The Bear Lake Data Repository (BLDR) is an active archive, containing a growing compilation of biological, chemical, and physical datasets collected from Bear Lake and its surrounding watershed. The datasets herein have been digitized from historical records and reports, extracted from papers and theses, and obtained from public and private entities, including the United States Geological Survey, PacifiCorp, and, inter alia, Ecosystems Research Institute.

Contributions are welcome. The BLDR accepts biological, chemical, or physical datasets obtained at Bear Lake, irrespective of funding source. There is no submission size limit at present—workarounds will be found if submissions exceed Hydroshare limits (20 GB). Contributions are published with an open access license and will serve many use cases. The current repository steward, Bear Lake Watch, will advise on submissions and make accepted contributions available promptly.

Metadata files are provided for each dataset, however, contact with original contributor(s) is encouraged for questions and additional details prior to data usage. The BLDR and its contributors shall not be liable for any damages resulting from misinterpretation or misuse of the data or metadata.

The NSF Public Access Repository contains an initial collection of journal publications and the final accepted version of the peer-reviewed manuscript or the version of record. To do this, NSF draws upon services provided by the publisher community including the Clearinghouse of Open Research for the United States, CrossRef, and International Standard Serial Number. When clicking on a Digital Object Identifier number, you will be taken to an external site maintained by the publisher. Some full text articles may not be available without a charge during the embargo, or administrative interval. Some links on this page may take you to non-federal websites. Their policies may differ from this website.

The dataset contains five models with extracted elements for each model. The elements are manually labeled by researchers with construction/civil engineering backgrounds based on discussion and majority vote.

FishSounds presents a compilation of acoustic recordings and published information on sound production across all extant fish species globally. We hope this information can be used to advance research into fish behavior, passive acoustic monitoring, and human impacts on underwater soundscapes as well as serve as a public resource for anyone interested in learning more about fish sounds. This work is the product of an international collaboration between researchers and developers from five organizations. We have taken a cross-disciplinary approach, combining expertise in fish ecology, bioacoustics, and data management to produce a website that we hope will serve the wider marine research community. This Dataverse dataset serves as a permanent repository for all versions of the FishSounds website and associated publications and products. Please see the latest version for the most detailed methodology and data, though the other versions are available for reference. All of the data provided here may be more easily viewed and searched at FishSounds.net. We will be continuing to update and add to FishSounds.net and this repository, so if you would like to suggest an edit or contribute a reference or associated fish sound recording, please contact us at fishsoundscontact@gmail.com.

This dataset contains resources transformed from other datasets on HDX. They exist here only in a format modified to support visualization on HDX and may not be as up to date as the source datasets from which they are derived.

Source datasets: https://data.hdx.rwlabs.org/dataset/idps-data-by-region-in-mali

The Administrative Data Repository (ADR) was established to provide support for the administrative data elements relative to multiple categories of a person entity such as demographic and eligibility information. Although initially focused on the computing needs of the Veterans Health Administration, the ADR is positioned to provide identity management and demographics support for all IT systems within the Department of Veterans Affairs.

EdSight is an education data portal that integrates information from over 30 different sources – some reported by districts and others from external sources. The portal can be accessed here: http://edsight.ct.gov/.

Information is available on key performance measures that make up the Next Generation Accountability System, as well as dozens of other topics, including school finance, special education, staffing levels and school enrollment.

amazon-sagemaker/repository-metadata dataset hosted on Hugging Face and contributed by the HF Datasets community

Data collected from major Canadian and international research data repositories cover data storage, preservation, metadata, interchange, data file types, and other standard features used in the retention and sharing of research data. The outputs of this project primarily aim to assist in the establishment of recommended minimum requirements for a Canadian research data infrastructure. The committee also aims to further develop guidelines and criteria for the assessment and selection o f repositories for deposit of Canadian research data by researchers, data managers, librarians, archivists etc.

Repository that serves to coordinate searches across data and biospecimen collections from participants in numerous clinical trials and epidemiologic studies and to provide an electronic means for requests for additional information and the submission of requests for collections. The collections, comprising data from more than 80 trials or studies and millions of biospecimens, are available to qualified investigators under specific terms and conditions consistent with the informed consents provided by the individual study participants. Some datasets are presented with studies and supporting materials to facilitate their use in reuse and teaching. Datasets support basic research, clinical studies, observational studies, and demonstrations. Researchers wishing to apply to submit biospecimen collections to the NHLBI Biorepository for sharing with qualified investigators may also use this website to initiate that process.

