A Guide toData Managementin Ecology
and Evolution

Contents
Preface .................................................................................................................................................... 01
Introduction ......................................................................................................................................... 03
Planning data management ......................................................................................................... 11Creating data ....................................................................................................................................... 17
Processing data .................................................................................................................................. 21
Documenting data ............................................................................................................................. 27
Preserving data .................................................................................................................................. 29
Sharing data ......................................................................................................................................... 33
Reusing data ........................................................................................................................................ 34
Sources and Further Reading ....................................................................................................... 35
Acknowledgements .......................................................................................................................... 35
Index ........................................................................................................................................................ 36

Copyright © British Ecological Society, 2014,
except where noted on certain images.

This work is licensed under a Creative Commons Attribution
4.0 International License. To view a copy of this licence,
except where noted on certain images.
visit http://creativecommons.org/licenses/by/4.0/
British Ecological SocietyCharles Darwin House
12 Roger Street
London WC1N 2JU
UK
hello@britishecologicalsociety.org

This booklet is also available electronically at:www.britishecologicalsociety.org/publications/guides-to
Cover image: Adam Seward
Booklet design: Cylinder2Preface
Good data management is fundamental to research
excellence. It produces high-quality research data that areaccessible to others and usable in the future. The value ofdata is now explicitly acknowledged through citations so
researchers can make a difference to their own careers, aswell as to their fields of research, by sharing and making
data available for reuse.
		 Many of the scientific questions on researchers’
agendas now require collaborative research. Data management and sharing isfundamental to this and, consequently, many research funders have come to view
data sharing as a priority. This attitude is reflected in their policies and requirements
for data management plans during applications for research grants.
		 Higher education institutions have reacted to this growing trend towards data
sharing and management by creating their own policies, guides, tools and training.
Publishers have also responded by introducing data archiving policies to ensure thatthe data behind published research are preserved and made accessible for futureuse. The British Ecological Society (BES) believes that accessibility and preservationof data is important to the fostering of ecological science; in 2011 we formulated adata archiving policy to reflect this and from 2014 introduced a mandate that all data
behind published material in our journals be archived.
		 This guide is designed to help early career researchers navigate data
management firstly by explaining what data and data management are and why
data sharing is important, and secondly by providing advice and examples of bestpractice in data management. The information used in this guide was sourced fromvarious online resources (see Sources) and by approaching researchers working
with ecology and evolution (see Acknowledgments) for real-life examples of thechallenges they have faced and lessons they have learned from their own data
management experiences.
Kate Harrison
Assistant Editor
British Ecological Society12Introduction
What are research data?
Research data are the factual pieces of information used to produce and validate
research results. Data can be classified into five categories:•Observational: data which are tied to time and place and are irreplaceable		 (e.g. field observations, weather station readings, satellite data)•Experimental: data generated in a controlled or partially controlled		 environment which can be reproduced although it may be expensive to do so		 (e.g. field plots or greenhouse experiments, chemical analyses)•Simulation: data generated from models (e.g. climate or population modelling)•Derived: data which are not collected directly but generated from (an)other data		 file(s) (e.g. a population biomass which has been calculated from population
		 density and average body size data)•Metadata: data about data (see Document)
A key challenge facing researchers today is the need to work with different data
sources. It is not uncommon now for projects to integrate any combination of datatypes into a single analysis, even drawing on data from disciplines outside ecology
and evolution. As research becomes increasingly collaborative and interdisciplinary,
this issue will grow in prevalence.

3

Introduction

CreateReuse

Process

Plan
Share

Document(& use)Preserve

Fig. 1. The data lifecycle

The data lifecycle
Data often have a longer lifespan than the project they were created for, as illustrated
by the data lifecycle (Fig. 1).
		 Some projects may only focus on certain parts of the lifecycle, such as primarydata creation, or reusing others’ data. Other projects may go through several
revolutions of the cycle. Either way, most researchers will work with data at all stages
throughout their career.
		 Traditionally, researchers were mainly concerned with the early stages of thelifecycle – creating, processing and using. Part of the reason for this was that data
sharing and archiving was much harder to do when data were only on paper. Now,a combination of technologies that allow data sharing, and the increasing need to
combine different datasets to address ecological questions means that preserving
and sharing data has become an important part of the scientific process.
4Introduction
Why should I manage data?
Data management concerns how you plan for all stages of the data lifecycle andimplement this plan throughout the research project. Done effectively it will ensurethat the data lifecycle is kept in motion. It will also keep the research process efficientand ensure that your data meet all the expectations set by you, funders, research
institutions and legislation (e.g. copyright, data protection).
		 Ask yourself, ‘Would a colleague be able to take over my project tomorrow if I
disappeared, or make sense of the data without talking to me?’ If you can answer
with yes, then you are managing your data well.
Potential benefits of good data management include:•ensuring data are accurate, complete, authentic and reliable•increasing research efficiency•saving time and money in the long run – ‘undoing’ mistakes is frustrating•meeting funder requirements•minimizing the risk of data loss
•preventing duplication by others•facilitating data sharing
Why should I share my data?
It is increasingly common for funders and publishers to mandate data sharing
wherever possible. In addition, some funding bodies require data managing andsharing plans as part of grant applications. Sharing data can be daunting, but data
are valuable resources and their usefulness could extend beyond the original
purpose for which they were created. Benefits of sharing data include:•increasing the impact and visibility of research•encouraging collaborations and partnerships with other researchers•maximizing transparency and accountability•encouraging the improvement and validation of research methods•reducing costs of duplicating data collection•advancing science by letting others use data in innovative ways

