEP is a participative science, where objects ranging
in size from electrons all the way up to nuclei are disassembled, and data
about the messy results of this disassembly is examined to retrieve
information about the interior structure of the original. This reconstruction
from collision data depends on a shifting engineering understanding of rivers
of data, out of instruments which are one-off works of art, designed and
assembled by a thousand-strong community, close-packed into a detector the
size of a small cathedral, attached to a machine with its own postcode.

The result of this is that HEP data analysis is rather tricky, with many steps
between data and science, each of which depends on software which encodes a
detailed understanding of the data’s provenance. In consequence, although HEP
data is typically distributed with multiple levels of reduction, almost none
of these levels (with the exception of formal publications) are
straightforwardly suitable for long-term preservation. This is because
interpretation of this data is heavily dependent on software, the use of which
requires detailed experimental knowledge which it may be infeasible to
preserve. In OAIS terms, the designated community is tiny because the
Representation Information is hugely complex.

In addition to this, HEP data has a considerably shorter shelf-life than
astronomy data, as discussed above. In contrast, old HEP data is typically
made redundant by new data, obtained from more powerful accelerators. Also in
contrast to astronomy data, HEP data is not expected to be generally
intelligible for very long: two- or three-decade old data might potentially be
useful or intelligible, but much beyond that would count as archaeology. At
the risk of being whimsical, we can compare the roughly millennial lifespan of
astronomical data with the roughly three-decade lifespan of HEP data, and
conclude that the latter goes ‘off’ about 30 times faster than the former.
Although facilities make very considerable efforts to manage data safely while
an experiment is running, there is little real pressure to preserve HEP data
into the long term.

Of course, things are not quite as straightforward as that in fact. (i) The
LHC gains interaction energy at the expense of a messier collision, so there
are potentially some features that will be detectable in one dataset (for
example the HERA p-e data) which would not be findable in the LHC. While
interaction energy is the most prominent metric of an accelerator’s
performance, it is not the only one, so that larger accelerators will not
render smaller ones obsolete as inevitably as we may have suggested above.
Similarly to this, (ii) data reduction errors may be dominated by theoretical
uncertainties rather than experimental ones, and these will only be improved,
and the data re-reduced, after the experiment is over. Finally (iii) there are
no accelerators bigger than the LHC currently scheduled, so that this dataset
may remain the highest-energy one for a relatively long time. The archaeology
is illustrated in [25] and the problem further explored in [26], which also
discusses the HEP community’s developing plans for data preservation.
Qualifications notwithstanding, the overall timescales in HEP are shorter than
in astronomy, and the solutions described in [26] are concerned with
prolonging a continuous low-level relationship with a dataset rather than
being able to return to a dataset cold.

Unlike astronomy, HEP has for the last few decades been organised into larger
and larger collaborations, and these collaborations have developed intricate,
and socially fascinating, cultures for managing this. The two larger
instruments at the LHC, ATLAS and Compact Muon Solenoid (CMS), each have
author lists of order 3 000 people, so that the various CERN collaborations
account for around 10 000 research-active individuals. There is extensive
discussion of the history and structure of the LHC collaborations in [27] and
in the outputs of the pegasus project,††margin:
http://www.pegasus.lse.ac.uk/research.htm and in particular [28] ††margin:
http://www.pegasus.lse.ac.uk/research.htm and in particular [28] but many of
the collaborations’ relevant organisational features are echoed in the GW
community: this is discussed in Sect. 1.6.1 and we do not discuss them here.

###  1.6 Gravitational wave physics

The gravitational wave community has astronomical goals, but in the scale of
the LIGO project, and in the amount of novel technology involved, as well as
in the fact that many of the personnel involved came originally from a HEP
background, the project’s culture more closely resembles that of a HEP
experiment than of an astronomical telescope. We discuss some specific
features of LIGO data in [29]; here we discuss where GW data, and the
discipline’s organisation structure, fits on the spectrum between astronomical
and HEP data.

####  1.6.1 Gravitational wave consortia

There are three principal sources of recent GW data available to UK
researchers: LIGO, GEO600 and Virgo. There are other detectors which are
either smaller efforts (in terms of consortium sizes), which have stopped
taking data (TAMA-300), or which are still at the planning stage. See [30] for
an overview of current detectors, and of detector physics.

LIGO Lab is a collaboration between Caltech and MIT, which designs and runs
three interferometers in Hanford, WA, and Livingston, LA, in the US. GEO is a
German/British collaboration, which runs the GEO600 interferometer. The three
LIGO interferometers were shut down in October 2010 to refit for Advanced LIGO
(aLIGO); the GEO600 interferometer is still currently running. The LSC is the
result of a network of Memoranda of Understanding between LIGO Lab (or more
loosely the LSC) and multiple other institutions of various size.††margin:
The MOU which created the LVC is at [31], but MOUs are not routinely made
public. ††margin:  The MOU which created the LVC is at [31], but MOUs are not
routinely made public. These relationships involve hardware, resources, and
data access of various types. Most typically, the resources in question are
personnel, and an institution such as a university physics department, which
wishes access to LIGO data, will contribute in return fractions of staff from
permanent staff, through post-docs, to PhD students, for a broad spectrum of
activities including data analysis, instrument fabrication and shift-work in
the detector control room. However in some cases, the MOUs are concerned with
data swaps, and set up limited data releases with other scientists: for
example, there are a few MOUs between the LSC, Virgo and other observatories,
which describe what data is to be shared, in what volumes, and the outline
authorship arrangements for any subsequent papers. GEO’s MOU describes a
particularly close relationship with LIGO Lab, but most of the MOUs are
broadly similar to each other, and the process of creating one is by now
streamlined. In total (as of June 2010),††margin:  The definition of LSC
membership is included in [32] and the construction of the author list in
[33]. ††margin:  The definition of LSC membership is included in [32] and the
construction of the author list in [33]. the LSC consists of a little over
1300 ‘members’; of these, 615 spend more than 50% of their time dedicated to
the project and so have a place on the LSC author list.

The term ‘LIGO’ has a number of not quite equivalent meanings: sometimes it
refers to LIGO Lab, sometimes to LIGO Lab plus the LSC, and the phrase ‘LIGO
detectors’ is generally understood to refer to the LIGO Lab and GEO detectors.

The Italian/French Virgo consortium has its own detector and analysis
pipeline, and has a data-sharing agreement with the LSC, represented by the
LVC.††margin:  The term ‘LVC’ is not an initialism. It colloquially refers to
the data-sharing agreement [31] and joint meetings between the LSC and the
Virgo Collaboration. Though there are ‘LSC/Virgo collaboration groups’, there
is no formal big-C Collaboration. ††margin:  The term ‘LVC’ is not an
initialism. It colloquially refers to the data-sharing agreement [31] and
joint meetings between the LSC and the Virgo Collaboration. Though there are
‘LSC/Virgo collaboration groups’, there is no formal big-C Collaboration. As
with LIGO, the Virgo detector will shut down between 2011 and roughly 2015.
Virgo has 246 members (with a slightly different definition from the LSC), and
GEO600 around 100.

There is an attempt to summarise these relationships in Fig. 1.

Figure 1: The relationships between various GW consortia.

These experiments have a common purpose: they exist to detect signatures of
gravitational waves, which are confidently predicted by the General Theory of
Relativity, but the actual observation of which would be a major scientific
event (there exists an LSC data processing flowchart which includes the not
entirely serious branch ‘‘Call Stockholm!’’).

Gravitational waves are sufficiently weak, however, that the existing
equipment will not become sensitive enough to have a good chance of detecting
them until after its refit, which began in late 2010 (when the project entered
the phase known as aLIGO), and which is scheduled to be completed when the new
detectors are commissioned in 2015.

####  1.6.2 GW data

Although the consortia have (as expected) announced no detection so far, they
nonetheless produce a large volume of auxiliary data, representing background
and calibration signals of various types, and this, together with the core
data, means that the LSC collectively produces data at a rate of approximately
one $\mathrm{PB}\text{\,}{\mathrm{yr}}^{-1}$.

We can readily identify the levels of data which were discussed in Sect. 1.4:

Raw data

The lowest-level GW data consists of the signals from the core detectors. This
data is made meaningful only by processing with software which is completely
specific to the detectors in question. This is stored in ‘frame format’, which
is a very simple format intelligible to all the primary data analysis software
in the community, and which is multiply replicated across North America,
Europe and Australia. Although the disk format is common, the semantic content
of the raw data is specific to detectors and software, so that preserving it
long-term would represent a significant curation challenge.

