uc santa cruz: data management for scientists

Data Management for Scientists

Carly Strasser, PhD California Digital Library, UC Office of the President

[email protected] www.carlystrasser.net

Reduce your workload Reuse your ideas Recycle your data

From Flickr by Mark McLaughlin

UC Santa Cruz February 2012

Roadmap

4.  Toolbox

1.  Background

2.  Data management landscape 3.  How to improve

NSF funded DataNet Project Office of Cyberinfrastructure

B

C A

Pre DataONE . DataONE

NSF funded DataNet Project Office of Cyberinfrastructure

Community Engagement &

Outreach

Courtesy of DataONE

Cyberinfrastructure

From Flickr by wetwebwork

Is data management being taught? Do attitudes about

sharing differ among disciplines?

What role can libraries play in data education?

How can we promote storing data in repositories?

What barriers to sharing can we eliminate?

Why don’t people share data?

Roadmap

4.  Toolbox

1.  Background

2.  Data management landscape 3.  How to improve

Digital data From

Flickr by Flickm

or

From

Flickr by US Arm

y En

vironm

ental C

omman

d

From

Flickr by DW08

25

C. Strasser

Courtesey of W

HOI

www.woodrow.org

From

Flickr by deltaMike

Digital data +

Complex analyses

Data

Maximum Likelihood estimation

Matrix Models

Models

Images Tables Paper

Data

Maximum Likelihood estimation

Matrix Models

Images Tables Paper

Models

UGLY TRUTH

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

5shortessays.blogspot.com

Many Earth | Environmental | Ecological scientists…

Data Hangover

From Flickr by SteveMcN

What happened?

Where data end up

Data

Metadata

Recreated from Klump et al. 2006

blog.order2disorder.com

From Flickr by csessum

s From

Flickr by csessums

From Flickr by diylibrarian

www �

Who cares?

www.rba.gov.au

From Flickr by Redden-‐McAllister

From Flickr by AJC1

Data

Metadata

Recreated from Klump et al. 2006

www �

Where data end up

From Flickr by torkildr

From Flickr by diylibrarian

www �

Data Management

Data Reuse

Data Sharing

Trends in Data Archiving

Journal publishers Joint Data Archiving Agreement

Trends in Data Archiving

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

Roadmap

4.  Toolbox

1.  Background

2.  Data management landscape 3.  Best practices

Best Practices for Data Management

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

1.  Planning 2.  Data collection & organization 3.  Quality control & assurance 4. Metadata 5. Workflows 6. Data stewardship & reuse 7.  Planning


C:\Documents and Settings\hampton\My Documents\NCEAS Distributed Graduate Seminars\[Wash Cres Lake Dec 15 Dont_Use.xls]Sheet1Stable Isotope Data Sheet

Wash Cresc Lake Peter's lab Don't use - old dataAlgal Washed RocksDec. 16Tray 004