5

6

Introduction
There are, however, reasons for not sharing data. These include:•if the datasets contain sensitive information about endangered or threatened		 species•if the data contain personal information – sharing them may be a breach of the		 Data Protection Act in the UK, or equivalent legislation in other countries•if parts of the data are owned by others – you may not have the rights to share		 themDuring the planning stages of your project you will determine which of your data canand should be shared. Journal data archiving policies recognize these reasons for not
sharing. The BES policy, for example, states:
Exceptions, including longer embargoes or an exemption from the requirement, may
be granted at the discretion of the editor, especially for sensitive information such asconfidential social data or the location of endangered species.
Data sharing is one manifestation of a cultural shift towards open science. Other
terms that will become more prevalent as this movement grows include:
Virtual research environments: a relatively new phenomenon, currently used by only
a few universities for collaborative research within the institution. They allow data
sharing among colleagues by providing a private virtual workspace for membersof a research group to share files, manage workflows, track version control, accessresources and communicate with each other.
Open notebooks: lab notebooks that are made publicly available online, including all
the raw data and any other materials that may be generated in the research project– even ‘failed’ experiments. They are a transparent approach to research, allowing
others to access and feedback on your project in real time, without limitations onreuse. Open notebooks are not widely used but they are gaining momentum as partof the movement towards open approaches to research practices and publishing.
Open data: public data that anyone can use and that are licensed in a way that allows
for unrestricted reuse. Advocates of open data are often interested in new computing
techniques that unlock the potential of information held in datasets. The term open
data came into the mainstream in 2009 when governments, including those of the
UK, USA and New Zealand, announced initiatives to open up access to their public
information.
7

Introduction
Big data: a term used to describe extremely large, complex datasets that are difficult
to process using traditional methods and require extra computer power. ‘Big data’
as a concept is more subjective than open data because of this dependence on
computers to process them – what seems big today may not seem so big tomorrow
when computing technologies are more advanced.
As more and more researchers share their research and work collaboratively, thepossibilities of combining open data and big data increase, and the results of thiscombination have the potential to be very powerful1. In fields such as ecology, open
and big data could contribute to answering questions on climate change and help
shape environmental policy.

http://www.theguardian.com/public-leaders-network/2014/apr/15/big-data-open-data-transform-government
accessed 10 October 2014.

1

8

9

10

Planning data management

Plan

Regardless of whether your funder requires you to havea data management or sharing plan as part of a grantapplication, having such a plan in place before you begin
your research project will mean you are prepared for any
data management issues that may come your way.