Data products

The raw data is processed into calibrated ‘strain data’, which is the data
channel in which a GW signal will eventually be found (this is possibly, but
not necessarily, also held in frame format). This is the class of data
products which will eventually be made public. Unusually, it turns out that GW
raw data is in a semi-standard format, and the data products are specific to
the analysis pipeline which produced them.

Publications

Sitting above the data products is a class of high-level data products,
scientific papers, and other peer-reviewed outputs. The GW projects have
announced no detections of gravitational waves, but have nonetheless produced
a broad range of astrophysically significant negative results [30, §6.2].

As with the general astronomy data products discussed in Sect. 1.4, the
distinction between the ‘raw data’ and the ‘data products’ is that the latter
datasets, alongside their supporting documentation, will be available for use
and reuse by scientists who do not have an intimate connection with, and
knowledge of, the instrument.

Both the ‘data product’ and ‘publication’ groups are broad classes of objects.
The practical boundary between them is clear, however: what we are calling
‘publications’ are entities such as journal articles or derived catalogues
whose long-term curation is not the responsibility of the LSC data archive,
though they may be held in some separate LSC paper archive, which is as such
out of scope for this project.

####  1.6.3 Gravitational wave data releases

Because the LSC has not announced the detection of any signal so far, and
because the data will remain proprietary to the consortium until well after
such an announcement, there are no distributed data products so far, and so
the issues surrounding formats and documentation have not yet been addressed.
However it is the eventual public data products which are the highest-value
outputs from the experiment, and which are the products which it will be most
important to archive indefinitely.

At present, LIGO data is available only to members of the LSC. This is an open
collaboration, and research groups which join the LSC have access to all of
the LIGO data††margin:  http://www.ligo.org/about/join.php ††margin:
http://www.ligo.org/about/join.php . In return, they contribute personnel to
the project (including for example people to do shift-work manning the
detectors), and accept the collaboration’s publication policies, which require
that all publications based on LIGO data are reviewed by the entire
collaboration, and carry the complete 800-person author list. At present, and
in the future, data which is referred to by an LSC publication is made
publicly available. See Sect. 3.3.2 for further details on LIGO’s DMP plan.

####  1.6.4 Summary: big-science preservation challenges

In the three sections above, we have tried to describe both differences and
commonalities between three large-scale scientific disciplines. Possibly the
biggest difference between the three areas is that high-level astronomical
data products are much more generally intelligible than even the highest-level
HEP products. In each case, however, we have a ladder of reasonably well-
defined data products, with each rung generated from the lower ones by
sophisticated data reduction pipelines.

The situation is not as rosy, from the point of view of long-term
preservation, as this account may suggest. Because the pipelines have
developed organically over a number of years, under the influence of
experience with earlier versions and increased understanding of the
instrument, the knowledge they represent is sometimes encoded within them in a
less structured way than would be desirable. Sometimes, metadata is encoded in
filenames, or in configuration files, or wikis, or even private emails. Of
course, one could simply argue that this information should be documented
better, but it would be hard to argue that the costs of this work would be
justifiable, to service a future theoretical need that few believe would even
become an actual one. In consequence, although the resulting data product will
be regarded as perfectly reliable, it may be infeasible to redo the analysis
other than by preserving and rerunning the pipeline software (even if it were
feasible, it would be prohibitively expensive, and rarely seen as valuable;
see also Sect. 2.4). For this reason, software preservation has some role in
the overall data preservation strategy. However it is not clear to us what
this role should be, and the thorny issue of software preservation is
addressed at greater length in Sect. 3.2.

###  1.7 A contrast: social science data

It is possibly instructive to contrast the data management practices discussed
here, with the very different problems faced by data managers in the social
sciences. In [34], the authors survey a number of social science projects,
with a particular focus on two large (for the social sciences) programmes
funded by the Economic and Social Research Council (ESRC) (the UK social
science research council) with substantial responsibilities for data
preservation and sharing.††margin:  This work was part of the ‘Data Management
Planning for ESRC Research Data-Rich Investments’ project (DMP-ESRC)
(http://www.data-archive.ac.uk/create-manage/projects/JISC-DMP), funded by
JISC, like the present project, as part of the Managing Research Data
programme. ††margin:  This work was part of the ‘Data Management Planning for
ESRC Research Data-Rich Investments’ project (DMP-ESRC) (http://www.data-
archive.ac.uk/create-manage/projects/JISC-DMP), funded by JISC, like the
present project, as part of the Managing Research Data programme.

For the ESRC projects, the artefacts being stored are simple things, at the
level of Content Information: they are conventional Word documents and audio
files, rather than the heavily structured and still somewhat experimental big
science data objects. The ESRC archive contents will remain broadly
intelligible to future researchers, without much archive-specific effort to
define Representation Information or a Designated Community. In contrast to
this simplicity, however, the ESRC archives have to cope with a broad range of
associated contextualising metadata, which is different for different
projects, and inconsistently or incompletely specified by the originating
researchers, perhaps as an afterthought. This makes archive ingest a
complicated problem, in contrast to the big science cases, where archive
ingest fundamentally involves little more than copying a self-contained set of
artefacts from working storage to some preservation store. In particular, the
ESRC projects have a complicated set of anxieties about copyright, IPR,
confidentiality, anonymization and consent; while LIGO cares intricately about
data access and security, it does so in the rather formal context of
professional ethics rather than family secrets.

This illustrates two further notable differences between physical science data
and that of social science or broader archival resources.

Firstly, the responsibility for ESRC data in practice lies with more junior
researchers, helped by part-funded archivists [34, §§5.2.1 & 5.4]. For big
science projects, it is funders and senior collaboration members who drive the
preservation efforts.

Secondly, essentially all physics data is born digital and complete, meaning
that all of the information to be archived is present at the time of deposit.
Of course, this is not complete from the point of view of reproducibility
(that requires journal articles and personal knowledge) and does not discount
the subsequent addition of subjective metadata as finding aids, but it is
completely specified from the point of view of conventional future analysis.
The distinction is that experimental data is a complete and objective account
of everything that was believed to be relevant in recording a physical event
which happened at a specific time. One can disagree with the experimenters’
beliefs about completeness (this shades into questions of reproducibility and
tacit knowledge), complain that some details might be recorded in notebooks
rather than digital records (more true of lab-scale than facility-scale
experiments), or in extreme cases argue about the nature of objectivity, but a
natural science experiment has a much clearer boundary, in space, time and
documentary extent, and so a more natural expectation of documentary
completeness, than will be usual for an experiment in the social or human
sciences. This is different from the traditional archive problem, where the
problems of interpretation are more visible and acknowledged, and the problem
of incompleteness more evident.††margin:  For a vivid and illuminating
discussion of the complications and physicality of reproducing experiments,
see [35] and references therein (by coincidence, this describes observations
amongst gravitational wave experimenters in Glasgow); that discussion is
reprised in a larger context in [6, ch.35]. The question of tacit knowledge is
discussed at length in [36]. For a discussion of different types of reuse, see
[37, §3]. ††margin:  For a vivid and illuminating discussion of the
complications and physicality of reproducing experiments, see [35] and
references therein (by coincidence, this describes observations amongst
gravitational wave experimenters in Glasgow); that discussion is reprised in a
larger context in [6, ch.35]. The question of tacit knowledge is discussed at
length in [36]. For a discussion of different types of reuse, see [37, §3].

The summary is not that the ESRC or the big science archives have an easier
job overall, but that the complications express themselves in different parts
of the mapping from OAIS abstractions to local fact. Big science archives must
preserve large complicated objects for a hard-to-describe Designated
Community, but because they are essentially always project-specific archives,
their implementation does not have to be generic, and many of the ingestion
issues can be baked into the original archive design.

###  1.8 Babylonian data management (less contrast than you’d think)

Contemporary astronomy began, in the west, in Mesopotamia in the fifth and
fourth centuries bce. Although earlier datasets exist – the _Venus tablet of
Ammisaduqa_ is a cluster of 7th C bce copies of 17th C or 16th C data
recording the rise times of Venus over a 21 year period – these earlier omen
texts seem to have been preserved for largely cultural reasons.††margin:  See
[38] for background and further references, and [39, ch.4] for very detailed
discussion of the physical tablets. The precise date of the observations is of
considerable scholarly interest, since an agreed date would provide an
absolute fix for the otherwise relative chronology of the Late Bronze Age Near
East. ††margin:  See [38] for background and further references, and [39,
ch.4] for very detailed discussion of the physical tablets. The precise date
of the observations is of considerable scholarly interest, since an agreed
date would provide an absolute fix for the otherwise relative chronology of
the Late Bronze Age Near East.