SD for delta 13C = 0.07 SD for delta 15N = 0.15

Position SampleID Weight (mg) %C delta 13C delta 13C_ca %N delta 15N delta 15N_ca Spec. No.A1 ref 0.98 38.27 -25.05 -24.59 1.96 4.12 3.47 25354A2 ref 0.98 39.78 -25.00 -24.54 2.03 4.01 3.36 25356A3 ref 0.98 40.37 -24.99 -24.53 2.04 4.09 3.44 25358A4 ref 1.01 42.23 -25.06 -24.60 2.17 4.20 3.55 25360 Shore Avg ConA5 ALG01 3.05 1.88 -24.34 -23.88 0.17 -1.65 -2.30 25362 c -1.26 -27.22A6 Lk Outlet Alg 3.06 31.55 -30.17 -29.71 0.92 0.87 0.22 25364 1.26 0.32A7 ALG03 2.91 6.85 -21.11 -20.65 0.48 -0.97 -1.62 25366 cA8 ALG05 2.91 35.56 -28.05 -27.59 2.30 0.59 -0.06 25368A9 ALG07 3.04 33.49 -29.56 -29.10 1.68 0.79 0.14 25370A10 ALG06 2.95 41.17 -27.32 -26.86 1.97 2.71 2.06 25372B1 ALG04 3.01 43.74 -27.50 -27.04 1.36 0.99 0.34 25374 cB2 ALG02 3 4.51 -22.68 -22.22 0.34 4.31 3.66 25376B3 ALG01 2.99 1.59 -24.58 -24.12 0.15 -1.69 -2.34 25378 cB4 ALG03 2.92 4.37 -21.06 -20.60 0.34 -1.52 -2.17 25380 cB5 ALG07 2.9 33.58 -29.44 -28.98 1.74 0.62 -0.03 25382B6 ref 1.01 44.94 -25.00 -24.54 2.59 3.96 3.31 25384B7 ref 0.99 42.28 -24.87 -24.41 2.37 4.33 3.68 25386B8 Lk Outlet Alg 3.04 31.43 -29.69 -29.23 1.07 0.95 0.30 25388B9 ALG06 3.09 35.57 -27.26 -26.80 1.96 2.79 2.14 25390B10 ALG02 3.05 5.52 -22.31 -21.85 0.45 4.72 4.07 25392C1 ALG04 2.98 37.90 -27.42 -26.96 1.36 1.21 0.56 25394 cC2 ALG05 3.04 31.74 -27.93 -27.47 2.40 0.73 0.08 25396C3 ref 0.99 38.46 -25.09 -24.63 2.40 4.37 3.72 25398

23.78 1.17

Reference statistics:

Sampling Site / Identifier:Sample Type:

Date:Tray ID and Sequence:

From Stephanie Hampton (2010) ESA Workshop on Best Practices

2 tables Random notes




Position SampleID Weight (mg) %C delta 13C delta 13C_ca %N delta 15N delta 15N_ca Spec. No.A1 ref 0.98 38.27 -25.05 -24.59 1.96 4.12 3.47 25354A2 ref 0.98 39.78 -25.00 -24.54 2.03 4.01 3.36 25356A3 ref 0.98 40.37 -24.99 -24.53 2.04 4.09 3.44 25358A4 ref 1.01 42.23 -25.06 -24.60 2.17 4.20 3.55 25360 Shore Avg ConA5 ALG01 3.05 1.88 -24.34 -23.88 0.17 -1.65 -2.30 25362 c -1.26 -27.22A6 Lk Outlet Alg 3.06 31.55 -30.17 -29.71 0.92 0.87 0.22 25364 1.26 0.32A7 ALG03 2.91 6.85 -21.11 -20.65 0.48 -0.97 -1.62 25366 cA8 ALG05 2.91 35.56 -28.05 -27.59 2.30 0.59 -0.06 25368A9 ALG07 3.04 33.49 -29.56 -29.10 1.68 0.79 0.14 25370A10 ALG06 2.95 41.17 -27.32 -26.86 1.97 2.71 2.06 25372B1 ALG04 3.01 43.74 -27.50 -27.04 1.36 0.99 0.34 25374 cB2 ALG02 3 4.51 -22.68 -22.22 0.34 4.31 3.66 25376B3 ALG01 2.99 1.59 -24.58 -24.12 0.15 -1.69 -2.34 25378 cB4 ALG03 2.92 4.37 -21.06 -20.60 0.34 -1.52 -2.17 25380 cB5 ALG07 2.9 33.58 -29.44 -28.98 1.74 0.62 -0.03 25382B6 ref 1.01 44.94 -25.00 -24.54 2.59 3.96 3.31 25384B7 ref 0.99 42.28 -24.87 -24.41 2.37 4.33 3.68 25386B8 Lk Outlet Alg 3.04 31.43 -29.69 -29.23 1.07 0.95 0.30 25388B9 ALG06 3.09 35.57 -27.26 -26.80 1.96 2.79 2.14 25390B10 ALG02 3.05 5.52 -22.31 -21.85 0.45 4.72 4.07 25392C1 ALG04 2.98 37.90 -27.42 -26.96 1.36 1.21 0.56 25394 cC2 ALG05 3.04 31.74 -27.93 -27.47 2.40 0.73 0.08 25396C3 ref 0.99 38.46 -25.09 -24.63 2.40 4.37 3.72 25398

