Data Management: The Current Landscape

Data Management The Current Landscape Carly Strasser California Digital Library 2012 IASSIST Conference University of California Curation Center June 2012

From Flickr by DW0825 From Flickr by Flickmor From Flickr by deltaMike Digital data www.woodrow.org C. Strasser Courtesey of WHOI From Flickr by US Army Environmental Command

Digital data + Complex analyses

Data Models Maximum Likelihood estimation Matrix Models Images Tables Paper

UGLY TRUTH Most Earth | Environmental | Ecological scientists… 5shortessays.blogspot.com are not taught data management don’t know what metadata are can’t name data centers or repositories don’t share data publicly or store it in an archive aren’t convinced they should share data

Where data end up From Flickr by diylibrarian www blog.order2disorder.com From Flickr by csessums Data Metadata From Flickr by csessums Recreated from Klump et al. 2006

Who cares? From Flickr by Redden-‐McAllister From Flickr by AJC1 www.rba.gov.au

Where data end up From Flickr by diylibrarian www Data wwwMetadata From Flickr by torkildr Recreated from Klump et al. 2006

Data Reuse Data Sharing Data Management

Trends in Data Archiving Journal publishers Joint Data Archiving Agreement Data Papers etc. Ecological Archives, Beyond the PDF Funders Data management requirements

What is a data management plan? A document that describes what you will do with your data during your research and after you complete your research

Why should a scientist prepare a DMP? Saves time Increases eﬃciency Easier to use data Others can understand & use data Credit for data products Funders require it

NSF DMP Requirements From Grant Proposal Guidelines: DMP supplement may include: 1.  the types of data, samples, physical collections, software, curriculum materials, and other materials to be produced in the course of the project 2.  the standards to be used for data and metadata format and content (where existing standards are absent or deemed inadequate, this should be documented along with any proposed solutions or remedies) 3.  policies for access and sharing including provisions for appropriate protection of privacy, conﬁdentiality, security, intellectual property, or other rights or requirements 4.  policies and provisions for re-‐use, re-‐distribution, and the production of derivatives 5.  plans for archiving data, samples, and other research products, and for preservation of access to them

NSF’s Vision* DMPs and their evaluation will grow & change over time (similar to broader impacts) Peer review will determine next steps Community-‐driven guidelines –  Different disciplines have different definitions of acceptable data sharing –  Flexibility at the directorate and division levels –  Tailor implementation of DMP requirement Evaluation will vary with directorate, division, & program officer *Unofficially Help from Jennifer Schopf, NSF

dmp.cdlib.org dmponline.dcc.ac.uk

now called DataUp •  Open source add-‐in & web application •  Facilitate data management, sharing, archiving for scientists •  Focus on atmospheric, ecological, hydrological, and oceanographic data •  Collecting requirements for add-‐in from scientists, data centers, libraries Funders: Gordon and Betty Moore Foundation, Microsoft Research

www.dataone.org •  Data Education Tutorials •  Database of best practices & software tools •  Primer on data management •  Investigator Toolkit now called DataUp

Data Management Best Practices Carly Strasser California Digital Library 2012 IASSIST Conference University of California Curation Center June 2012

Best Practices for Data Management 1.  Planning 2.  Data collection & organization 3.  Quality control & assurance 4.  Metadata 5.  Workﬂows 6.  Data stewardship & reuse

2. Data collection & organization Create unique identiﬁers •  Decide on naming scheme early •  Create a key •  Diﬀerent for each sample From Flickr by zebbie From Flickr by sjbresnahan

2. Data collection & organization Standardize •  Consistent within columns – only numbers, dates, or text •  Consistent names, codes, formats Modiﬁed from K. Vanderbilt From Pink Floyd, The Wall themurkyfringe.com

2. Data collection & organization Use descriptive ﬁle names PhDcomics.com

2. Data collection & organization Use descriptive file names * •  Unique •  Reflect contents Bad: Mydata.xls Better: Eaffinis_nanaimo_2010_counts.xls 2001_data.csv best version.txt Study Year organism Site name What was measured *Not for everyone From R Cook, ESA Best Practices Workshop 2010