“I think that having a data management plan is absolutely crucial and planning
for archiving and making your data accessible (after a suitable time period if
you like) is really important for research in the future. There are now some great
resources out there that help you to organize and plan your data collection andmake it more usable for yourself as well as others who may wish to use it in the
future. Make a plan. Stick to the plan from day one.”- Yvonne Buckley, Trinity College Dublin, Ireland
What if things do not go to plan? Good data management is a reflective process
and should adapt and respond to changes in circumstance or opportunities that
may arise throughout a project. Keep your plan as a ‘living document’ which iscontinuously revisited and adapted, if necessary.
Before you start planning:
Check funder specifications for data management plans. Each funder will haveslightly different requirements but common requirements include: descriptionof the data, quality assurance measures, plans for sharing, restrictions on sharing(if applicable), copyrights and intellectual property rights of data, storage andbackup measures, data management roles and responsibilities, and costs of data
management.
Consult with your institution. Institutions have resources and policies in placethat can help throughout the data management process. Policies will state an
institution’s expectations of good data management as well as outline any issues
you need to be aware of when it comes to implementing your plan. Advice andresources for data management on university websites can often be found on the
Library or Information Services pages.
11Planning data management
Consider your budget. Data management will have its costs and this should be
included within the larger budget of your whole research project. You can use thedata lifecycle to help price each activity needed throughout data management interms of people’s time or extra resources required. Costing tools may be available
from universities and other online data management resources.
Talk to your supervisor, colleagues and collaborators. Discuss any challenges they
have already faced in their experience – learn from their mistakes.
Key things to consider when planning:Time. Writing a data management plan may well take a significant amount of time.
It is not as simple as filling out a template or working through a checklist of things
to include. Planning for data management should be thorough before the research
project starts to ensure that data management is embedded throughout the
research process.
Design according to your needs. Data management should be planned andimplemented with the purpose of the research project in mind. Knowing how yourdata will be used in the end will help in planning the best ways to manage data at
each stage of the lifecycle (e.g. knowing how the data will be analysed will affect how
the data will be collected and organized).
“As a student I was always told to plan my analysis well in advance and collectand organize the data toward this. I (and most of my early career colleagues)
ignored this advice. Only after spending hours reorganising different datasets
did I learn my lesson.”- Kulbhushansingh Suryawanshi, Nature Conservation Foundation, India
Roles and responsibilities. Creating a data management plan may be theresponsibility of one single person, but data management implementation
may involve various people at different stages. One of the major uses of a data
management plan is to enable coordinated working and communication among
researchers on a project – with the increase of consortium projects acrossinstitutions, this role of planning is a really important way of making sure everyone
has a common understanding of what data are being created and used, and underwhat terms they are available. During planning it is therefore important to clearly
assign roles and responsibilities instead of merely presuming them. Others who
12Planning data management
may be involved in data management besides you and your collaborators include
external people involved in collecting data, university IT staff who provide storage
and backup services, external data centres or data archives.
Review. Plan how data management will be reviewed throughout the project
and adapted if necessary; this will help to integrate data management into theresearch process and ensure that the best data management practices are being
implemented. Reviewing will also help to catch any issues early on, before they turninto bigger problems.
The data management checklist (p14) from the UK Data Archive will help prompt youon the things you need to think about when planning your data management, andenable you to keep on top of your data management once the project has started.

13

Data Management Checklist 2• Are you using standardized and consistent procedures to collect, process, 				 check, validate and verify data?
• Are your structured data self-explanatory in terms of variable names, codes		 and abbreviations?
• Which descriptions and contextual documentation can explain what your		 data mean, how they were collected and the methods used to create them?
• How will you label and organize data, records and files?
• Will you apply consistency in how data are catalogued, transcribed and 				 organized, e.g. standard templates or input forms?
• Which data formats will you use? Do formats and software enable sharing
		 and long-term validity of data, such as non-proprietary software and		 software based on open standards?
• When converting data across formats, do you check that no data or internal
		 metadata have been lost or changed?
• Are your digital and non-digital data, and any copies, held in a safe and		 secure location?
• Do you need secure storage for personal or sensitive data?
• If data are collected with mobile devices, how will you transfer and store		 the data?
• If data are held in various places, how will you keep track of versions?
• Are your files backed up sufficiently and regularly and are backups stored		 safely?
• Do you know what the master version of your data file is?
• Do your data contain confidential or sensitive information? If so, have you 				 discussed data sharing with the respondents from whom you collected		 the data?
• Are you gaining (written) consent from respondents to share data beyond
		 your research?
• Do you need to anonymize data, e.g. to remove identifying information or 				 personal data, during research or in preparation for sharing?
• Have you established who owns the copyright of your data?
• Who has access to which data during and after research? Are various access		 regulations needed?
• Who is responsible for which part of data management?
• Do you need extra resources to manage data, such as people, time or hardware?
UK Data Archive ‘Managing and Sharing Data’, May 2011, p 35, CC BY-SA 3.0, Copyright 2011 University of Essex214© Jeremy Holloway
15

16

Creating data
In the data lifecycle creating datasets occurs as a
researcher collects data in the field or lab, and digitizes
them to end up with a raw dataset.
		 Quality control during data collection is importantbecause often there is only one opportunity to collectdata from a given situation. Researchers should becritical of methods before collection begins – high-quality
methods will result in high-quality data. Likewise, whencollection is under way, detailed documentation of thecollection process should be kept as evidence of quality.