23.78 1.17




From Stephanie Hampton (2010) ESA Workshop on Best Practices

Wash Cres Lake Dec 15 Dont_Use.xls




Position SampleID Weight (mg) %C delta 13C delta 13C_ca %N delta 15N delta 15N_ca Spec. No.A1 ref 0.98 38.27 -25.05 -24.59 1.96 4.12 3.47 25354A2 ref 0.98 39.78 -25.00 -24.54 2.03 4.01 3.36 25356A3 ref 0.98 40.37 -24.99 -24.53 2.04 4.09 3.44 25358A4 ref 1.01 42.23 -25.06 -24.60 2.17 4.20 3.55 25360 Shore Avg ConA5 ALG01 3.05 1.88 -24.34 -23.88 0.17 -1.65 -2.30 25362 c -1.26 -27.22A6 Lk Outlet Alg 3.06 31.55 -30.17 -29.71 0.92 0.87 0.22 25364 1.26 0.32A7 ALG03 2.91 6.85 -21.11 -20.65 0.48 -0.97 -1.62 25366 cA8 ALG05 2.91 35.56 -28.05 -27.59 2.30 0.59 -0.06 25368A9 ALG07 3.04 33.49 -29.56 -29.10 1.68 0.79 0.14 25370A10 ALG06 2.95 41.17 -27.32 -26.86 1.97 2.71 2.06 25372B1 ALG04 3.01 43.74 -27.50 -27.04 1.36 0.99 0.34 25374 c SUMMARY OUTPUTB2 ALG02 3 4.51 -22.68 -22.22 0.34 4.31 3.66 25376B3 ALG01 2.99 1.59 -24.58 -24.12 0.15 -1.69 -2.34 25378 c Regression StatisticsB4 ALG03 2.92 4.37 -21.06 -20.60 0.34 -1.52 -2.17 25380 c Multiple R 0.283158B5 ALG07 2.9 33.58 -29.44 -28.98 1.74 0.62 -0.03 25382 R Square 0.080178B6 ref 1.01 44.94 -25.00 -24.54 2.59 3.96 3.31 25384 Adjusted R Square-0.022024B7 ref 0.99 42.28 -24.87 -24.41 2.37 4.33 3.68 25386 Standard Error1.906378B8 Lk Outlet Alg 3.04 31.43 -29.69 -29.23 1.07 0.95 0.30 25388 Observations 11B9 ALG06 3.09 35.57 -27.26 -26.80 1.96 2.79 2.14 25390B10 ALG02 3.05 5.52 -22.31 -21.85 0.45 4.72 4.07 25392 ANOVAC1 ALG04 2.98 37.90 -27.42 -26.96 1.36 1.21 0.56 25394 c df SS MS F Significance FC2 ALG05 3.04 31.74 -27.93 -27.47 2.40 0.73 0.08 25396 Regression 1 2.851116 2.851116 0.784507 0.398813C3 ref 0.99 38.46 -25.09 -24.63 2.40 4.37 3.72 25398 Residual 9 32.7085 3.634278

23.78 1.17 Total 10 35.55962

CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%Intercept -4.297428 4.671099 -0.920003 0.381568 -14.8642 6.269341 -14.8642 6.269341X Variable 1-0.158022 0.17841 -0.885724 0.398813 -0.561612 0.245569 -0.561612 0.245569