Figure 2: Calculated ephemeris for the period 104 bce March 23 to 101 bce
April 18, written on Seleucid year 209, month IX, day 18 (103 bce December
20?). Comparison with a JPL ephemeris shows that the text conjunction times
remain within a couple of hours of the correct values, with an offset
attributable to an error in the initial value. For detailed discussion, see
[40]. British Museum item Sp-II.52, ©Trustees of the British Museum.

Distinct from these, there is a large set of 4–500 other texts, ranging from
4th C bce to 75 ce with a smattering going back as far as mid-8th C bce, and
spanning the development of Babylonian theoretical astronomy during the 4th C
bce. These are a mixture of observations, calculated ephemerides (such as Fig.
2), and telegraphically obscure technical documentation. The observation texts
– ‘astronomical diaries’, forming the majority of the texts – describe in
sequence celestial and meterological observations, daily commodity prices,
river levels, and topical events. The observations of the Sun, Moon and
planets were of good enough quality, and preserved over a long enough time,
that when babylonian mathematical models were fitted to them they produced
values for the synodic and anomalistic months and (implicitly) the orbital
periods of the planets, which are very respectably close to their currently-
determined values (out by a factor of $3\times 10^{-7}$, in the case of the
synodic month). These were used to predict the first and last appearances of
planets, and the times of lunar (but not solar) eclipses.

The information in these texts is sometimes available on multiple tablets,
although it is not clear whether these duplicates were backups, mirrors, or
media refreshes. Many tablets have acquisition metadata, added in ink by the
archives, millennia apart, in Babylon and Bloomsbury.

It is clear that the tablets that have survived represent only a small
fraction of the total. but both the data, and the mathematical technology that
reduced the data and generated the ephemerides, were available and fully
intelligible to Hipparchus (_c_ 150 bce) and, either via him or directly, to
Ptolemy (_c_ 150 ce). The Babylon Data Centre was still active in the first
century ce, though funding cuts meant new acquisitions were by then minimal,
and it was operating in the collapsing ruins of the desert city.

The Content Information in the texts is sufficiently well preserved that if
the texts can be dated at all (in some cases through contemporary ingest
metadata), they can generally be dated to the very day; the technical
Representation Information, in contrast, is so terse as to make sense only
after the procedure being documented is reconstructed from the Content. The
cuneiform presents a challenge, but once this has been transliterated, the
datasets are fundamentally intelligible to current astronomers. The
preservation strategy is a daring one: by effectively founding western
astronomy, and arranging that the data was preserved just long enough that it
could be taken over by the (hellenic) successor civilisation,††margin:  This
can be classed as a ‘high-risk’ data preservation strategy, and is not
included amongst this report’s Recommendations to STFC. ††margin:  This can be
classed as a ‘high-risk’ data preservation strategy, and is not included
amongst this report’s Recommendations to STFC. the babylonians ensured that
their coordinate system (based on the zodiac) and number system (with angles
in degrees, subdivided into base-60 fractions) would still be in use by
astronomers 25 centuries later.

###  1.9 Bibliographic repositories

Though it is not strictly data, it seems useful to make parenthetical mention
of the big science communities’ literature repositories, since they seem to
illustrate the way in which the communities have learned to act collectively.

The preprint archive at arXiv.org started in 1991 as an electronic version of
the long-established practice of distributing preprints of accepted journal
articles around the high-energy physics community, by post. It currently
receives around 6000 submissions per month, predominantly in HEP, astronomy,
condensed matter physics and mathematics; it probably receives copies of
nearly 100% of the HEP community’s output.††margin:
http://arxiv.org/Stats/hcamonthly.html ††margin:
http://arxiv.org/Stats/hcamonthly.html Authors most typically submit papers at
the point when they have been accepted by the journal, but some submit earlier
versions, and a few are not further published at all. Although the journals
are still providing an _imprimatur_ , many papers are now principally read as
preprints, and many journals permit citations by arXiv reference. ArXiv is
supported by requesting contributions from its heaviest institutional users,
on a sliding scale rising to $4 000/year.††margin:
http://arxiv.org/help/support/whitepaper ††margin:
http://arxiv.org/help/support/whitepaper JISC Collections is one of these
‘tier 1’ supporters, on behalf of UK colleges and universities.

The NASA ADS at the Smithsonian Astrophysical Observatory preserves
bibliographic information for the astronomy literature, holds references to or
copies of journal article full texts, and curates digitised copies of older
articles sometimes unavailable from publishers. It also curates links between
these publications and the arXiv, and between publications and data. See [41]
for context, and some discussion of the arXiv numbers mentioned above.

The publication paradigm represented by arXiv (and similar smaller-scale
efforts) is underpinned by the peer review processes of journals. However as
journal subscription costs rise, journals are progressively cancelled, in a
process which may ultimately damage the reviewing process on which the
paradigm depends. The SCOAP3 consortium††margin:  http://scoap3.org/about.html
††margin:  http://scoap3.org/about.html aims to break out of this cycle by
directly supporting a small number of HEP journals, through a levy on the
funding agencies which support the field, in proportion to the share of HEP
publishing they support. In return for this the journals will remove both
subscription charges and page charges for these journals.

###  1.10 Virtual Observatories

A Virtual Observatory is an astronomical data-sharing system, composed of a
network of archives and data-access protocols. The goal is that the data
appears to be integrated and ideally appears to be local.

The earliest VOs were Astrogrid in the UK, the US-VO in the US (which became
NVO and then VAO), and the Astrophysical Virtual Observatory in Europe (which
became Euro-VO). They, along with a growing collection of smaller national or
regional VOs, formed the IVOA in 2002.††margin:  http://www.astrogrid.org,
http://www.usvao.org/, and http://www.euro-vo.org; plus http://www.ivoa.net.
††margin:  http://www.astrogrid.org, http://www.usvao.org/, and
http://www.euro-vo.org; plus http://www.ivoa.net. The IVOA exists to broker
portable network protocols for sharing data, on the part of cooperating
archives, and accessing it, on the part of client applications. The IVOA
focuses primarily on ‘traditional’ astronomy, and so has poor coverage of
solar physics and more broadly geophysics (and certainly provides no access to
GW data).

From this has grown the more general notion of the ‘VxO’, which is ‘‘[a]
service that ensures that all resources from sub-field $x$ are known,
discoverable, and easily accessible. It looks to the user like a uniform data
provider, but it is virtual.’’††margin:  See further commentary in
http://lwsde.gsfc.nasa.gov/VxO_Report_Decadal_Survey_5_2011.pdf ††margin:  See
further commentary in
http://lwsde.gsfc.nasa.gov/VxO_Report_Decadal_Survey_5_2011.pdf Examples
include the Virtual Solar-Terrestrial Observatory [42], HELIO, and NASA’s
Heliophysics Data Environment.††margin:  http://www.helio-vo.eu and
http://lwsde.gsfc.nasa.gov/ ††margin:  http://www.helio-vo.eu and
http://lwsde.gsfc.nasa.gov/

###  1.11 Data products and proprietary periods: reifying data management and
release

A common feature of the various data styles above is the notion of the _data
product_ , and it seems useful to recap and stress the salient features of
this here. Data products A data product is a designed and documented output of
an instrument, intended to be both archivable and immediately useful to other
researchers, by virtue of having observational artefacts removed as much as
possible. Depending on the discipline and the engineering complexity of the
instrument, data products may be anything from the raw data to a highly
processed derivative of the raw data; the ideal data product contains all the
scientifically relevant information with none of the experimental artefacts.

Researchers are not restricted to using only data products, but it will only
rarely be necessary for them to resort to reanalysing raw data (see the
discussion on p.1.4).

Data products correspond closely to the ‘Information Packages’ of the OAIS
model (see Sect. 3.1.1). In our experience, there tends to be little practical
difference between Submission Information Packages (SIPs) and Archival
Information Packages (AIPs), and where there are distinct Dissemination
Information Packages (DIPs), they tend to be available in addition to the
available SIPs and AIPs. An exception to this is archives such as the Wide-
Field Astronomy Unit at Edinburgh,††margin:  http://www.roe.ac.uk/ifa/wfau/
††margin:  http://www.roe.ac.uk/ifa/wfau/ which specialises in astronomical
survey science, and develops enhanced archives (which is to say, value-added
AIPs) as part of its participation in collaborative astronomy projects.

When the various Packages differ, they tend to be regarded as successively
higher-level, as opposed to alternative, data products.

