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Archive for the ‘Data reuse’ Category

Alfred P. Sloan Foundation grant will fund implementation of shared staffing model across 7 academic libraries and Dryad

We’re thrilled to announce that Dryad will participate in a three-year, multi-institutional effort to launch the Data Curation Network. The implementation — led by the University of Minnesota Libraries and backed by a $526,438 grant from the Alfred P. Sloan Foundation — builds on previous work to better support researchers faced with a growing number of requirements to openly and ethically share their research data.

The result of many months of research and planning, the project brings together eight partners:

Currently, staff at each of these institutions provide their own data curation services. But because data curation requires a specialized skill set — spanning a wide variety of data types and discipline-specific data formats — institutions cannot reasonably expect to hire an expert in each area.

Curation workflow for the DCN

The intent of the Data Curation Network is to serve as a cross-institutional staffing model that seamlessly connects a network of expert data curators to local datasets and to supplement local curation expertise. The project aims to increase local capacity, strengthen cross-institutional collaboration, and ensure that researchers and institutions ethically and appropriately share data.

Lisa R. Johnston, Principal Investigator for the DCN and Director of the Data Repository for the University of Minnesota (DRUM), explains:

Functionally, the Data Curation Network will serve as the ‘human layer’ in a local data repository stack that provides expert services, incentives for collaboration, normalized curation practices, and professional development training for an emerging data curator community.

For our part, the Dryad curation team is excited to join a collegial network of professionals, to help develop shared procedures and understandings, and to learn from the partners’ experience and expertise (as they may learn from ours).

As an independent, non-profit repository, we are especially pleased to get to work more closely with the academic library community, and hope this project can provide a launchpad for future, international collaborations among organizations with similar missions but differing structures and funding models.

Watch this space for news as the project develops, and follow the DCN on Twitter: #DataCurationNetwork

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Dryad is a general purpose repository for data underlying scholarly publications. Each new submission we receive is reviewed by our curation team before the data are archived. Our main priority is to ensure compliance with Dryad’s Terms of Service, but we also strongly believe that curation activities add value to your data publication, since curated data are more likely to be FAIR (findable, accessible, interoperable, and reusable).

FAIR

Before we register a DOI, a member of our curation team will check each data package to ensure that the data files can be opened, that they appear to contain information associated with a scientific publication, and that metadata for the associated publication are technically correct. We prefer common, non-proprietary file types and thorough documentation, and we may reach out if we are unable to view files as provided.

Our curators are also on the lookout for sensitive information such as personally identifiable human subjects data or protected location information, and for files that contain copyright and license statements that are incompatible with our required CC0 waiver.

To make the data archiving process more straightforward for authors, our curation team has authored sets of guidelines that may be consulted when preparing a data submission for a public repository such as Dryad. We hope these guidelines will help you as you prepare your Dryad data package, and that they will lessen the amount of time from point of submission to registered data DOI!

A series of blog posts will highlight each of the guidelines we’ve created. First up is our best practices for sharing human subjects data in an open access repository, from former Dryad curator Rebecca Kameny.

— Erin Clary, Senior Curator – curator@datadryad.org

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Preparing human subject data for open access

Collecting, cleaning, managing, and analyzing your data is one thing, but what happens when you are ready to share your data with other researchers and the public?

peopleBecause our researchers come from fields that run the gamut of academia — from biology, ecology, and medicine, to engineering, agriculture, and sociology — and because almost any field can make use of data from human subjects, we’ve provided guidance for preparing such data for open access. We based our recommendations and requirements on well-respected national and international sources from government institutions, universities, and peer-reviewed publications.

Dryad curators will review data files for compliance with these recommendations, and may make suggestions to authors, however, authors who submit data to Dryad are ultimately responsible for ensuring that their data are properly anonymized and can be shared in a public repository.

handle-43946_960_720In a nutshell, Dryad does not allow any direct identifiers, but we do allow up to three indirect identifiers. Sound simple? It’s not. If the study involves a vulnerable population (such as children or indigenous people), if the number of participants is small, or if the data are sensitive (e.g., HIV status, drug use), three indirect identifiers may be too many. We evaluate each submission on a case-by-case basis.

If you have qualitative data, you’ll want to pay close attention to open-ended text, and may need to replace names with pseudonyms or redact identifiable text.

Quick tips for preparing human subjects data for sharing

  • Ensure that there are no direct identifiers.
  • Remove any nonessential identifying details.
  • Reduce the precision of a variable – e.g., remove day and month from date of birth; use county instead of city; add or subtract a randomly chosen number.
  • Aggregate variables that are potentially revealing, such as age.
  • Restrict the upper or lower ranges of a continuous variable to hide outliers by collapsing them into a single code.
  • Combine variables by merging data from two variables into a summary variable.