Random stats output

27




Position SampleID Weight (mg) %C delta 13C delta 13C_ca %N delta 15N delta 15N_ca Spec. No.A1 ref 0.98 38.27 -25.05 -24.59 1.96 4.12 3.47 25354A2 ref 0.98 39.78 -25.00 -24.54 2.03 4.01 3.36 25356A3 ref 0.98 40.37 -24.99 -24.53 2.04 4.09 3.44 25358A4 ref 1.01 42.23 -25.06 -24.60 2.17 4.20 3.55 25360 Shore Avg ConA5 ALG01 3.05 1.88 -24.34 -23.88 0.17 -1.65 -2.30 25362 c -1.26 -27.22A6 Lk Outlet Alg 3.06 31.55 -30.17 -29.71 0.92 0.87 0.22 25364 1.26 0.32A7 ALG03 2.91 6.85 -21.11 -20.65 0.48 -0.97 -1.62 25366 cA8 ALG05 2.91 35.56 -28.05 -27.59 2.30 0.59 -0.06 25368A9 ALG07 3.04 33.49 -29.56 -29.10 1.68 0.79 0.14 25370A10 ALG06 2.95 41.17 -27.32 -26.86 1.97 2.71 2.06 25372B1 ALG04 3.01 43.74 -27.50 -27.04 1.36 0.99 0.34 25374 c SUMMARY OUTPUTB2 ALG02 3 4.51 -22.68 -22.22 0.34 4.31 3.66 25376B3 ALG01 2.99 1.59 -24.58 -24.12 0.15 -1.69 -2.34 25378 c Regression StatisticsB4 ALG03 2.92 4.37 -21.06 -20.60 0.34 -1.52 -2.17 25380 c Multiple R 0.283158B5 ALG07 2.9 33.58 -29.44 -28.98 1.74 0.62 -0.03 25382 R Square 0.080178B6 ref 1.01 44.94 -25.00 -24.54 2.59 3.96 3.31 25384 Adjusted R Square-0.022024B7 ref 0.99 42.28 -24.87 -24.41 2.37 4.33 3.68 25386 Standard Error1.906378B8 Lk Outlet Alg 3.04 31.43 -29.69 -29.23 1.07 0.95 0.30 25388 Observations 11B9 ALG06 3.09 35.57 -27.26 -26.80 1.96 2.79 2.14 25390B10 ALG02 3.05 5.52 -22.31 -21.85 0.45 4.72 4.07 25392 ANOVAC1 ALG04 2.98 37.90 -27.42 -26.96 1.36 1.21 0.56 25394 c df SS MS F Significance FC2 ALG05 3.04 31.74 -27.93 -27.47 2.40 0.73 0.08 25396 Regression 1 2.851116 2.851116 0.784507 0.398813C3 ref 0.99 38.46 -25.09 -24.63 2.40 4.37 3.72 25398 Residual 9 32.7085 3.634278

23.78 1.17 Total 10 35.55962

CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%Intercept -4.297428 4.671099 -0.920003 0.381568 -14.8642 6.269341 -14.8642 6.269341X Variable 1-0.158022 0.17841 -0.885724 0.398813 -0.561612 0.245569 -0.561612 0.245569




SampleID ALG03 ALG05 ALG07 ALG06 ALG04 ALG02 ALG01 ALG03 ALG07

Weight (mg) 2.91 2.91 3.04 2.95 3.01 3 2.99 2.92 2.9

%C 6.85 35.56 33.49 41.17 43.74 4.51 1.59 4.37 33.58delta 13C -21.11 -28.05 -29.56 -27.32 -27.50 -22.68 -24.58 -21.06 -29.44

delta 13C_ca -20.65 -27.59 -29.10 -26.86 -27.04 -22.22 -24.12 -20.60 -28.98

%N 0.48 2.30 1.68 1.97 1.36 0.34 0.15 0.34 1.74delta 15N -0.97 0.59 0.79 2.71 0.99 4.31 -1.69 -1.52 0.62

delta 15N_ca -1.62 -0.06 0.14 2.06 0.34 3.66 -2.34 -2.17 -0.03

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

4.00

-35.00 -30.00 -25.00 -20.00 -15.00 -10.00 -5.00 0.00

Series1

Create unique identifiers •  Decide on naming scheme early •  Create a key •  Different for each sample

2. Data collection & organization

From Flickr by sjbresnahan From Flickr by zebbie

Standardize •  Consistent within columns – only numbers, dates, or text

•  Consistent names, codes, formats

Modified from K. Vanderbilt From Pink Floyd, The Wall themurkyfringe.com


Google Docs Forms

Standardize •  Reduce possibility of manual error by constraining entry choices

Modified from K. Vanderbilt


Excel lists Data

validataion

Identify missing data •  Numeric fields: distinct value (e.g. 9999) •  Text fields: NULL or NA •  Use data flags in a separate column to qualify empty cells