The notion of data products has a number of concrete advantages.

  * •

Most immediately, the existence of a stable and documented output makes it
easier for researchers to use and repurpose experimental and observational
results.

  * •

Because the products are so central to an instrument’s output, they, and the
pipelines that produce them, are designed and costed at early stages of an
instrument’s production.

  * •

Researchers can produce and share software which processes well-defined
products, possibly from more than one instrument.

  * •

Because they are so explicit, they form well-defined start and end points of
discussions about interoperability between instruments. Indeed, the VO
programme could be characterised as an extended effort to negotiate new common
products which archives and software developers agree can be successfully
generated (by archives) from existing AIPs.

There is of course a cost associated with the design and development of data
products, but we believe that this will in most cases be much smaller than the
costs associated with the retrospective documentation and distribution of _ad
hoc_ datasets.

Another notion that is well-known in the physical sciences, but which as far
as we are aware is rare outside, is that of explicit _proprietary periods_ for
data. Proprietary period A ‘proprietary period’ is a period after data is
acquired, and therefore archived, by a shared instrument, during which it is
private to the observer or observers who requested it, and after which the
data (usually automatically) becomes public. The term ‘embargo period’ would
possibly be more generally intelligible, but ‘proprietary’ is conventional.
The notion is discussed elsewhere in this document (see for example Sect.
1.4), but we stress it here because it usefully concretizes a number of
otherwise vague questions about data release.

Instead of rather broad questions of the how, when, why and whether of data
management and release, we instead have questions such as ‘what are the data
products?’, ‘whom are they documented for, and how expensively?’, ‘how long is
the proprietary period?’ or ‘what is the quid pro quo for this
period?’††margin:  Compare the comments about Herschel data in Sect. 1.4.
††margin:  Compare the comments about Herschel data in Sect. 1.4. These
questions don’t magically become easy to answer, but they become a lot easier
to ask, and invite concrete answers and negotiation rather than _ad hoc_
argument.

There is nothing in the notions of data products and proprietary periods which
is obviously specific to the physical sciences. The notions have become well-
established in this area probably because it has long experience, of
necessity, of using large shared instruments which are operated to a greater
or lesser extent as services. This is less often the case in disciplines with
more bench-scale experimental norms, but even some areas of biology are now
more often using shared facilities, and in other disciplines, data products
and proprietary periods would become more natural, the more that preservation-
aware storage is used [43].

We commend the notions of data products and proprietary periods, and the data
culture they engender, to the broader research community. Indeed, we recommend
that ††margin:  Recommendation 1 ††margin:  Recommendation 1 data managers
should consider adopting the language of data products and explicit
proprietary periods when designing and documenting their holdings.

##  2 The responsibilities for data preservation

###  2.1 Visualising benefits

Why do funders wish to preserve data? Because they perceive _benefits_ to that
preservation.

Building on this truism, it seems useful to explicitly articulate these
benefits. The JISC-funded project Keeping Research Data Safe (KRDS) (see
http://www.beagrie.com/krds.php and [44]) described a collection of studies
and tools supporting data preservation. Amongst the KRDS innovations was a
typology of _benefits_ , describing three dimensions: direct to indirect,
near- to long-term and public to private. In a slight extension to the work in
KRDS, we can take the notion of ‘dimensions’ perfectly literally, assign any
particular benefit to a position along each of the three axes, and plot the
result in a three-dimensional space; see Fig. 3.

Figure 3: Visualizing benefits

In this figure we identify four benefits which might be associated with a big-
science project – namely the existence of data-reduction software, good
metadata, the provision of open data and the existence of system
administrators – and we sketch the approximate volumes they might occupy along
the three axes (in blue). On the same diagram, we can indicate (in red) the
approximate areas of interest of four sample stakeholders.

In the example here, ‘sysadmin support’ can be seen as an indirect benefit to
researchers, typically private to an institution, but creating value in the
near- and long term; it is therefore spread along the ‘near-long term’ axis,
but at one extreme of the other two dimensions. We can put on the same diagram
the approximate areas of interest of various research stakeholders. For
simplicity, we are here conceiving of individual researchers as selfish and
short-termist, though the same researchers will have long-term interest when
they have a collaboration or institutional hat on, and indirect public
interests in the long-term health of their discipline when they are serving on
a funding council grants panel; below we will take the term ‘funders’ to refer
both to the officials of funding bodies, acting as proxies for the wider
interests of society, and to members of the research community discharging
service roles.

We should not take this diagram too literally – it is not clear that the axes
are independent, and the extent and even the gross positions of the various
interests and benefits are debatable. The diagram is nonetheless thought-
provoking. For example, it visually predicts that much of the research
community is not particularly interested in ‘open data’††margin:  (unless it’s
_other people’s_ open data, of course) ††margin:  (unless it’s _other
people’s_ open data, of course) and only incompletely interested in ‘good
metadata’ (in-collaboration researchers care when a dataset was acquired,
because they need that information to perform their analyses, but they have
little interest in dissemination and licensing metadata, for example, because
that is the long-term concern of funders and their proxies). We can therefore
naturally conceive of the funders taking the role of the conscience of a
discipline, worrying about long-term imponderables so that individual
researchers don’t have to. It follows from this, that the open data case made
to funders, for example, will be an institutionally self-interested one, but
that the case made to researchers must be qualitatively different, and be
either pragmatic (‘you must care because your funders care’) or high-minded
(‘your socio-cultural duty is…’). Neither of these is a poor argument, nor
indeed a cynical one, but we are acknowledging here that, to a busy and
distracted researcher, the self-interest argument in isolation may have little
purchase.

###  2.2 The case for open data

Internationally, there is a push towards such data sharing in the more general
context of scholarly research (see for example [45] or [46]). The most
explicit statement here is in the NSF’s GC-1 document [47], which in section
41 states that ‘‘[NSF] expects investigators to share with other researchers,
at no more than incremental cost and within a reasonable time, the data,
samples, physical collections and other supporting materials created or
gathered in the course of the work. It also encourages grantees to share
software and inventions or otherwise act to make the innovations they embody
widely useful and usable.’’ This is reiterated in almost the same words in
their 2010 data sharing policy [3]. They additionally require a brief
statement, attached to proposals, of how the proposal would conform to NSF’s
data-sharing policy.

STFC, in common with the other UK research councils, requires that ‘‘the full
text of any articles resulting from the grant that are published in journals
or conference proceedings […] must be deposited, at the earliest opportunity,
in an appropriate e-print repository’’††margin:
http://www.scitech.ac.uk/rgh/rghDisplay2.aspx?m=s&s=64 ††margin:
http://www.scitech.ac.uk/rgh/rghDisplay2.aspx?m=s&s=64 ; it has not yet made
any corresponding statement on data releases.

The year 2009 saw some excitement (relating to the incident inevitably
labelled ‘climategate’, and to some other data-release disputes††margin:
http://www.guardian.co.uk/environment/2010/apr/20/climate-sceptic-wins-data-
victory ††margin:  http://www.guardian.co.uk/environment/2010/apr/20/climate-
sceptic-wins-data-victory ) related to the management and release of climate
data. This illustrated the political and social significance of some science
data sets; the contrast between what scientists know, and the public believes,
to be normal scientific practice; and some of the issues involved in the
generation, ownership, use and publication of data.††margin:  UEA’s Climate
Research Unit is a partner in the ACRID project, also funded by the JISC MRD
programme: http://www.cru.uea.ac.uk/cru/projects/acrid/ ††margin:  UEA’s
Climate Research Unit is a partner in the ACRID project, also funded by the
JISC MRD programme: http://www.cru.uea.ac.uk/cru/projects/acrid/ The cases
during that year illustrate a number of complications involved in data
releases.

  1. 1.

Data is often passed from researchers or groups directly to others, across
borders, with no general permission to distribute it further.

  2. 2.

Data collection may be onerous, and the result of significant professional and
personal investments.

  3. 3.

Raw data is generally useless without the more or less significant processing
which cleans it of artefacts and makes it useful for further analysis.

  4. 4.

However not all disciplines have the clear notion of published data products
which is found in astronomy and which is implicit in the OAIS notion of
archival deposit.

  5. 5.

Science is a complicated social process.††margin:  The last point is
simultaneously obvious and deeply intricate. Unpacking it would distract us
here, but there is further discussion, in a very apposite historical context,
in [6], elaborated in [36]. ††margin:  The last point is simultaneously
obvious and deeply intricate. Unpacking it would distract us here, but there
is further discussion, in a very apposite historical context, in [6],
elaborated in [36].