CreateKey things to consider during data collection:•logistical issues in the field (see Frida Piper’s experience of collecting data on p19)
•calibration of instruments•taking multiple measurements/observations/samples•creating a template for use during data collection to ensure that all informationis collected consistently, especially if there are multiple collectors•describing any conditions during data collection that might affect the qualityof the data•creating an accompanying questionnaire for multiple collectors, asking them any
questions which may affect the quality of the data•widening the possible applications of data, and therefore increasing their impact,by adding variables and parameters to data, e.g. wider landscape variables, which
will encourage reuse and potentially open up new avenues for research“The project I work on supports a variety of data collection for both long-term
data collection, as well as a diverse range of PhD projects. I collect behaviouraldata through multiple methods, data from laboratory samples and a range
of environmental data. The main challenge of such a large-scale research
project is due to the fact that there are numerous volunteers and PhD students
working together to collect the same data. This means there is a constantneed for effective communication and clear ways to record not only the data
themselves, but the fact that they have been collected.”- Cassandra Raby, University of Liverpool, UK
17

Creating data

“Digitize and organize your data immediately after field collection so it is freshin your mind and you do not forget about aspects that only the field collector isaware of.”- Roberto Salguero-Gomez, University of Queensland, Australia“There are very few researchers who have collected 10-year datasets, yet theresults that emerge from such data are revealing and impossible to predict
from short-term data. Think of associated data that could help you if you had alonger time sequence, then begin to collect those data. ”- Andy Dyer, University of South Carolina Aiken, USA“If using empirical data collected by someone else, discuss the format of theoutput and generate a template of the recording spreadsheet for the dataprior to recording, including the accompanying explanatory notes and data
variable keys. This is particularly important if you are involved in a multi-siteexperiment – a common recording template provided to all collaborators will
make collating the data easier. ”- Caroline Brophy, National University of Ireland Maynooth, IrelandSpace for possible pic?

18

Creating data
Data may be collected directly in a digital form using devices that feed results
straight into a computer or they may be collected as hand-written notes. Either way,
there will be some level of processing involved to end up with a digital raw dataset.
Key things to consider during data digitization include:•designing a database structure to organize data and data files•using a consistent format for each data file – e.g. one row represents a complete		 record and the columns represent all the parameters that make up that record
		 (this is known as spreadsheet format)•atomizing data – make sure that only one piece of data is in each entry•using plain text characters (e.g. ASCII, Unicode) to ensure data are readable by		 a maximum number of software programmes•using code – coding assigns a numerical value to variables and allows for		 statistical analysis of data. Keep coding simple•describing the contents of your data files in a ‘readme.txt’ file, or other metadata		 standard, including a definition of each parameter, the units used and codes for		 missing values•keeping raw data raw“The region where I live and work, Chilean Patagonia, is remote, pristine,
isolated and has an often harsh climate. To collect data I usually have to driveon unpaved roads, then hike and climb to the treeline with a cooler box full of
ice packs which I use to conserve the tissue samples I collect. As there are nouniversities in the area, I don’t usually have students or assistants to help, andoften do this collection alone. Back in the lab there is limited access to basic
materials needed to perform chemical analysis, and many of the procedures
have never been performed in that lab before, so I must install their protocol
for the first time. All of these logistical limitations in the field and the lab mean I
must be tough, efficient and independent.”- Frida Piper, Centro de Investigación en Ecosistemas de la Patagonia, Chile

19© Kevin Weng.
The CC-BY licence does not apply to this image.
20

Processing data
Data should be processed into a format that is suited tosubsequent analyses and ensures long-term usability.
Data are at risk of being lost if the hardware and software
originally used to create and process them are renderedobsolete. Therefore, data should be well organized,
structured, named and versioned in standard formats
that can be interpreted in the future.
		 Digital information can be altered very easily,
so it is important to be transparent in all aspects ofprocessing that have taken place. Careful documentation and using a script will help
demonstrate the authenticity of the data.

Process

File formats. Data should be written in non-proprietary formats, also known as open
standard formats, as far as possible. These files can be used and implemented byanyone, as opposed to proprietary formats which can only be used by those with thecorrect software installed. The most common format used for spreadsheet data arecomma-separated values files (.csv). Other non-proprietary formats include: plain text
files (.txt) for text; and GIF, JPEG and PNG for images.
Filenames and folders. To keep track of data and know how to find them, digital files
and folders should be structured and well organized – this is particularly importantwhen working in collaboration. Use a folder hierarchy that fits the structure of
the project (e.g. grouping according to who collected data is only relevant to thecollectors, not to those using the data for other things – grouping by data or site
may be more relevant) and ensure that it is used consistently. Drawing a folder map
which details where specific data are saved may be particularly useful if others will
be accessing folders, or if there is simply a lot to navigate.
Filenames should be unique, descriptive, succinct, naturally ordered and consistent.
Ideally they should describe the project, file contents, location, date, researcher’s
initials and version. Computers will add basic properties to a file, such as dates andfile type, but relying on these is not best data management practice. Do not use
spaces in filenames – these can cause problems with scripting and metadata.
Quality assurance. Checking that data have been edited, cleaned, verified andvalidated to create a reliable masterfile which will become the basis for further
analyses. Use a scripting language, such as R, to process your data for quality
checking so that all steps are documented.
21