M1 = missing; no sample collected

E1 = estimated from grab sample



Create parameter table Create a site table

From doi:10.3334/ORNLDAAC/777

From doi:10.3334/ORNLDAAC/777

From R Cook, ESA Best Practices Workshop 2010

Quick on the draw Clickety-‐click and you’re ready to fire

Always there in time Everyone has Excel

Smarter than he lets on Stats, Pivot tables, VB scripts

Cleans up real pretty Graphics, fonts, colors, borders

From Mark Schildhauer


SPREADSHEETS: THE GOOD



Shoot first ask later Click&fire Click&fire Click&fire

No scruples Delete row, click&fire, ctrl-‐x/ctrl-‐c, click&fire, re-‐sort, save

Talks a good story but not much education Stats

SPREADSHEETS: THE BAD

Ill-‐mannered Takes data prisoner; conflates raw and summary data

Gaudy Use of visual cues as metadata: color, font, border

Shifty Cross-‐linking worksheets sets up “invisible” dependencies

Shiftless No provenance

The more complicated your spreadsheet, the uglier it gets for use with other software From Mark Schildhauer


SPREADSHEETS: THE UGLY

2. Data collection & organization All of the things that make Excel great for data are bad for archiving!

1.  Create archive-‐ready raw data 2.  Put it somewhere special 3.  Have your fun with fancy Excel techniques 4.  Keep archiving in mind

A relational database is A set of tables Relationships among the tables A language to specify & query the tables



What about databases?

*siteID site_name latitude longitude description

Sample sites

* Denotes the primary key

*speciesID species_name common_name family order

Species *sampleID siteID sample_date speciesID height flowering flag comments

samples

*sampleID siteID sample_date speciesID height flowering flag comments

Samples



Databases often enforce good practice Must define

Tables Attributes Relationships (constraints)

Databases provide:

Scalability: millions+ records Features for sub-‐setting, querying, sorting Scripted language: SQL Reduced redundancy & potential data entry errors



A B C

1 2 3

4 5 6

7 8 9

D E

10 11

12 13

14 15

16 17

Spreadsheets •  Good for simple, self-‐contained

charts, graphs, calculations •  Handy for collecting raw data •  Flexible cell content type But… •  Hard to subset or sort •  Lack “record” integrity: can sort a

column independently of all others •  Harder to maintain as complexity

and size of data grows

Databases •  Works well with lots of data •  Easy to query and subset data •  Data fields are constrainted •  Columns cannot be sorted

independently of each other •  Normalization reduces data entry

and potential for error But… •  More to learn •  Harder to use



You should invest time in learning databases if your data sets are large or complex

Consider investing time in learning databases if your data are small and humble you ever intend to share your data you are < 30 years old



Use descriptive file names

PhDcomics.com


Use descriptive file names •  Unique •  Reflect contents

From R Cook, ESA Best Practices Workshop 2010

Bad: Mydata.xls 2001_data.csv best version.txt

Better: Eaffinis_nanaimo_2010_counts.xls

Site name

Year What was measured

Study organism


*Not for everyone

*

Organize files logically

Biodiversity

Lake

Experiments

Field work

Grassland

Biodiv_H20_heatExp_2005to2008.csv Biodiv_H20_predatorExp_2001to2003.csv … Biodiv_H20_PlanktonCount_2001toActive.csv Biodiv_H20_ChlAprofiles_2003.csv …

From S. Hampton


Preserve information •  Keep raw data raw

•  Use scripts to process data & save them with data

Raw data as .csv

R script for processing & analysis


Before data collection •  Define & enforce standards •  Assign responsibility for data quality

3. Quality control and quality assurance

From

Flickr by StacieBe

e

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 scho

ck

After data entry •  Check for missing, impossible,

anomalous values •  Perform statistical summaries •  Look for outliers

•  Normal probability plots •  Regression •  Scatter plots •  Maps


0

10

20

30

40

50

60

0 10 20 30 40

4. Metadata basics Why are you promoting Excel?

What is metadata?