In science, we preserve data so that we can make it available later. This is
on the grounds that scientific data should generally be universally available,
partly because it is usually publicly paid for, but also because the public
display of corroborating evidence has been part of science ever since the
modern notion of science began to emerge in the 17th century (ce) – witness
the Royal Society’s motto, ‘nullius in verba’, which the Society glosses as
‘take nobody’s word for it’. Of course, the practice is not quite as simple as
the principle, and a host of issues, ranging across the technical, political,
social and personal, complicate the social, evidential and moral arguments for
general data release.

The arguments _against_ general data releases are practical ones: data
releases are not free, and may have significant financial and effort costs (cf
Sect. 3.4). Many of these costs come from (preparation for) data preservation,
since it is formally archived data products that are the most naturally
releasable objects: releasing raw or low-level data _may_ be cheap, but may
also have little value, since raw underdocumented datasets are likely to be
useless; or more pessimistically they may have a negative value, if they end
up fostering misunderstandings which are time-consuming to counter (this point
obviously has particular relevance to politicised areas such as climate
science). In consequence of this, the ‘open data question’ overlaps with the
question of data preservation – if the various costs and sensitivities of data
preservation are satisfactorily handled, then a significant subset of the
practical problems with open data release will promptly disappear. We discuss
the data preservation question below, in Sect. 2.3.

It seems worth noting, in passing, that the physical sciences broadly perform
better here than other disciplines, both in the technical maturity of the
existing archives and in the community’s willingness to allocate the time and
money to see this done effectively.

What all this indicates is that there is a need for an explicit framework for
discussing the pragmatics of open data (cf point 4 above). We can go further
and suggest (it is almost a Recommendation) that the OAIS model’s notion of an
AIP, and its reflection in the notion of a _data product_ should be central to
this discussion.

###  2.3 The case for data preservation

The case for data preservation in astronomy was implicitly made in Sect. 1.4:
as an observational science, much astronomy data is repeatable, but there are
important cases where what is being observed is a slow secular change, or some
unpredictable (usually ultimately explosive) event; sometimes data can be
opportunistically reanalysed to extract information distinct from the
information the observation was designed for. Astronomical data is potentially
useful _and_ usable almost indefinitely. Thus there is a reasonable
expectation that the data can be and will be exploited by unknown astronomers,
far into the future.

HEP data is somewhat different (as noted in Sect. 1.5). As an experimental
science, it is generally very much in control of what it observes, and is able
to design experiments of considerable ingenuity, in order to make measurements
of exquisite discriminatory power. A consequence of this is firstly that HEP
experiments have a much stronger tendency to become obsolete with each
technological generation, and secondly that the complication of the apparatus
makes it hard to communicate into the future a level of understanding
sufficient to make plausible use of the data. Experimental apparatus will
generally be understood better and better as time goes on (this is also true
of satellite-borne detectors in astronomy), so that data gathered early in an
experiment will be periodically reanalysed with increased accuracy. However
this understanding is generally not preserved formally, but is pragmatically
communicated through wikis, workshops, word of mouth, configuration and
calibration files, and internal and external reports. Even if all of the
tangible records were magically preserved with complete fidelity, and
supposing that the more formal records do contain all the information required
to analyse the raw data, an archive would still be missing the word-of-mouth
information which a new postgrad student (for example) has to acquire before
they can understand the more complete documentation. We can think of this as a
‘bootstrap problem’. In OAIS terms, the Representation Network for HEP data is
particularly intricate, and while the Representation Information nearest to
the Data Object may be complete, it may be infeasible to gather the
Representation Information necessary to let a naive researcher make sense of
it. The Designated Community for HEP data may therefore be null in the long
term.

This sounds pessimistic, but [26] describes a number of scenarios in which HEP
data can and should be reanalysed some decades after an experiment has
finished, and describes ongoing work on the development of consensus models
for preserving data for long enough to enable such post-experiment
exploitation. This provides a strong case for a style of preservation somewhat
different from the astronomical one. What these models have in common is a
commitment of staff to actively conserve and continuously exploit the data.
This post-experiment staff can therefore be conceived as a form of walking
Representation Information so that, while they are still involved, the data
might have a Designated Community which corresponds to those individuals in a
position to undertake an extended apprenticeship in the data analysis (this
model is further discussed on p.3.4).

GW data is, as usual, somewhere between these two extremes. As astronomy, the
GW data consists of unrepeatable measurements which will potentially be of
value to astronomers well into the future; as a HEP-style experiment it makes
those measurements using two or three generations of highly sophisticated
apparatus, each generation of which will improve on the sensitivity of its
predecessors by orders of magnitude. An additional feature, however, is that
no-one has ever convincingly detected a gravitational wave, though there have
been repeated claims of detection in the past, so that the first claims by
LIGO or aLIGO will be scrutinized particularly closely.

Finally, and as noted in Sect. 2.2, if data is well archived, then most of the
pragmatic objections to opening that data do not apply. Thus, to the extent
that general data release is a good in itself, it is a further argument in
favour of a well supported archive.

###  2.4 Should raw data be preserved?

In the data-preservation world, there is often an automatic expectation that
‘everything should be preserved’, so that an experiment can be redone, results
reanalysed, or an analysis repeated, later. Is this actually true? Or if it is
at least desirable, how much effort should be expended to make it true? This
question is implicit in, for example, the discussion of software preservation
in Sect. 3.2.

When a physical experiment is set up and working, it is usual to avoid
tinkering with it as much as possible, to avoid any unexpectedly significant
change. That is, even with a small-scale lab-bench experiment, it is accepted
that not everything can be effectively documented, and that an experiment
might not be immediately replicable purely from published information (cf [6,
ch.35] and Sect. 1.7). This expectation (or rather, lack of expectation) is
also true of larger-scale experiments, which might be financially,
professionally or, at the largest scales, politically infeasible to
replicate.††margin:  It is because very large-scale experiments are impossible
to replicate, and even hard for an external reviewer of an article to
criticise meaningfully, that large collaborations submit their publications to
extremely scrupulous internal review. See
http://stuver.blogspot.com/2011/03/big-dog-in-envelope.html for a post-mortem
account of such a review. ††margin:  It is because very large-scale
experiments are impossible to replicate, and even hard for an external
reviewer of an article to criticise meaningfully, that large collaborations
submit their publications to extremely scrupulous internal review. See
http://stuver.blogspot.com/2011/03/big-dog-in-envelope.html for a post-mortem
account of such a review. Perhaps this attitude should extend to other aspects
of the experimental process.

In many cases, the pipeline for reducing raw data seems to fall into this
category: it encodes hard-to-document information, but is itself hard to
document, hard to use, and unlikely ever to be reused in fact. If this
software is not preserved, then the raw data is effectively unreadable, which
means there is no case for preserving it. There is therefore a case that at
least some details of the experimental environment – digital as well as
physical – are not reasonably preservable, and that as a result little effort
should be expended on preserving them.

It is data products that make raw data less necessary. It is feasible to
document the scientific meaning of data products, and the community expects
that a project will provide this documentation as part of the publication of
the products (indeed, it the documentation that makes these _products_ rather
than just a casual data snapshot). The data products allow researchers to dig
beneath the conclusions of a particular article (or indeed the contents of a
higher-level data product), and to criticise and build on what they find
there. Higher-level products are the result of higher-level scientific
judgements, and it is normal for these to be regenerated by researchers other
than the originators, either using their own software or the originators’
pipelines. These later-stage pipelines are more formally supported by
projects, which involves making them reasonably portable, so that they are
both easier to preserve as well as being more valuable objects of
preservation.

We should stress that we are not advocating deliberately deleting raw data,
and its associated pipelines – it _might_ be useful, and it _might_ be usable
– but simply noting that one should not overstate its value.

###  2.5 OAIS: suitability and motivation

In Sect. 3.1.1, we provide an overview of the OAIS model, and describe how it
relates to astronomical data.