SWISS-MODEL homology models mapping to UniProtKB Proteome UP000001584 (Mycobacterium tuberculosis)

This document describes data collected from the Main Collection of the Web of Science database. Records of published studies addressing the intersection of Open Science and data repository were searched up to January 15th, 2024, and the final dataset was comprised of 545 records for bibliometric analysis.

Identifiers of many kinds are the key to creating unambiguous and persistent connections between research objects and other items in the global research infrastructure (GRI). Many repositories are implementing mechanisms to collect and integrate these identifiers into their submission and record curation processes. This bodes well for a well-connected future, but many existing resources submitted in the past are missing these identifiers, thus missing the connections required for inclusion in the connected infrastructure. Re-curation of these metadata is required to make these connections. The Dryad Data Repository has existed since 2008 and has successfully re-curated the repository metadata several times, adding identifiers for research organizations, funders, and researchers. Understanding and quantifying these successes depends on measuring repository and identifier connectivity. Metrics are described and applied to the entire repository here. Identifiers for papers (DOIs) connected to datasets in Dryad have long been a critical part of the Dryad metadata creation and curation processes. Since 2019, the % of datasets with connected papers has decreased from 100% to less than 40%. This decrease has significant ramifications for the re-curation efforts described above as connected papers are an important source of metadata. In addition, missing connections to papers make understanding and re-using datasets more difficult. Connections between datasets and papers are many times difficult to make because of time lags between submission and publication, lack of clear mechanisms for citing datasets and other research objects from papers, changing focus of researchers, and other obstacles. The Dryad community of members, i.e. users, research institutions, publishers, and funders have vested interests in identifying these connections and critical roles in the curation and re-curation efforts. Their engagement will be critical in building on the successes Dryad has already achieved and ensuring sustainable connectivity in the future. Methods These data are Dryad metadata retrieved from https://datadryad.org and translated into csv files. There are two datasets: 1. DryadJournalDataset was retrieved from Dryad using the ISSNs in the file DryadJournalDataset_ISSNs.txt, although some had no data. 2. DryadOrganizationDataset was retrieved from Dryad using the RORs in the file DryadOrganizationDataset_RORs.txt, although some had no data. Each dataset includes four types of metadata: identifiers, funders, keywords, and related works, each in a separate comma (.csv) or tab (.tsv) delimited files. There are also Microsoft Excel files (.xlsx) for the identifier metadata and connectivity summaries for each dataset (*.html). The connectivity summaries include summaries of each parameter in all four data files with definitions, counts, unique counts, most frequent values, and completeness. These data formed the basis for an analysis of the connectivity of the Dryad repository for organizations, funders, and people.

This data set accompanies Fenner et al. (2024) and contains the data (and references to data sources) of the plots and tables therein.

Data are organized by Figure and Table in the article, each located in a separate (zip-)folder.

See README.pdf for additional information and data descriptions.

Detailed data processing details are given in the Appendices of the article.

RAW measurement data are accessible via the Zenodo “urbisphere” community.

Fenner, D., Christen, A., Grimmond, S., Meier, F., Morrison, W., Zeeman, M., Barlow, J., Birkmann, J., Blunn, L., Chrysoulakis, N., Clements, M., Glazer, R., Hertwig, D., Kotthaus, S., König, K., Looschelders, D., Mitraka, Z., Poursanidis, D., Tsirantonakis, D., Bechtel, B., Benjamin, K., Beyrich, F., Briegel, F., Feigel, G., Gertsen, C., Iqbal, N., Kittner, J., Lean, H., Liu, Y., Luo, Z., McGrory, M., Metzger, S., Paskin, M., Ravan, M., Ruhtz, T., Saunders, B., Scherer, D., Smith, S. T., Stretton, M., Trachte, K. and Van Hove, M., 2024: urbisphere-Berlin campaign: Investigating multi-scale urban impacts on the atmospheric boundary layer. Bull. Am. Meteorol. Soc. DOI: 10.1175/BAMS-D-23-0030.1

THIS RESOURCE IS NO LONGER IN SERVICE, documented January 13, 2022. Open access repository of original, unprocessed data underlying work published by Stowers researchers to allow the scientific community to validate and extend the findings made by Stowers researchers. For papers first submitted for publication after November 1, 2011, the Stowers Institute requires its members to deposit original data files into the Stowers Original Data Repository or to repositories maintained by third parties at the time of publication. Access to the Stowers Original Data Repository is free, but you will be asked to register before you can download data.