It’s also good research practice to provide clear documentation of your data in a README file. Your README should define your variables and allowable values, and can be used to alert users to any changes you made to the original dataset to protect participant identity.

Our guidelines expand upon the tips above, and link to some useful references that will provide further guidance to anyone who would like to share human subjects data safely.

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In 2011 Peggy Schaeffer penned an entry for this blog titled “Why does Dryad use CC0?” While 2011 seems like a long time ago, especially in our rapidly evolving digital world, the information in that piece is still as valid and relevant now as it was then. In fact, Dryad curators routinely direct authors to that blog entry to help them understand and resolve licensing issues. Since dealing with licensing matters can be confusing, it seems about time to revisit this briefly from a practical perspective.

Dryad uses Creative Commons Zero (CC0) to promote the reuse of data underlying scholarly literature. CC0 provides consistent, clear, and open terms of reuse for all data in our repository by allowing researchers, authors, and others to waive all copyright and related rights for a work and place the work in the public domain. Users know they can reuse any data available in Dryad with minimal impediments; authors gain the potential for more citations without having to spend time responding to requests from those wishing to use their data. In other words, CC0 helps eliminate the headaches associated with copyright and licensing issues for all stakeholders, leading to more data reuse.

So what does this mean in practical terms? Dryad’s curators have come up with a few suggestions to keep in mind as you prepare your data for submission. These tips can help you manage the CC0 requirements and avoid any problems:

DO:

  • Make sure any software included with your submission can be released under CC0. For example, licenses such as GPL or MIT are common and are not compatible with CC0. Be sure there are no licensing statements displayed in the software itself or in associated readme files.
  • Be aware that there are software applications out there that automatically place any output produced by the software under a non-CC0 compatible license. Consider this when you are deciding which software to use to prepare your data.
  • Know the terms of use for any information you get from a website or database.
  • Ensure that any images, videos, or other media that are not your own work can be released under CC0.
  • Be sure to clean up your data before submitting it, especially if you are compressing it using a tool such as zip or tar. Remove anything that can’t be released under CC0, along with any other extraneous materials, such as user manuals for hardware or software tools. Not only does removing extraneous files lessen the chance something will conflict with Dryad’s CC0 policy, it also makes your data more streamlined and easier to use.

DON’T:

  • Don’t add text anywhere in your data submission requiring permission or attribution for reuse. Community norms do a great job of putting in place the expectation that anyone reusing your data will provide the proper citations. CC0 actually encourages citation by keeping the process as simple as possible.
  • Don’t include your entire manuscript or parts of your manuscript in your data package. Most publications have licensing that restricts reuse and is not compatible with CC0.

I hope this post leaves you with a little more understanding about why Dryad uses CC0 and with a few tips that will help make following Dryad’s CC0 requirement easier.

 

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We present a guest post from researcher Falk Lüsebrink highlighting the benefits of data sharing. Falk is currently working on his PhD in the Department of Biomedical Magnetic Resonance at the Otto-von-Guericke University in Magdeburg, Germany. Here, he talks about his experience of sharing early MRI data and the unexpected impact that it is having on the research community.

Early release of data

The first time I faced a decision about publishing my own data was while writing a grant proposal. One of our proposed objectives was to acquire ultrahigh resolution brain images in vivo, making use of an innovative development: a combination of an MR scanner with ultrahigh field strength and a motion correction setup to remediate subject motion during data acquisition. While waiting for the funding decision, I simply could not resist acquiring a first dataset. We scanned a highly experienced subject for several hours, allowing us to acquire in vivo images of the brain with a resolution far beyond anything achieved thus far.

 MRI data showing the cerebellum in vivo

MRI data showing the cerebellum in vivo at (a) neuroscientific standard resolution of 1 mm, (b) our highest achieved resolution of 250 µm, and (c) state-of-the-art 500 µm resolution.

When our colleagues saw the initial results, they encouraged us to share the data as soon as possible. Through Scientific Data and Dryad, we were able to do just that. The combination of a peer-reviewed open access journal and an open access digital repository for the data was perfect for presenting our initial results.

17,000 downloads and more

‘Sharing the wealth’ seems to have been the right decision; in the three months since we published our data, there has been an enormous amount of activity:

A distinct need for data re-use

MRI studies are highly interdisciplinary, opening up numerous opportunities for sharing and re-using data. For example, our data might be used to build MR brain atlases and illustrate brain structures in much greater detail, or even for the first time. This could advance our understanding of brain functions. Algorithms used to quantify brain structures needed in the research of neurodegenerative disorders could be enhanced, increasing accuracy and reproducibility. Furthermore, by making available raw signals measured by the MR scanner, image reconstruction methods could be used to refine image quality or reduce the time it takes to collect the data.