The OAIS standard is formally a product of the Consultative Committee for
Space Data Systems (CCSDS),††margin:  http://www.ccsds.org ††margin:
http://www.ccsds.org and with this in its lineage it is quite naturally
matched to the data management problems of the physical sciences. Essentially
all the explicit and implicit assumptions of the OAIS standard are true in the
area we are studying: the data producer (a satellite or a detector) is usually
obvious, the various Information Package s (or data products) well understood,
and the Designated Community easily identified.††margin:  There is no
contradiction here with the remarks in Sect. 1.7 about the difficulty of
describing the Designated Community of science archive users. It is easy to
name a science Designated Community, but it may be hard to describe ahead of
time what those community members can be expected to know. A social science
archive may have an unpredictably broad range of ultimate users, but using the
archive will need little specialist knowledge; in contrast a particle physics
dataset will probably be of interest only to particle physicists, but the
normal education of such a physicist three decades hence, and thus the content
and extent of the specialised Representation Information that Community will
need, might be very hard to guess at. ††margin:  There is no contradiction
here with the remarks in Sect. 1.7 about the difficulty of describing the
Designated Community of science archive users. It is easy to name a science
Designated Community, but it may be hard to describe ahead of time what those
community members can be expected to know. A social science archive may have
an unpredictably broad range of ultimate users, but using the archive will
need little specialist knowledge; in contrast a particle physics dataset will
probably be of interest only to particle physicists, but the normal education
of such a physicist three decades hence, and thus the content and extent of
the specialised Representation Information that Community will need, might be
very hard to guess at.

The motivation for a digital preservation standard, as discussed in the OAIS
standard itself [4, §2], is that digital preservation represents a double
problem: (i) digital information is intrinsically harder to preserve than
traditional information, which is capable of sitting on a shelf in a well-
understood and intelligible format, and mouldering at a well-understood and
graceful rate; and (ii) more and more organisations are producing digital
information _and_ are implicitly expected to archive their own material. This
means that these non-specialist archives have a complicated task to perform,
which is potentially at odds with the daily urgencies of their main business.

This _appears_ to mean in turn (and in JISC contexts it is often taken to mean
in practice) that these organisations need as much detailed and prescriptive
help as possible, ideally devolving their archive responsibilities to a
central discipline- or funder-specific archive, to the extent possible while
respecting the low-level complications and friction alluded to in Sect. 1.7.
This is not the model which is appropriate for big-science datasets.

###  2.6 What should big-science funders require, or provide?

We have described several common features of big-science data management in
Sect. 1.3. and we have outlined some particular contrasts with other
communities in Sect. 1.7. As noted in Sect. 0.1, our focus here is on STFC’s
strategically funded projects, rather than the smaller projects funded by
individual research grants.

Big-science data sets are generally intimately coupled to solutions to
leading-edge technical challenges, and cannot usefully be regarded as
incremental changes to previous solutions. This, coupled with the general
availability of extensive technical expertise within such communities, means
that any generic solution is very unlikely to be appropriate, and that it is
both reasonable and feasible to require custom archiving solutions for such
projects. There is no _recipe_ for data preservation on this scale, and all
that can be hoped for is a structured approach to a custom solution. Having
said this, not even the most innovative science experiments are so completely
_sui generis_ that they warrant a data preservation approach which is
reimagined from scratch. It is therefore wasteful to ignore the considerable
intellectual investments in the OAIS model, the growing penumbra of
commentaries on and developments of it, and the minor industry of validation
and auditing efforts related to it.

We are therefore led to the conclusion that the most effective overall
strategy for effective data management in the large-scale experimental
physical sciences is that ††margin:  Recommendation 2 ††margin:
Recommendation 2 funders should simply require that a project develop a high-
level DMP plan as a suitable profile of the OAIS specification [4]. This
profile should be detailed enough to require negotiation with the funder and
with the experiment’s community, but can leave many of the implementation
details to the good engineering judgement of the project’s management. We
believe the LIGO DMP plan [5] can be taken to be exemplary in this regard.

Big-science projects have the technical skills, the management structures, and
the budgets to take on such a task, and to deliver a custom archive which can
be shown to meet identified goals. We recommend that ††margin:  Recommendation
3 ††margin:  Recommendation 3 funders should support projects in creating per-
project OAIS profiles which are appropriate to the project and meet funders’
strategic priorities and responsibilities.

The discussion in Sect. 3.5 suggests that one result of the development of an
OAIS-based DMP plan is that the resulting plan is explicit enough to generate
useful deliverables, and to benefit from the growing interest in OAIS
‘validation’.

We suggest the following specific funder actions.

  * •

Actively engage with projects to help them develop an OAIS profile. This will
include overview literature, including the OAIS specification, tutorial
reports such as [48], and commentary such as [49], or perhaps specialised
workshops if necessary. These are high-level introductions, rather than
procedure-based tick-lists.

  * •

Develop or support expertise in criticising and validating such OAIS profiles.
For example, the CASPAR consortium (see for example [50]) has developed
strategies for detailed validation of projects’ claims about long-term data
migration. Similar work – for example validating a project’s assumptions about
its Designated Community – would reassure the wider community that the archive
design is likely to achieve its goals for the future.

The first of these is reasonably straightforward, consisting of little more
than gathering resources. The second is a longer-term project which may
require some expertise to be built up and supported at a funder-supported
facility (such as RAL, in the UK), or through liaison with the DCC.

A corollary of this more active engagement is that funders must financially
support the preservation work they require. See Sect. 3.4.

##  3 The practicalities of data preservation

###  3.1 Modelling the archive

####  3.1.1 The OAIS model

We introduce here the main concepts of the OAIS model. Full details are in [4]
with a useful introductory guide in [48] and some discussion in the LSC
context in [5]; the OAIS motivation is further discussed in Sect. 2.5.

Figure 4: The highest-level structure of an OAIS archive, annotated with the
corresponding labels from conventional astronomical practice (redrawn from [4,
Fig. 2-4]). The dissemination data products will typically be the same as the
submitted ones, but archives can sometimes create value-added ones of their
own.

The term _OAIS_ stands for an _Open Archival Information System_. The word
‘open’ is not intended to imply that the archived data is freely available
(though it may be), but instead that the process of defining and developing
the system is an open one. The principal concern of an OAIS is to preserve the
usability of digital artefacts for a pragmatically defined long term. An OAIS
is not only concerned with storing the lowest-level _bits_ of a digital object
(though this part of its concern, and is not a trivial problem), but with
storing enough _information_ about the object, and defining an adequately
specified and documented _process_ for migrating those bits from system to
system over time, that the information or knowledge those bits represent can
be retrieved from them at some indeterminate future time. The OAIS model can
therefore be seen as addressing an administrative and managerial problem,
rather than an exclusively technical one.

The OAIS specification’s principal output is the _OAIS reference model_ ,
which is an explicit (but still rather abstract) set of concepts and
interdependencies which is believed to exhibit the properties that the
standard asserts are important (Fig. 4). The OAIS model can be criticised for
being so high-level that ‘‘almost any system capable of storing and retrieving
data can make a plausible case that it satisfies the OAIS conformance
requirements’’ [49], and there exist both efforts to define more detailed
requirements [49], and efforts to devise more stringent and more auditable
assessments of an OAIS’s actual ability to be appropriately responsive to
technology change [50].

An OAIS archive is conceived as an entity which preserves objects (digital or
physical) in the Long Term, where the ‘Long Term’ is defined as being long
enough to be subject to technological change. The archive accepts objects
along with enough Representation Information to describe how the digital
information in the object should be interpreted so as to extract the
information within it (for example, the FITS specification is Representation
Information for a FITS file). That Information may need further context – for
example, to say that a file is an ASCII file requires one to define what ASCII
means – and the collection of such explanations turns into a Representation
Network. This information is all submitted to the archive in the form of a SIP
agreed in some more or less formal contract between the archive and its data
producers.

Once the information is in the archive, the long-term responsibility for its
preservation is _transferred_ from the provider to the archive, which must
therefore have an explicit plan for how it intends to discharge this.

The archive distributes its wares to Consumers in one or more Designated
Communities, by transforming them, if necessary, into the DIP which
corresponds to a ‘data product’. The members of the Designated Community are
those users, in the future, whom the archive is designed to support. This
design requires including, in the AIP, Representation Information at a level
which allows the Designated Community to interpret the data products _without
ever having met one of the data Producers_ , who are assumed to have died,
retired, or forgotten their email addresses.

The OAIS model originated within the space science community, so it can be
mapped to the physical science data of the GW community without much violence.

####  3.1.2 The DCC Curation Lifecycle model

The OAIS model is on the face of it a linear one, and suggests that data is
created, then ingested, then preserved, and then accessed, in a process which
has a clear beginning and end. This is compatible with the observation that
one point of archiving data is to reuse or repurpose it, creating new
archivable data products in turn, but this longer-term cycle remains only
implicit in the model. The OAIS model is therefore very usefully explicit
about those aspects of archival work concerned with long-term preservation,
but its conceptual repertoire is such that a discussion framed by it runs the
risk of underemphasizing the range of roles a data repository has, or even of
marginalising it.