Collected in this dataset are the slideset and abstract for a presentation on Toward a Reproducible Research Data Repository by the depositar team at International Symposium on Data Science 2023 (DSWS 2023), hosted by the Science Council of Japan in Tokyo on December 13-15, 2023. The conference was organized by the Joint Support-Center for Data Science Research (DS), Research Organization of Information and Systems (ROIS) and the Committee of International Collaborations on Data Science, Science Council of Japan. The conference programme is also included as a reference.

Title

Toward a Reproducible Research Data Repository

Author(s)

Cheng-Jen Lee, Chia-Hsun Ally Wang, Ming-Syuan Ho, and Tyng-Ruey Chuang

Affiliation of presenter

Institute of Information Science, Academia Sinica, Taiwan

Summary of Abstract

The depositar (https://data.depositar.io/) is a research data repository at Academia Sinica (Taiwan) open to researhers worldwide for the deposit, discovery, and reuse of datasets. The depositar software itself is open source and builds on top of CKAN. CKAN, an open source project initiated by the Open Knowledge Foundation and sustained by an active user community, is a leading data management system for building data hubs and portals. In addition to CKAN's out-of-the-box features such as JSON data API and in-browser preview of uploaded data, we have added several features to the depositar, including sourcing from Wikidata as dataset keywords, a citation snippet for datasets, in-browser Shapefile preview, and a persistent identifier system based on ARK (Archival Resource Keys). At the same time, the depositar team faces an increasing demand for interactive computing (e.g. Jupyter Notebook) which facilitates not just data analysis, but also for the replication and demonstration of scientific studies. Recently, we have provided a JupyterHub service (a multi-tenancy JupyterLab) to some of the depositar's users. However, it still requires users to first download the data files (or copy the URLs of the files) from the depositar, then upload the data files (or paste the URLs) to the Jupyter notebooks for analysis. Furthermore, a JupyterHub deployed on a single server is limited by its processing power which may lower the service level to the users. To address the above issues, we are integrating the BinderHub into the depositar. BinderHub (https://binderhub.readthedocs.io/) is a kubernetes-based service that allows users to create interactive computing environments from code repositories. Once the integration is completed, users will be able to launch Jupyter Notebooks to perform data analysis and vsualization without leaving the depositar by clicking the BinderHub buttons on the datasets. In this presentation, we will first make a brief introduction to the depositar and BinderHub along with their relationship, then we will share our experiences in incorporating interactive computation in a data repository. We shall also evaluate the possibility of integrating the depositar with other automation frameworks (e.g. the Snakemake workflow management system) in order to enable users to reproduce data analysis.

Keywords

BinderHub, CKAN, Data Repositories, Interactive Computing, Reproducible Research

A listing of NIH supported data sharing repositories that make data accessible for reuse. Most accept submissions of appropriate data from NIH-funded investigators (and others), but some restrict data submission to only those researchers involved in a specific research network. Also included are resources that aggregate information about biomedical data and information sharing systems. The table can be sorted according by name and by NIH Institute or Center and may be searched using keywords so that you can find repositories more relevant to your data. Links are provided to information about submitting data to and accessing data from the listed repositories. Additional information about the repositories and points-of-contact for further information or inquiries can be found on the websites of the individual repositories.

In order to better understand the factors that most influence where researchers deposit their data when they have a choice, we collected survey data from researchers who deposited phylogenetic data in either the TreeBASE or Dryad data repositories. Respondents were asked to rank the relative importance of eight possible factors. We found that factors differed in importance for both TreeBASE and Dryad, and that the rankings differed subtly but significantly between TreeBASE and Dryad users. On average, TreeBASE users ranked the domain specialization of the repository highest, while Dryad users ranked as equal highest their trust in the persistence of the repository and the ease of its data submission process. Interestingly, respondents (particularly Dryad users) were strongly divided as to whether being directed to choose a particular repository by a journal policy or funding agency was among the most or least important factors. Some users reported depositing their data in multiple repositories and archiving their data voluntarily.

The Securities Finance: Repo Data Analytics dataset equips users with essential metrics and comprehensive data to make informed decisions in global repo trading.