4. Metadata basics

Metadata = Data reporting

WHO created the data?

WHAT is the content of the data set?

WHEN was it created?

WHERE was it collected?

HOW was it developed?

WHY was it developed?

•  Digital context

•  Name of the data set

•  The name(s) of the data file(s) in the data set

•  Date the data set was last modified

•  Example data file records for each data type file

•  Pertinent companion files

•  List of related or ancillary data sets

•  Software (including version number) used to prepare/read the data set

•  Data processing that was performed

•  Personnel & stakeholders

•  Who collected

•  Who to contact with questions

•  Funders

•  Scientific context

•  Scientific reason why the data were collected

•  What data were collected

•  What instruments (including model & serial number) were used

•  Environmental conditions during collection

•  Where collected & spatial resolution When collected & temporal resolution

•  Standards or calibrations used

•  Information about parameters

•  How each was measured or produced

•  Units of measure

•  Format used in the data set

•  Precision & accuracy if known

•  Information about data

•  Definitions of codes used

•  Quality assurance & control measures

•  Known problems that limit data use (e.g. uncertainty, sampling problems)

•  How to cite the data set

4. Metadata basics

•  Provides structure to describe data

Common terms | definitions | language | structure

4. Metadata basics

•  Lots of different standards EML , FGDC, ISO19115, DarwinCore,…

•  Tools for creating metadata files

Morpho (EML), Metavist (FGDC), NOAA MERMaid (CSGDM)

What is metadata?

Select the appropriate metadata standard

What is a metadata standard?

What does metadata look like?

4. Metadata basics

Temperature data

Salinity data

Data import into R

Analysis: mean, SD

Graph production

Quality control & data cleaning “Clean” T

& S data

Summary statistics

Data in R format

5. Workflows

Workflow: how you get from the raw data to the final products of your research

Simple workflows: flow charts

•  R, SAS, MATLAB •  Well-‐documented code is…

Easier to review Easier to share Easier to repeat analysis

5. Workflows

Workflow: how you get from the raw data to the final products of your research

Simple workflows: commented scripts

# % $

&

Fancy Schmancy workflows: Kepler Resulting output

5. Workflows

https://kepler-‐project.org

Workflows enable

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

5. Workflows

From Flickr by merlinprincesse

Minimally: document your analysis commented code; simple flow-‐chart

Emerging workflow applications will… −  Link software for executable end-‐to-‐end analysis −  Provide detailed info about data & analysis −  Facilitate re-‐use & refinement of complex, multi-‐step

analyses −  Enable efficient swapping of alternative models &

algorithms − Help automate tedious tasks

5. Workflows

www.littlebytesoflife.com

The 20-‐Year Rule The metadata accompanying a data set should be written for a user 20 years into the future

6. Data stewardship & reuse

(National Research Council 1991)

From Flickr by greensambaman

RULE

Use stable formats csv, txt, tiff

Create back-‐up copies original, near, far

Periodically test ability to restore information


Modified from R. Cook

Store your data in a repository

Institutional archive

Discipline/specialty archive

DataCite list of repostiories: www.datacite.org/repolist


From Flickr by torkildr

Allows readers to find data products

Get credit for data and publications

Promotes reproducibility

Better measure of research impact

Modified from R. Cook


Data Citation

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

1.  Planning 2.  Data collection & organization 3.  Quality control & assurance 4. Metadata 5. Workflows 6. Data stewardship & reuse 7.  Planning & data management plans in

particular


A document that describes what you will do with your data during your research and after you complete your research

What is a data management plan?

1. Planning

Data Hangover

Saves time Increases efficiency Easier to use data Others can understand & use data Credit for data products Funders require it

1. Planning

Why should I prepare a DMP?

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 DMP Requirements

From Grant Proposal Guidelines:

•  Types of data produced

•  Relationship to existing data

•  How/when/where will the data be captured or created?

•  How will the data be processed?

•  Quality assurance & quality control measures

•  Security: version control, backing up

•  Who will be responsible for data management during/after project?