Figure 5: The DCC lifecycle model, from [51]

In contrast,††margin:  We thank Dorothea Salo, of the University of Wisconsin
library, for emphasizing to us the useful applicability of the DCC model to
the case of big science data, and Angus Whyte, for elaborating the contrasts
between the DCC and OAIS models. ††margin:  We thank Dorothea Salo, of the
University of Wisconsin library, for emphasizing to us the useful
applicability of the DCC model to the case of big science data, and Angus
Whyte, for elaborating the contrasts between the DCC and OAIS models. the DCC
has produced a lifecycle model [51] (Fig. 5) which stresses that data
creation, management, and reuse are part of a cycle in which preservation
planning, for example, can naturally happen before data creation as well as
after it; and in which data can be appraised, reappraised, and possibly
disposed of if it becomes obsolete. It therefore makes explicit both the
short- and long-term cycles in the flow of active research data, and it
emphasizes the active involvement of data curators in maintaining that cycle.

Cycles of use and re-use are not the only links between datasets. As discussed
in [52], one digital object can also provide context for another, in a variety
of ways. To some extent this remark rediscovers the notion of the OAIS
Representation Network, and this in turn prompts us to stress that although we
have contrasted OAIS and DCC here, they are not in competition: OAIS is
concerned with the creation and management of a working archive with
gatekeepers and firm goals; the DCC model is concerned with the location of
the archive in the wider intellectual context.

The DCC model is immediately compatible with the observation, in Sect. 3.4
below, that HEP and GW archives effectively avoid some preservation costs by
seeing long-term preservation as only part of the role of a data repository.
Accepting data, making it available as working storage, transforming it into
immediately useful forms, or appraising (possibly regenerable) datasets whose
storage costs outweigh their usefulness, all give the archive a familiarity
with the data, and the researchers a familiarity with the archive, which means
that the decision to select certain data for long-term preservation is
potentially more easily reached, more easily defended and more easily funded,
than if the archive is conceived as a cost-centre bucket bolted on the side of
the project. This appears to be borne out by the LIGO experience, in which the
new DMP plan was developed and successfully argued for by the same personnel
who were long responsible for the design and management of the data management
system on which everyone’s daily work depends.

###  3.2 Software preservation

As discussed in, for example, Sect. 1.6.2, there is often a substantial amount
of important information encoded in ways which are only effectively documented
in software, or software configuration information. There is therefore an
obvious case for preserving this software (though note the caveats of Sect.
2.4).

Preservation of a software pipeline requires preserving the pipeline software
itself, a possibly large collection of libraries the software depends on, the
operating system (OS) it all runs on, and the configuration and start-up
instructions for setting the whole thing in motion. The OS may require
particular hardware (CPUs or GPUs), the software may be qualified for a very
small range of OSs and library versions, and it may be hard to gather all of
the configuration information required (there is some discussion of how one
approaches this problem in for example [26]). It is not certain that it is
necessary, however: if the data products are well-enough described, then re-
running the analysis pipeline may be unnecessary, or at least have a
sufficiently small payoff to be not worth the considerable investment required
for the software preservation. We feel that, of the two options – preserve the
software, or document the data products – the latter will generally be both
cheaper and more reliable as a way of carrying the experiment’s information
content into the future, and that this tradeoff is more in favour of data
preservation as we consider longer-term preservation.

This last point, about the changing tradeoff, emphasizes that the two options
are not exclusive: one can preserve data _and_ preserve software, and the
JISC-funded Software Sustainability Institute††margin:
http://www.software.ac.uk/ ††margin:  http://www.software.ac.uk/ provides a
growing set of resources which provide guidance here. However the solutions
presented generally focus on active curation, in the sense of preserving
software through continuing use and maintenance. This can be
successful††margin:  The UK Starlink project provided astronomical software.
It ran from 1980 to 2005, when it was rescued from oblivion by being taken up
by the UK Joint Astronomy Centre Hawai‘i. The current distribution includes
still-working code from the 80s. The Netlib and BLAS libraries have components
which date from the 70s. ††margin:  The UK Starlink project provided
astronomical software. It ran from 1980 to 2005, when it was rescued from
oblivion by being taken up by the UK Joint Astronomy Centre Hawai‘i. The
current distribution includes still-working code from the 80s. The Netlib and
BLAS libraries have components which date from the 70s. , and is the approach
implicit in [26], but it seems brittle in the face of significant funding
gaps, and would not deal well with the case where a software release is
deliberately unused, for example because it has been superseded.

###  3.3 Data management planning

####  3.3.1 DMP in space

As one might expect, both NASA and ESA have formalised DMP plans.

NASA’s National Space Science Data Center (NSSDC) ††margin:  %**** report.tex
Line 2500 ****http://nssdc.gsfc.nasa.gov ††margin:  %**** report.tex Line 2500
****http://nssdc.gsfc.nasa.gov has led NASA’s data planning since the mid-80s.
It was initially the NSSDC which negotiated a Project DMP plan with missions,
but since the 1990s this has become the responsibility of the NASA Planetary
Data System (PDS) ††margin:  http://pds.nasa.gov/ ††margin:
http://pds.nasa.gov/ . The NSSDC’s data retention policy††margin:
http://nssdc.gsfc.nasa.gov/nssdc/data_retention.html ††margin:
http://nssdc.gsfc.nasa.gov/nssdc/data_retention.html describes what categories
of data product should be retained indefinitely, and the PDS provides
resources to mission planners on the processes and tools††margin:
http://pds.nasa.gov/tools/index.shtml ††margin:
http://pds.nasa.gov/tools/index.shtml for preparing data for
preservation.††margin:  We are grateful to Paul Butterworth of NASA for
helpful advice here. ††margin:  We are grateful to Paul Butterworth of NASA
for helpful advice here.

ESA’s Planetary Science Archive††margin:
http://www.rssd.esa.int/index.php?project=PSA&page=about ††margin:
http://www.rssd.esa.int/index.php?project=PSA&page=about ‘‘provides expert
consultancy to all of the data producers throughout the archiving process. As
soon as an instrument is selected, PSA begin working with the instrument team
to define a set of data products and data set structures that will be suitable
for ingestion into the long-term archive.’’ The ESA archive is by design
compatible with the PDS.

####  3.3.2 Current and future DMP in the LSC

The current LIGO DMP plan [5], discusses DM planning with an emphasis on the
preparations for the eventual public data release.

The LIGO DMP plan proposes a two-phase data release scheme, to come into play
when aLIGO is commissioned; this was prepared at the request of the NSF,
developed during 2010–11, and will be reviewed yearly.

The plan documents the way in which the consortium will make LIGO data open to
the broader research community, rather than (as at present) only those who are
members of the LSC. This document describes the plans for the data release and
its proprietary periods, and outlines the design, function, scope and
estimated costs of the eventual LIGO archive, as an instance of an OAIS model.
This is a high-level plan, with much of the detailed implementation planning
delegated to partner institutions in the medium term.

In the first phase, data is released much as it is at present: validated data
will be released when it is associated with detections, or when it is related
to papers announcing _non_ -detections (for example, associated with another
astronomical event which might be expected or hoped to produce detectable
GWs). In the second phase – after detections have become routine, and the LIGO
equipment is acting as an observatory rather than a physics experiment – the
data will be routinely released in full: ‘‘the entire body of gravitational
wave data, corrected for instrumental idiosyncrasies and environmental
perturbations, will be released to the broader research community. In
addition, LIGO will begin to release near-real-time alerts to interested
observatories as soon as LIGO _may_ have detected a signal.’’ This second
phase will begin after LIGO has probed a given volume of space-time (see [5,
ref 7]), _or_ after 3.5 years have elapsed since the formal LIGO
commissioning, whichever is earlier. Alternatively, LIGO may elect to start
phase two sooner, if the detection rate is higher than expected.

In phase two, the data will have a 24-month proprietary period.

The DMP plan describes three (OAIS) Designated Communities. Quoting from [5,
§1.5], the communities are as follows.

  * •

LSC scientists: who are assumed to understand, or be responsible for, all the
complex details of the LIGO data stream.

  * •

External scientists: who are expected to understand general concepts, such as
space-time coordinates, Fourier transforms and time-frequency plots, and have
knowledge of programming and scientific data analysis. Many of these will be
astronomers, but also include, for example, those interested in LIGO’s
environmental monitoring data.

  * •

General public: the archive targeted to the general public, will require
minimal science knowledge and little more computational expertise than how to
use a web browser. We will also recommend or build tools to read LIGO data
files into other applications.