Processing data
Assurance checks may include:•identifying estimated values, missing values or double entries•performing statistical analyses to check for questionable or impossible values

		 and outliers (which may just be typos from data entry)•checking the format of the data for consistency across the dataset•checking the data against similar data to identify potential problems“Communicating the decision process of cleaning data clearly via a scripted
audit trial lets others find my mistakes and helps me correct them. Write ascript that describes every bit of processing you do. This will mean everythingis repeatable and perfectly described; unlike the written list of Excel clicks
and copy–pastes you think you carried out, mistakes in scripts can be foundand corrected later. You’ll never again need a file called “results_final_FINAL_
pleaseFINAL.xlsx” because you can keep your analysis and data wrangling inthe same script.”- Will Pearse, University of Minnesota, USA

Version control. Once the masterfile has been finalized, keeping track of ensuing
versions of this file can be challenging, especially if working with collaborators in
different locations. A version control strategy will help locate required versions, andclarify how versions differ from one another.
Version control best practice includes:•deciding how many and which versions to keep
•using a systematic file naming convention, using filenames that include the		 version number and status of the file, e.g. v1_draft, v2_internal, v3_final•record what changes took place to create the version in a separate file,		 e.g. a version table•mapping versions if they are stored in different locations•synchronizing versions across different locations•ensuring any cross-referenced information between files is also subject

		 to version control

22

Processing data

“I’m a statistician, so I mostly analyse other peoples’ data. That can be of
all sorts – field observations, genetic data, remote sensing data, almost
anything. Getting the data into the format I want can be difficult. Sometimes
collaborators can be too helpful, and do all sorts of strange transformations,which I then cannot reproduce. For other projects, I have to pull in data fromdifferent sources and getting everything to line up correctly can be a problem.
		 Be clear about the process of going from the raw data to the analyses– use code that can be saved, not a spreadsheet. That way you can check forerrors, and make changes more easily. Document what you have done (i.e. write
comments in the code) to explain what was done and why. It is important to beable to make the path, from raw data to what is used in the analysis, clear andreproducible.”- Bob O’Hara, Biodiversität und Klima Forschungszentrum, Germany

23

Processing data

R – a free software environment for statistical computing and graphics 3“Once a master dataset set has been finalized, all subsequent changes to thedata during analysis should be done in R (open source to which all collaborators
have access), where changes and analyses can easily be viewed and tracked by
all collaborators. In R, multiple datasets of different forms can easily be handled
as separate data frames in one single R file and conversions of data form caneasily be achieved.”- Kyle Demes, Simon Fraser University, Canada“Working with multiple forms of data is challenging but R can transform yourmanagement and analysis of data. My workflows rely on the integration of the
R programming language and scripting in the UNIX environment. Scripts andversion control of data manipulation and analysis are important and makecollaboration transparent and relatively trouble free.”- Andrew Beckerman, University of Sheffield, UK“All modification made to the raw data should be scripted and retained as arecord. Use tools like R for your data manipulation because by using R scripts
you can keep a record of any changes and manipulations that you do. If you
make those changes in Excel you won’t remember what you have done 1, 5, 10
years later.”- Yvonne Buckley, Trinity College Dublin, Ireland“R seems to have completely transformed the landscape for data management
and analysis. It has limitations as an interpreted language – for instance it isslow for some types of statistical models where a compiled language such as
C++ would be faster. However, I have switched over to using R , largely because
I can use the R code (with lots of embedded comments) to trace directly fromthe raw data files (which still need thorough and separate documentation!) tospecific results and graphs.”- Charles Canham, Cary Institute of Ecosystem Studies, USA

http://www.r-project.org/

3

24

25

26

Documenting data
Producing good documentation and metadata ensuresthat data can be understood and used in the longterm. Documentation describes the data, explains any
manipulations and provides contextual information – noroom should be left for others to misinterpret the data.
All documentation requirements should be identified
at the planning stages so you are prepared for it during
all stages of the data lifecycle, particularly during data
creation and processing. This will avoid having to performa rescue mission in situations where you have forgotten what has happened or whena collaborator has left without leaving any key documentation behind.