There are also opportunities beyond those that our particular dataset offers. A recent emerging trend in MRI comes from the field of machine learning. Neuronal networks are being built to perform and potentially improve all kinds of tasks, from image reconstruction, to image processing, and even diagnostics. To train such networks, huge amounts of data are necessary; these data could come from repositories open to the public. Such re-use of MRI data by researchers in other disciplines is having a strong impact on the advancement of science. By publicly sharing our data, we are allowing others to pursue new and exciting directions.

Download the data for yourself and see what you can do with it. In the meantime, I am still eagerly awaiting the acceptance of the grant application . . . but that’s a different story.

The data: http://dx.doi.org/10.5061/dryad.38s74

The article: http://dx.doi.org/10.1038/sdata.2017.32

— Falk Lüsebrink

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We’re pleased to present a guest post from data scientist Juan M. Banda, the lead author of an important, newly-available resource for drug safety research. Here, Juan shares some of the context behind the data descriptor in Scientific Data and associated data package in Dryad. – EH

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As I sit in a room full of over one hundred bio-hackers at the 2016 Biohackathon in Tsuruoka, Yamagata, Japan, the need to have publicly available and accessible data for research use is acutely evident. Organized by Japan’s National Biosciences Database Center (NBDC) and Databases Center for Life Science (DBLS), this yearly hackathon gathers people from organizations and universities all over the world, including the National Center for Biotechnology Information (NCBI) and the European Bioinformatics Institute (EBI), with the purpose of extending and interlinking resources like PubChem, PhenomeCentral, Bio2RDF, and PubAnnotation.

The end goal: finding better ways to access data that will allow researchers to focus on analysis of the data rather than preparation.

In the same spirit, our publication “A curated and standardized adverse drug event resource to accelerate drug safety research” (doi:10.1038/sdata.2016.26; data in Dryad at http://doi.org/10.5061/dryad.8q0s4) helps researchers in the drug safety domain with the standardization and curation of the freely available data from the Federal Food and Drug Administration (FDA) adverse events reporting system (FAERS).

FAERS collects information on adverse events and medication errors reported to the FDA, and is comprised of over 10 million records collected between 1969 to the present. As one of the most important resources for drug safety efforts, the FAERS database has been used in at least 750 publications as reported by PubMed and was probably manipulated, mapped and cleaned independently by the vast majority of the authors of said publications. This cleaning and mapping process takes a considerable amount of time — hours that could have been spent analyzing the data further.

Our publication hopes to eliminate this needless work and allow researchers to focus their efforts in developing methods to analyze this information.

OHDSIAs part of the Observational Health Sciences Initiative (OHDSI), whose mission is to “Improve health, by empowering a community to collaboratively generate the evidence that promotes better health decisions and better care,” we decided to tackle the task of cleaning and curating the FAERS database for our community, and the wider drug safety community. By providing a general common data model (CDM) and a general vocabulary to standardize how electronic patient data is stored, OHDSI allows its participants to join a research network with over 655 million patients.

With a significant fraction of the community’s research being focused on drug safety, it was a natural decision to standardize the FAERS database with the OMOP vocabulary, to allow all researchers on our network access to FAERS. Since the OMOP vocabulary incorporates general vocabularies such as SNOMED, MeSH, and RxNORM, among others, the usability of this resource is not limited to participants of this community.

In order to curate this dataset, we took the source FAERS data in CSV format and de-duplicated case reports. We then performed value imputation for certain fields that were missing. Drug names were standardized to RxNorm ingredients and standard clinical names (for multi-ingredient drugs). This mapping is tricky because some drug names have spelling errors, and some are non-prescription drugs, or international brand names. We achieved coverage of 93% of the drug names, which in turn cover 95% of the case reports in FARES.

For the first time, the indication and reactions have been mapped to SNOMED-CT from their original MedRA format. Coverage for indications and reactions is around 64% and 80%, respectively. The OMOP vocabulary allows RxNorm drug codes as well as SNOMED-CT codes to reside in the same unified vocabulary space, simplifying use of this resource. We also provide the complete source code we developed in order to allow researchers to refresh the dataset with the new quarterly FAERS data releases and improve the mappings if needed. We encourage users to contribute the results of their efforts back to the OHDSI community.

With a firm commitment to making open data easier to use, this resource allows researchers to utilize a professionally curated (and refreshable) version of the FAERS data, enabling them to focus on improving drug safety analyses and finding more potentially harmful drugs, as a part of OHDSI’s core mission.