The LIGO DMP plan is, we believe, a good example of a plan for a project of
LIGO’s size: it is specific where necessary, it was negotiated with the
project’s funder (NSF) so that it achieved their goals, and it went through
enough iterations with the broader LIGO community (the agreed version in [5]
is version 14) that its authors could be confident it had their approval, and
that the community was comfortable with what the DMP plan was proposing. The
document has a strong focus on the LIGO data release criteria, since this was
the most immediate concern of both the funder and the project, but it
systematically lays out a high-level framework for future data preservation,
guided by the OAIS functional model.

###  3.4 Data preservation costs

There is a good deal of detailed information, and some modelling, of the costs
of digital preservation. The KRDS2 study [44, §§6&7] includes detailed
costings from a number of running digital preservation projects, in some cases
down to the level of costings spreadsheets. The LIFE3 project has also
developed predictive costings tools [53], and the PLANETS project
(http://www.planets-project.eu/) has generated a broad range of materials on
preservation planning, including costing studies.

Although there is a broad range of preservation projects surveyed in the KRDS
report, there are numerous common features. Staff costs dominate hardware
costs, and scale only very weakly with archive size. The study also notes that
acquisition and ingest costs are a substantial fraction (70–80%) of overall
staff costs, but also scale very weakly with archive size. These are
relatively small archives, generally below a few $\mathrm{T}\mathrm{B}$ in
size, where ingest is a significant component of the workload. In this report
we are interested in archives three or four orders of magnitude larger than
this where (as discussed below) ingest may be cheaper, but in broad terms, it
appears still to be true that staff costs dominate hardware costs at larger
scales, and scale only weakly with archive size.

Parenthetically, notice that the above discussion prompts the question ‘what
is the size of an archive?’ The number of bytes it consumes is an obvious and
readily available measure, but may not be particularly meaningful in this
context. The number of items (such as interview transcripts, images or
database rows) may be a better measure, and still objectively identifiable,
archive by archive. If there were some measure of abstract information
content, we speculate that this is what would scale most straightforwardly
with the effort required for quality control and metadata curation, and hence
with staff effort. We hesitate to ask what such a measure might be, in case
the answer is ‘citation analysis’.

The lack of scaling with size, even when an archive progressively grows in
size, seems to suggest that it is an archive’s _initial_ size (in the sense of
small, medium or large, for the time) that largely governs the costs.

We were given access to confidential figures for the development and
operations of a mid-to-large size astronomy archive (of order
$10\text{\,}\mathrm{T}\mathrm{B}$ of relational data and
$100\text{\,}\mathrm{T}\mathrm{B}$ of flat file data), developed by an
experienced archive site. The archive software and system development cost
25–30 staff-years of effort: the bulk of this was for the core database
system, but between a quarter and a third was for software to support ingest
and the generation of data products. The organisation budgets around 3 FTEs
for operation of this archive, which includes ingest, quality control and
helpdesk support (this is an estimated fraction of an operations team covering
several archives at the same site, so there may be some economies of scale).
About a quarter of the annual operating budget is spent on hardware.

The European Southern Observatory (ESO) data archive manages data from
multiple ESO facilities;††margin:  We are most grateful to Fernando Comerón,
of ESO, for sharing these figures. ††margin:  We are most grateful to Fernando
Comerón, of ESO, for sharing these figures. it shares space with the still-
developing ALMA archive, but the figures below do not include ALMA. The
archive is based on spinning disks backed by a tape library (for further
details, see [54]). It currently holds $190\text{\,}\mathrm{TB}$, increasing
at around $7\text{\,}\mathrm{TB}\text{\,}{\mathrm{month}}^{-1}$. The hardware
costs average around $330\text{\,}\mathrm{k\hbox{{#\cr\vfil\hbox
to0.70007pt{=\hss}\vfil\cr\hbox{C}\crcr}}}\text{\,}{\mathrm{yr}}^{-1}$, which
includes hardware replacement and data migration, and which has remained flat
for some years, despite the slowly increasing data volumes. Running costs
amount to $55\text{\,}\mathrm{k\hbox{{#\cr\vfil\hbox
to0.70007pt{=\hss}\vfil\cr\hbox{C}\crcr}}}\text{\,}{\mathrm{yr}}^{-1}$ (some
smaller systems account for part of this), and licences, networks and other
consumables account for about $30\text{\,}\mathrm{k\hbox{{#\cr\vfil\hbox
to0.70007pt{=\hss}\vfil\cr\hbox{C}\crcr}}}\text{\,}{\mathrm{yr}}^{-1}$.
Manpower costs come to 4 FTEs of ESO staff plus around
$270\text{\,}\mathrm{k\hbox{{#\cr\vfil\hbox
to0.70007pt{=\hss}\vfil\cr\hbox{C}\crcr}}}\text{\,}{\mathrm{yr}}^{-1}$ of
outsourced staff. Neither hardware nor software costs appear to scale with
data volume, with some cost elements even dropping as the archive moves to
completely on-line data distribution.

There is some discussion of the CDS funding model in Sect. 1.4.1.

The NASA PDS has developed a parameterized model for helping proposers
estimate the costs involved in preparing data for archiving in the
PDS††margin:  http://pds.nasa.gov/tools/cost-analysis-tool.shtml ††margin:
http://pds.nasa.gov/tools/cost-analysis-tool.shtml ; most relevantly for the
above discussion it includes a scaling with data volume of
$1+1.5\log_{10}(\mbox{volume/MB})$ (that is, a multiplier which increases by
1.5 for each order of magnitude increase in data volume).

As noted in Sect. 1.5, the HEP community is now constructing more detailed
plans for data preservation, and the associated costs. Reference [26]
estimates that a formal long-term archive (a level-3 or -4 archive, in the
terms of that paper) would cost 2–3 FTEs for 2–3 years after the end of the
experiment, followed by 0.5–1.0 FTE/year/experiment spent on the archive’s
preservation. They compare this to the 100s of FTEs spent on for the running
of the experiment, and on this basis claim an archival staff investment of 1%
of the peak staff investment, to obtain a 5–10% increase in output (the latter
figure is based on their estimate that around 5–10% of the papers resulting
from an experiment appear in the years immediately after the experiment
finishes; since this latter figure is derived on the current model, which
achieves this without any formal preservation mechanisms, this estimate of the
return on investment in archives may be optimistic).

It is worth noting that in astronomical, HEP and GW contexts, archive ingest
is generally tightly integrated with the system for day-to-day data
management, in the sense that data goes directly to the archive on acquisition
and is retrieved from that archive by researchers, as part of normal
operations. On the other side of the archive, projects will generate and
disseminate data products – which look very much like OAIS DIP s – as part of
their interaction with external collaborators, without regarding these as
specifically archival objects. Thus the submissions into the archive may
consist of both raw data and things which look very much like DIPs, and the
objects disseminated will include either or both very raw and highly processed
data. The _long-term_ planning represented in the LIGO DMP plan [5], for
example, is therefore less concerned with setting up an archive, than with the
adjustments and formalizations required to make an existing data-management
system robust for the archival long term, and more accessible to a wider
constituency. What this means, in turn, is that some fraction of the OAIS
ingest and dissemination costs (associated with quality control and metadata,
for example) will be covered by normal operations, with the result that the
_marginal_ costs of the additional activity, namely long-term archival ingest
and dissemination, are probably both rather low and typically borne by
infrastructure budgets rather than requiring extra effort from
researchers.††margin:  This is consistent with the ERIM project’s conclusions
that ‘‘ideally information management interventions should result in a zero
net resource increase’’ [55, p.8]. In this case there is no extra resource
required from the researchers, though there might be a need for extra resource
under an infrastructure heading. ††margin:  This is consistent with the ERIM
project’s conclusions that ‘‘ideally information management interventions
should result in a zero net resource increase’’ [55, p.8]. In this case there
is no extra resource required from the researchers, though there might be a
need for extra resource under an infrastructure heading. This is corroborated
by our informants above, who generally regard archive costs as coming under a
different heading from ‘data processing costs’. The point here is not that the
OAIS model does not fit well – it fits very well indeed – nor that ingest and
dissemination do not have costs, but that if the associated activities can be
contrived to overlap with normal operations, then the costs directly
associated with the archive may be significantly decreased. This is the
intuition behind the recent developments in ‘archive-ready’ or ‘preservation-
aware storage’ (cf [43] and Sect. 3.1.2), and confirms that it is a viable and
effective approach.

As a final point, we note that big-science projects are inevitably also large-