Document

Data documentation includes information at project and data levels and should
cover the following:
Project level•the project aim, objectives and hypotheses•personnel involved throughout the project, including who to contact with 				 questions•details of sponsors•data collection methods, including details of instrumentation

		 and environmental conditions during collection, copies of collection
		 instructions if applicable•standards used•data structure and organisation of files•software used to prepare and read the data•procedures used for data processing, including quality control and versioning		 and the dates these were carried out•known problems that may limit data use•instructions on how to cite the data•intellectual property rights and other licensing considerations
Data level•names, labels and descriptions for variables•detailed explanation of codes used27

Documenting data
•definitions of acronyms or specialist terminology•reasons for missing values•derived data created from the raw file, including the code or algorithm used to		 create them

If a software package such as R is used for processing data, much of the data level
documentation will be created and embedded during analysis.
Metadata help others discover data through searching and browsing online andenable machine-to-machine interoperability of data, which is necessary for datareuse. Metadata are created by using either a data centre’s deposit form, a metadata
editor, or a metadata creator tool, which can be searched for online. Metadata follow
a standard structure and come in three forms:•descriptive – fields such as title, author, abstract and keywords•administrative – rights and permissions and data on formatting•structural – explanations of e.g. tables within the data
“Curate your master data file while you create it and document it with metadata
at the same time. Creating metadata afterwards is way more painful and you
risk misinterpreting your own data, your memory is not usually as good as you
think!”- Ignasi Bartomeus, Swedish University of Agricultural Sciences, Sweden

28

Preserving data

Preserve

To protect data from loss and to make sure data are
securely stored, good data management should include
a strategy for backing up and storing data effectively. It
is recommended to keep three versions of your data: theoriginal, external/local and external/remote.
Institutional policies. These will be in place to regulate
methods of data preservation and should be adhered to.
Determining a backup and storage procedure will dependon the availability of resources at the institution.

Backup. When designing a backup strategy, thought should be given to the possiblemeans by which data loss could occur. These include:•hardware failure•software faults•virus infection or hacking•power failure•human error•hardware theft or misplacement•hardware damage (e.g. fire, flood)•backups – good backups being overwritten with backups from corrupt data
The likelihood of each of these will be different, and it may be environment specific(e.g. data being collected in the field may be subject to different risks than data being
used across a multi-institutional research team). An ideal backup strategy should
provide protection against all the risks, but it can be sensible to consider which
are the most likely to occur in any particular context and be aware of these when
designing your backup strategy.
Things to consider when drawing up a backup strategy include:•which files require backup•who is responsible for backups•the frequency of backup needed, this will be affected by how regularly files

		 are updated•whether full or incremental backups are needed – consider running a mix
		 of frequent incremental backups (capturing recent data changes) along with29

30

Preserving data		 periodic full backups (capturing a ‘snapshot’ of the state of all files)•backup procedures for each location where data are held, e.g. tablets,

		 home-based computers or remote drives•how to organize and label backup files
Data storage. Data storage, whether of the original or backed up data, needs tobe robust. This is true whether the data are stored on paper or electronically, but
electronic storage raises particular issues. Best practice for electronic storage of datais to do the following:•use high-quality storage systems (e.g. media, devices)•use non-proprietary formats for long-term software readability•migrate data files every two to five years to new storage – storage media such		 as CDs, DVDs and hard drives can degrade over time or become outdated
		 (e.g. floppy disks)•check stored data regularly to make sure nothing has been lost•use different forms of storage for the same data, this also acts as a method of		 backup, e.g. using remote storage, external hard drives and a network drive•label and organize stored files logically to make them easy to locate and access•think about encryption: sensitive data may be regarded as protected while on a		 password-protected computer, but when backed up onto a portable hard drive		 they may become accessible to anyone – backups may need to be encrypted or		 secured too“Lots of people solve data storage by buying space on a cloud service such
as Dropbox, but they might actually be breaching their contract with their
university if they do this, particularly if they store any kind of sensitive material
e.g. student-related, patient-related or any kind of social data. People should
always consult their institution in terms of implementing not only data storage
but all aspects of data management.”- Rob Freckleton, University of Sheffield, UK

31

Preserving data
Data can be stored and backed up on:•Network drives which are managed by IT staff and are regularly backed up.

		 They ensure secure storage and prohibit unauthorized access to files.

•Personal devices such as laptops and tablets are convenient for short-term,
		 temporary storage but should not be used for storing master files. They are at		 high risk of being lost, stolen or damaged.
•External devices such as hard drives, USB sticks, CDs and DVDs are often
		 convenient because of their cost and portability. However, they do not guarantee		 long-term preservation and can also be lost, stolen or damaged. High-quality
		 external devices from reputable manufacturers should be used.
•Remote or online services such as Dropbox, Mozy and A-Drive use cloud		 technology to allow users to synchronize files across different computers. They		 provide some storage space for free but any extra space or functions will have to		 be bought.
•Paper! If data files are not too big, do not overlook the idea of printing out a paper
		 copy of important ones data files as a complement to electronic storage. It may
		 not be convenient, but ink on paper has proven longevity and system
		 independence (as long as you can remember where you put it)!
“I recently had a request for the raw data from a 25-year-old experiment andwas able to find the floppy disk but then spent a day trying to figure out how
to translate from an old software format into something readable. The request
was for data that didn’t make it into the published paper but that we had in fact
collected. It felt good to be able to offer it up for use in someone else’s work.”- Charles Canham, Cary Institute of Ecosystem Studies, USA