OHDSI_still2

Still from OHMSDI video

The data:

http://doi.org/10.5061/dryad.8q0s4

A full description of the dataset in Scientific Data:

http://www.nature.com/articles/sdata201626

 

— Juan M. Banda

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2015While gearing up for the Dryad member meeting (to be held virtually on 24 May – save the date!) and publication of our annual report, we’re taking a look at last year’s numbers.

2015 was a “big” year for Dryad in many respects. We added staff, and integrated several new journals and publishing partners. But perhaps most notably, the Dryad repository itself is growing very rapidly. We published 3,926 data packages this past year — a 44% increase over 2014 — and blew past the 10,000 mark for total data packages in the repository.

Data package size

Perhaps the “biggest” Dryad story from last year is the increase in the mean size of data packages published. In 2014, that figure was 212MB. In 2015, it more than doubled to 481MB, an increase of a whopping 127%.

This striking statistic is part of the reason we opted at the beginning of 2016 to double the maximum package size before overage fees kick in (to 20GB), and simplified and reduced our overage fees. We want researchers to continue to archive more (and larger) data files, and to do so sustainably. Meanwhile, we do continue to welcome many submissions on the smaller end of the scale.

boxplot_logscale_labels

Distribution of Dryad data package size by year. Boxplot shows median, 1st and 3rd quartiles, and 95% confidence interval of median. Note the log scale of the y-axis.

In 2015, the mean number of files in a data package was about 3.4, with 104 as the largest number of files in any data package. To see how times have changed, compare this to a post from 2011 (celebrating our 1,000th submission), where we noted:

Interestingly, most of the deposits are relatively small in size. Counting all files in a data package together, almost 80% of data packages are less than one megabyte. Furthermore, the majority of data packages contain only one data file and the mean is a little less than two and a half. As one might expect, many of the files are spreadsheets or in tabular text format. Thus, the files are rich in information but not so difficult to transfer or store.

We have yet to do a full analysis of file formats deposited in 2015, but we see among the largest files many images and videos, as would be expected, but also a notable increase in the diversity of DNA sequencing-related file formats.

So not only are there now more and bigger files in Dryad, there’s also greater complexity and variety. We think this shows that more people are learning about the benefits of archiving and reusing multiple file types, and that researchers (and publishers) are broadening their view of what qualifies as “data.”

Download counts

2015speciesSo who had the biggest download numbers in 2015? Interestingly, nearly all of last year’s most-downloaded data packages are from genetics/genomics. 3 of the top 5 are studies of specific wild populations and how they adapt to changing circumstances — Sailfin Mollies (fish), blue tits (birds), and bighorn sheep, specifically.

Another top package presents a model for dealing with an epidemic that had a deadly impact on humans in 2015. And rounding out the top 5 is an open source framework for reconstructing the relationships that unite all lineages — a “tree of life.”

In 5th place, with 367 downloads:

In 4th place, with 601 downloads:

In 3rd place, with 1,324 downloads:

In 2nd place, with 1,868 downloads:

And this year’s WINNER, with 2,678 downloads:

The above numbers are presented with the usual caveats about bots, which we aim to filter out, but cannot do with perfect accuracy. (Look for a blog post on this topic in the near future).

As always, we owe a huge debt to our submitters, partners, members and users for supporting Dryad and open data in 2015!

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The reason why Dryad is in the business of archiving, preserving, and providing access to research data is so that it will be reused, whether for deeper reading of the publication, for post-publication review, for education, or for future research. While it’s not yet as easy as we would like to track data reuse, one metric that is straightforward to collect is the number of times a dataset has been downloaded, and this is one of two data reuse statistics reported by our friends at ImpactStory and Plum Analytics.

2014 with fireworks

The numbers are very encouraging. There are already over a quarter million downloads for the 8,897 data files released in 2014 (from 2,714 data packages). That’s over 28 downloads per data file. While there is always the caveat that some downloads may be due to activity from newly emerged bots that we have yet to recognize and filter out, we think it is safe to say that most of these downloads are from people.

To celebrate, we would like to pay special tribute to the top five data packages from 2014, as measured by the maximum number of downloads for any single file (since many data packages have more than one) at the time of writing. They cover a diversity of topics from livestock farming in the Paleolithic to phylogenetic relationships among insects. That said, we are struck by the impressively strong showing for plant science — 3 of the top 5 data packages.

In 5th place, with 453 downloads

In 4th place, with 581 downloads

In 3rd place, with 626 downloads

In 2nd place, with 4,672 downloads

And in 1st place, with a staggering 34,879 downloads

Remarkably, given the number of downloads, this last data package was only released in November.

We’d like to thank all of our users, whether you contribute data or reuse it (or both), for helping make science just a little more transparent, efficient, and robust this past year. And we are looking forward to finding out some more of what you did with all those downloads in 2015!

 

 

 

 

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