2. Data collection & organization Preserve information R script for processing & analysis •  Keep raw data raw •  Use scripts to process data & save them with data Raw data as .csv

3. Quality control and quality assurance Before data collection •  Deﬁne & enforce standards •  Assign responsibility for data quality From Flickr by StacieBee

3. Quality control and quality assurance During data collection/entry •  Minimize manual entry •  Use double entry •  Use text-‐to-‐speech program to read data back •  Use a database •  Document changes From Flickr by schock

3. Quality control and quality assurance After data entry •  Check for missing, impossible, anomalous values •  Perform statistical summaries •  Look for outliers •  Normal probability plots •  Regression •  Scatter plots 60 50 40 •  Maps 30 20 10 0 0 10 20 30 40

4. Metadata Metadata = Data reporting WHO created the data? WHAT is the content of the data set? WHEN was it created? From Flickr by //ichael Patric|{ WHERE was it collected? HOW was it developed? WHY was it developed?

•  Scientific context 4. Metadata •  Scientific reason why the data were collected •  What data were collected •  Digital context •  What instruments (including model & •  Name of the data set serial number) were used •  The name(s) of the data file(s) in the data •  Environmental conditions during collection set •  Where collected & spatial resolution When •  Date the data set was last modified collected & temporal resolution •  Example data file records for each data •  Standards or calibrations used type file •  Information about parameters •  Pertinent companion files •  How each was measured or produced •  List of related or ancillary data sets •  Units of measure •  Software (including version number) •  Format used in the data set used to prepare/read the data set •  Precision & accuracy if known •  Data processing that was performed •  Information about data •  Personnel & stakeholders •  Definitions of codes used •  Who collected •  Quality assurance & control measures •  Who to contact with questions •  Known problems that limit data use (e.g. •  Funders uncertainty, sampling problems) •  How to cite the data set

4. Metadata What is metadata? Select the appropriate metadata standard •  Provides structure to describe data Common terms | definitions | language | structure •  Lots of different standards EML , FGDC, ISO19115, DarwinCore,… •  Tools for creating metadata files Morpho (EML), Metavist (FGDC), NOAA MERMaid (CSGDM)

5. Workflows Workflow: how you get from the raw data to the final products of your research Simple workflows: flow charts Temperature data Data import into R Data in R Salinity format data Quality control & “Clean” T data cleaning & S data Analysis: mean, SD Summary statistics Graph production

5. Workflows Workflow: how you get from the raw data to the final products of your research Simple workflows: commented scripts •  R, SAS, MATLAB •  Well-‐documented code is… Easier to review Easier to share % # $ Easier to repeat analysis &

5. Workﬂows Fancy Schmancy workﬂows: Kepler Resulting output https://kepler-‐project.org

5. Workflows Workflows enable From Flickr by merlinprincesse Reproducibility can someone independently validate findings? Transparency others can understand how you arrived at your results Executability others can re-‐run or re-‐use your analysis

6. Data stewardship & reuse From Flickr by greensambaman The 20-‐Year Rule The metadata accompanying a data set should be written for a user 20 years into the future RULE Document Document Document Document Document Document Document Document Document Document Document Document (National Research Council 1991)

6. Data stewardship & reuse Use stable formats csv, txt, tiﬀ Create back-‐up copies original, near, far Periodically test back-‐ups Modified from R. Cook

6. Data stewardship & reuse Store data in a repository Institutional archive Discipline/specialty archive From Flickr by torkildr

6. Data stewardship & reuse Data Citation Allows readers to find data products Get credit for data and publications Promotes reproducibility Better measure of research impact Example: Sidlauskas, B. 2007. Data from: Testing for unequal rates of morphological diversification in the absence of a detailed phylogeny: a case study from characiform fishes. Dryad Digital Repository. doi:10.5061/dryad.20 Learn more at www.datacite.org Modified from R. Cook

Check out the blog dcxl.cdlib.org or my website www.carlystrasser.net Email me carlystrasser@gmail.com Tweet me @carlystrasser | @dcxlCDL DCXL on FB DCXLatCDL

Data Management: The Current Landscape

by Carly Strasser

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