32

Sharing data
Research data can be shared in many ways andeach method of sharing will have advantages anddisadvantages. Ways to share data include:

Share•using a disciplinary data centre such as Dryad		 or GenBank•depositing data in your research funder’s data centre•depositing data in university repositories•making data available online via open notebooks or		 project websites•using virtual research environments such as 				 SharePoint and Sakai

The BES data archiving policy, which mandates that all data used to support theresults in papers published in its journals be archived in a suitable repository thatprovides ‘comparable access and guarantee of preservation’4 , encourages authors topick a repository best suited to their type of data and is most useful to the community
most likely to access their data.
Data repositories. Archiving your data in a repository is a reliable method of sharing
data. Data submitted to repositories will have to conform to submission guidelines,
which may restrict which data you share via the repository. However, the advantagesof sharing data through these centres include:•assurance for others that the data meet quality standards•guaranteed long-term preservation•data are secure and access can be controlled•data are regularly backed up•chances of others discovering the data are improved•citation methods are specified•secondary usage of the data is monitored
Longitudinal datasets that span many years are important in ecology and evolution.
Journals mandating that authors archive their data only guarantees the preservationof the data used in a particular paper, but researchers should be aware of the value
http://www.britishecologicalsociety.org/publications/journal-policies/#data accessed 10 October 2014

433

Sharing dataof archiving and sharing large datasets to drive discovery. Sharing large datasets
has not been common practice in fields such as ecology. However, as trust in ethicalguidelines and the communities expectations with regards to accessing andcorrectly citing others’ data grow, progress can be made towards a more open accessdata future.

Reusing data
All aspects of data management lead up to data discovery
and reuse by others. Intellectual property rights, licenses
and permissions, which concern reuse of data, should be
explained in the data documentation and/or metadata.
At this stage of the lifecycle it is important to state yourexpectations for the reuse of your data, e.g. terms of
acknowledgement, citation and coauthorship. Likewise,
it becomes the responsibility of others to reuse data
effectively, credit the collectors of the original data, cite
the original data and manage any subsequent research to the same effect.
		 When requesting to use someone else’s data it is important to clearly state
the purpose of the request, including the idea you will be addressing and yourexpectations for coauthorship or acknowledgement. Coauthorship is a complex
issue and should be discussed with any collaborators at the outset of a project.
		 Increasing openness to data and ensuring long-term preservation of data
fosters collaboration and transparency, furthering research that aims to answer the
big questions in ecology and evolution. By implementing good data management
practices, researchers can ensure that high-quality data are preserved for the
research community and will play a role in advancing science for future generations.

Reuse

34

Sources and Further Reading
All online sources were accessed 13 October 2014.
BES, Data Archiving Policy
http://www.britishecologicalsociety.org/publications/journal-policies/#data
DataONE Best Practices Primer https://www.dataone.org/sites/all/documents/DataONE_BP_Primer_020212.pdf
DCC, How to Develop a Data Management and Sharing Plan
http://www.dcc.ac.uk/resources/how-guides/develop-data-plan
EPSRC, Clarifications of EPSRC expectations on research data management
http://www.epsrc.ac.uk/files/aboutus/standards/clarificationsofexpectationsresearchdatamanagement/
MANTRA, Research Management Training http://datalib.edina.ac.uk/mantra/
Norman, H. ‘Mandating data archiving: experiences from the frontline’ Learned Publishing 27 S35-S38
http://www.ingentaconnect.com/content/alpsp/lp/2014/00000027/00000005/art00007
RCUK, Common Principles on Data Policy http://www.rcuk.ac.uk/research/datapolicy/
UK Data Archive, Managing and Sharing Data (2011) http://www.data-archive.ac.uk/media/2894/managingsharing.pdf

Acknowledgements
This booklet would not have been possible without contributions from: Peter Alpert, Andrea Baier, Liz Baker, Ignasi
Bartomeus, Andrew Beckerman, Caroline Brophy, Yvonne Buckley, Rosalie Burdon, Charles Canham, Tim Coulson, Kyle
Demes, Stéphane Dray, Andry Dyer, Rob Freckleton, David Gibson, Erika Newton, Bob O’Hara, Catherine Hill, Will Pearse,
Nathalie Pettorelli, Frida Piper, Cassandra Raby, Roberto Salguero-Gomez, Kulbhushansingh Suryawanshi, Phil Warren
and Ken Wilson.