1.  Types of data & other information

biology.kenyon.edu

C. Strasser

From Flickr by Lazurite

Wired.com

•  What metadata are needed to make the data meaningful? •  How will you create or capture these metadata? •  Why have you chosen particular standards and approaches

for metadata?

2.  Data & metadata standards

•  Are you under any obligation to share data?

•  How, when, & where will you make the data available?

•  What is the process for gaining access to the data?

•  Who owns the copyright and/or intellectual property?

•  Will you retain rights before opening data to wider use? How long? •  Are permission restrictions necessary? •  Embargo periods for political/commercial/patent reasons? •  Ethical and privacy issues? •  Who are the foreseeable data users? •  How should your data be cited?

3.  Policies for access & sharing 4.  Policies for re-‐use & re-‐distribution

•  What data will be preserved for the long term? For how long?

•  Where will data be preserved?

•  What data transformations need to occur before preservation?

5.  Plans for archiving & preservation

From Flickr by theManWhoSurfedTooMuch

•  What metadata will be submitted alongside the datasets?

•  Who will be responsible for preparing data for preservation? Who will be the main contact person for the archived data?

Don’t forget: Budget

•  Costs of data preparation & documentation Hardware, software Personnel Archive fees

•  How costs will be paid Request funding!

dorrvs.com

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

Roadmap

4.  Toolbox

1.  Background


E-‐notebooks & online science

•  NoteBook •  ORNL eNote •  Evernote •  Google Docs •  Blogs •  wikis •  TheLabNotebook.com •  NoteBookMaker

TheLabNotebook.com!

CDL Services for UC Community

www.cdlib.org/services/uc3

Where should I put my data?

Data Repository Deposit | Manage | Share | Preserve

•  Precise identification of a dataset •  Credit to data producers and data publishers •  A link from the traditional literature to the data •  Research metrics for datasets

CDL Services for UC Community

www.cdlib.org/services/uc3

Create & manage persistent identifiers

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

•  Open source add-‐in

•  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

Why are you promoting Excel?

Everyone uses it

Stopgap measure

Why are you promoting Excel?

Funders: Gordon and Betty Moore Foundation, Microsoft Research

•  Data Education Tutorials •  Database of best practices & software tools •  Links to DMPTool •  Primer on data management

www.dataone.org

dcxl.cdlib.org

Data Management 101"

www.carlystrasser.net

Resources"

Slideshare link: this presentation"

Best Practices for Preparing Environmental Data Sets to Share and Archive. September 2010. Hook, Santhana Vannan, Beaty, Cook, & Wilson http://daac.ornl.gov/PI/BestPractices-‐2010.pdf

Some Simple Guidelines for Effective Data Management. Borer, Seabloom, Jones, & Schildhauer. Bull Ecol Soc Amer, April 2009: 205-‐214.

Handy References

Roadmap

4.  Toolbox

1.  Background


1.  Take stock 2.  Take a time machine 3.  Break it down 4.  Get smart

Where to begin?

Getting down & dirty with your data

www.catfishing

tipstod

ay.com

•  What data do you have?

•  What data are you still generating?

•  What does your workflow look like?

•  Are you backing up?

•  How’s your filing system?

•  Etc…

1.  Take stock

From Flickr by charlie llewellin

Knowing what you know now, how would you plan for this project?

–  File structures

–  Metadata generation

–  Naming conventions

Consider writing up a formal data management plan

2.  Take a time machine

From Flickr by F1RSTBORN

You now have a vision. Break into manageable chunks

–  Set a final deadline

–  Set intermediate deadlines

–  Break down tasks to meet those deadlines

–  Be reasonable

3.  Break it down

From

www.gon

omad

.com

From

www.college

humor.com

Learn from mistakes

Plan better next time

Remember: good data management takes

Time

Thoughtfulness

Planning

Resources

4.  Get smart

static.tvtropes.org

dcxl.cdlib.org @dcxlCDL www.facebook.com/DCXLatCDL

www.carlystrasser.net [email protected]

@carlystrasser

uc santa cruz: data management for scientists

Technology

data centers

data education

data hangover

data management landscape

csessums data metadata

data stewardship reuse

deltamike digital data

data collection organization