Image credits
p2: Norwegian University of Life Sciences / Snow Leopard Foundation Pakistan
p3: Danielle Green
p6: Markku Larjavaara
p8: David Bird
p9: Kara-Anne Wardp10: Ute Bradter
p13: Benjamin Blonder
p15: © Jeremy Holloway
p16: Image provided by Koen and Walpole using Circuitscape
p18 Tomáš Václavík
p20: © Kevin Weng. The CC-BY licence does not apply to this image.
p23: Oliver Hyman
p25 Urs Kabitzer
p26: Hannah Grist
p28: Christian Schoeb
p30: Adam Seward35Index
Backup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p29
Big data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p8
Coauthorship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p34
Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p17
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p3
Data lifecycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p4
Data management checklist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p14
Data management plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p11
Digitization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p19
Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p27
File formats. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p21
Filenames and folders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p21
Metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p28
Open data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p7
Open notebooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p7
Quality assurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p21
R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p24
Repositories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p33
Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p31
Version control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p22
Virtual research environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p7

36

JOIN OUR COMMUNITY
We are an inclusive, member-centred Society and provide a diverse
range of initiatives for the global ecological community. Membership is
open to anyone, anywhere. Speak to us about how you can get involved.
hello@britishecologicalsociety.org@britishecolsoc+44(0)20 7685 2500

MEMBERSHIP FEES:

PUBLICATIONS:• Ordinary from £42 (c. €48, c. $54)

• Free access to all BES journal content on
the Wiley Online Library, including open
access journal Ecology and Evolution• Student from £21 (c. €24, c. $27)
• Concessions from £21 (c. €24, c. $27)

We offer one year FREE
membership to all students.

GRANTS:• Large Research grants – up to £20,000(c. €23,000, $25,000). Members only

• 25% discount on open access fees whenpublishing as first or corresponding
author in BES journals• 10% discount when publishing in
Ecology and Evolution• Member only discounts from leading
scientific publishers• Small Research grants – up to c. £5,000(c. €5,700, $6,500). Members only

PERSONAL DEVELOPMENT:• Training and Travel – up to £1,000(€1,150, $1,300). Members only

• Become a mentor or mentee in our
Mentoring Scheme• Outreach – up to c. £2,000(c. €2,300, $2,500)• Network with specific communities andjoin our Special Interest Groups• Ecologists in Africa – up to c. £8,000(c. €9,100, $10,200)• Access our career resources and attend
our career events• Join our career-developing webinars

EVENTS:
We offer discounted registration for ourmembers on all our events. Our Annual
Meeting, symposia and Special Interest
Group events are great ways to showcase
your research and network with others fromour community; you can present a talk or
poster, submit a thematic topic or workshop
or come along and listen to cutting edge
research in a friendly, informal setting.

• Respond to a policy consultation• Develop science communication skills
through our public engagement events

BULLETIN:
Our member magazine provides acomprehensive round-up of ecologicalnews, member stories, upcoming eventsand interesting articles that will keep
you up to date with our community. We
encourage our members to submit articles
to showcase our diversity.

BES PUBLICATIONS

For more information about all of our publications please visit:
british ecological society.org/publications

Journal of Ecology

Functional Ecology

journalofecology.org@jecologyfunctionalecology.org@funecology
High-impact papers thatenable a mechanistic
understanding of ecologicalpattern and process fromthe organismic to theecosystem scale.

High-impact, broadreaching articles on all
aspects of plant ecology(including algae), in bothaquatic and terrestrial
ecosystems.

Journal of Applied Ecology

Journal of Animal Ecology

journalofappliedecology.org@jappliedecol

journalofanimalecology.org@animalecology
Publishing the best
animal ecology researchthat develops, tests andadvances broad ecologicalprinciples.

Novel, high-impact papers
on the interface between
ecological science and the
management of biologicalresources.

Methods in Ecology and Evolution

Proud topartner withmethodsinecologyandevolution.org@methodsecolevol

ecolevol.org@wileyopenaccess

Promotes the developmentof new methods inecology and evolution,and facilitates theirdissemination and uptake
by the research community.

Guides to Better Science
Promoting research
excellence across a range of
topics including peer review,
data management, andgetting published. These
free guides contain plenty ofpractical tips for researchers
all over the world.

We partner with Wiley on
this open access journal for
quality research in all areasof ecology, evolution andconservation science.

Ecological Reviews
Books at the cutting edge ofmodern ecology, providing
a forum for current topics
that are likely to be oflong-term importance to theprogress of the field.