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867 results for “repositories”
Data repository of GPR survey of the Epithany Cathedral of Kyiv Brotherhood Monastery by Kseniia Bondar
<p>This is the data repository for the article by Kseniia Bondar, Serhiy Taranenko<sup>,</sup> Yaroslav Zatyliuk, Olena Popelnytska and Tetiana Osinchuk by the name "<strong>Ground penetrating radar scanning and historical interpretation of the location of the destroyed Epiphany Cathedral in Kyiv Brotherhood Monastery (Ukraine)</strong>". In this repository, all the measurement data are stored for further use.</p>
Data repository - Dietary shifts increase the feasibility of 1.5°C pathways
<p>This repository contains modelling results of a study conducted with the opensource Integrated Assessment Modelling (IAM) framework REMIND-MAgPIE (REMIND 3.2.0 and MAgPIE 4.6.7)</p> <p>The source code for REMIND 3.2.0 is openly available at https://github.com/remindmodel and https://doi.org/10.5281/zenodo.7852740. <br> The model documentation can be found at https://rse.pik-potsdam.de/doc/remind/3.2.0. Instructions for software installation, running the model and coupling to MAgPIE (tutorials subfolder) are available at https://github.com/remindmodel/remind.</p> <p>The source code for MAgPIE 4.6.7 is openly available at https://github.com/magpiemodel and https://doi.org/10.5281/zenodo.1418752. <br> The model documentation can be found at https://rse.pik-potsdam.de/doc/magpie/4.6.7/. Instructions for software installation and running the model are available at https://github.com/magpiemodel/magpie.</p>
Benchmark Data Repositories: Lessons and Recommendations
<p>Our dataset "repository_survey" summarizes a comprehensive survey of over 150 data repositories, characterizing their metadata documentation and standardization, data curation and validation, and tracking of dataset use in the literature. In addition, "survey_model_evaluation" includes our findings on model evaluation for five benchmark repositories. Column descriptions and further details can be found in "README.pdf." The data are associated with our paper "Benchmark Data Repositories: Lessons and Recommendations." </p>
Code repository for: Base editing mutagenesis maps functional alleles to tune human T cell activity
<p>Jupyter notebook and supplemental datasets required to created critical figures for the publication.</p>
Data from the Swiss Open Data Repository Landscape survey
<p>This file collection is part of the ORD Landscape and Cost Analysis Project (DOI: 10.5281/zenodo.2643460), a study jointly commissioned by the SNSF and swissuniversities in 2018.</p> <p>Please cite this data collection as:<br> von der Heyde, M. (2019). Data from the Swiss Open Data Repository Landscape survey. Retrieved from https://doi.org/10.5281/zenodo.2643487</p> <p>Further information is given in the corresponding data paper:<br> von der Heyde, M. (2019). Open Data Landscape: Repository Usage of the Swiss Research Community: Description of collection, collected data, and analysis methods [Data paper]. Retrieved from https://doi.org/10.5281/zenodo.2643430</p> <p> </p> <p><strong>Contact</strong></p> <p>Swiss National Science Foundation (SNSF)</p> <p>Open Research Data Group</p> <p>E-mail: <a href="mailto:ord@snf.ch">ord@snf.ch</a></p> <p> </p> <p>swissuniversities</p> <p>Program "Scientific Information"</p> <p>Gabi Schneider</p> <p>E-Mail: <a href="mailto:isci@swissuniversities.ch">isci@swissuniversities.ch</a></p>
Data from the International Open Data Repository Survey
<p>This file collection is part of the ORD Landscape and Cost Analysis Project (DOI: 10.5281/zenodo.2643460), a study jointly commissioned by the SNSF and swissuniversities in 2018.</p> <p>Please cite this data collection as:<br> von der Heyde, M. (2019). Data from the International Open Data Repository Survey. Retrieved from https://doi.org/10.5281/zenodo.2643493</p> <p>Further information is given in the corresponding data paper:<br> von der Heyde, M. (2019). International Open Data Repository Survey: Description of collection, collected data, and analysis methods [Data paper]. Retrieved from https://doi.org/10.5281/zenodo.2643450</p> <p> </p> <p><strong>Contact</strong></p> <p>Swiss National Science Foundation (SNSF)</p> <p>Open Research Data Group</p> <p>E-mail: <a href="mailto:ord@snf.ch">ord@snf.ch</a></p> <p> </p> <p>swissuniversities</p> <p>Program "Scientific Information"</p> <p>Gabi Schneider</p> <p>E-Mail: <a href="mailto:isci@swissuniversities.ch">isci@swissuniversities.ch</a></p>
Data and tools of the landscape and cost analysis of data repositories currently used by the Swiss research community
<p>This file collection is part of the ORD Landscape and Cost Analysis Project (DOI: 10.5281/zenodo.2643460), a study jointly commissioned by the SNSF and swissuniversities in 2018.</p> <p>Please cite this data collection as:<br> von der Heyde, M. (2019). Data and tools of the landscape and cost analysis of data repositories currently used by the Swiss research community. Retrieved from https://doi.org/10.5281/zenodo.2643495</p> <p>Connected data papers are:<br> von der Heyde, M. (2019). Open Data Landscape: Repository Usage of the Swiss Research Community: Description of collection, collected data, and analysis methods [Data paper]. Retrieved from https://doi.org/10.5281/zenodo.2643430<br> von der Heyde, M. (2019). International Open Data Repository Survey: Description of collection, collected data, and analysis methods [Data paper]. Retrieved from https://doi.org/10.5281/zenodo.2643450</p> <p>Connected data sets are:<br> von der Heyde, M. (2019). Data from the Swiss Open Data Repository Landscape survey. Retrieved from https://doi.org/10.5281/zenodo.2643487<br> von der Heyde, M. (2019). Data from the International Open Data Repository Survey. Retrieved from https://doi.org/10.5281/zenodo.2643493</p> <p> </p> <p><strong>Contact</strong></p> <p>Swiss National Science Foundation (SNSF)</p> <p>Open Research Data Group</p> <p>E-mail: <a href="mailto:ord@snf.ch">ord@snf.ch</a></p> <p> </p> <p>swissuniversities</p> <p>Program "Scientific Information"</p> <p>Gabi Schneider</p> <p>E-Mail: <a href="mailto:isci@swissuniversities.ch">isci@swissuniversities.ch</a></p>
Repository Analytics and Metrics Portal (RAMP) 2021 data
Open the record for dataset details and reuse information.
Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning: Code repository
Open the record for dataset details and reuse information.
Repository Analytics and Metrics Portal (RAMP) 2020 data
Open the record for dataset details and reuse information.
Examples of CARE-related Activities Carried out by Repositories, in Sequences or Groups
This dataset is designed to accompany the paper submitted to Data Science Journal: O'Brien et al, "Earth Science Data Repositories: Implementing the CARE Principles". This dataset shows examples of activities that data repositories are likely to undertake as they implement the CARE principles. These examples were constructed as part of a discussion about the challenges faced by data repositories when acquiring, curating, and disseminating data and other information about Indigenous Peoples, communities, and lands. For clarity, individual repository activities were very specific. However, in practice, repository activities are not carried out singly, but are more likely to be performed in groups or in sequence. This dataset shows examples of how activities are likely to be combined in response to certain triggers. See related dataset O'Brien, M., R. Duerr, R. Taitingfong, A. Martinez, L. Vera, L. Jennings, R. Downs, E. Antognoli, T. ten Brink, N. Halmai, S.R. Carroll, D. David-Chavez, M. Hudson, and P. Buttigieg. 2024. Alignment between CARE Principles and Data Repository Activities. Environmental Data Initiative. https://doi.org/10.6073/pasta/23e699ad00f74a178031904129e78e93 (Accessed 2024-03-13), and the paper for more information about development of the activities and their categorization, raw data of relationships between specific activities and a discussion of the implementation of CARE Principles by data repositories. Data in this table are organized into groups delineated by a triggering event in the first column. For example, the first group consists of 9 rows; while the second group has 7 rows. The first row of each group contains the event that triggers the set of actions described in the last 4 columns of the spreadsheet. Within each group, the associated rows in each column are given in numerical not temporal order, since activities will likely vary widely from repository to repository. For example, the first group of rows is about what likely needs to h
temporalNEON: Repository containing raw and cleaned-up organismal data from the National Ecological Observatory Network (NEON) useful for evaluating the links between change in biodiversity and ecosystem stability
Organismal data include the following taxonomic groups: small mammals, fish, ground beetles, and aquatic macroinvertebrates. Data were retrieved from the National Ecological Observatory Network (NEON) database in November 2020. We submit both raw data retrieved from NEON as .rds files, R code used to process these data, as well as processed data as .csv files.
Data repository to "The thermal and rheological state of the Northern Argentinian foreland basins"
<p>This is the data repository to the doctoral thesis "The thermal and rheological state of the northern Argentinian foreland basins" by Christian Meeßen. It contains data to the following chapters</p> <ul> <li>Chapter 2.1: "Crustal structure of the Andean foreland in northern Argentina: Results from data-integrative three-dimensional density modelling"</li> <li>Chapter 3.1: "How do first-order controlling factors of subduction zones affect the thermal field of retroarc foreland basins?"</li> <li>Chapter 3.2: "Differences between transient and steady-state thermal fields in the central Andean foreland"</li> <li>Chapter 4: "The present-day thermal and rheological state of the Chaco-Paraná basin"</li> </ul>
Repository for: Reddin et al. 2020. Marine clade sensitivities to climate change conform across time scales
<p>Contains R code and data to produce the main results (and some supplementary results) of the publication, Reddin et al. <em>Marine clade sensitivities to climate change conform across time scales</em>.</p>
Paper Repository and References for "Early software defect prediction: A systematic map and review"
<p>Context: Software defect prediction is a trending research topic, and a wide variety of the published papers focus on coding phase or after. A limited number of papers, however, includes the prior (early) phases of the software development lifecycle (SDLC).<br> Objective: The goal of this study is to obtain a general view of the characteristics and usefulness of Early Software Defect Prediction (ESDP) models reported in scientific literature. <br> Method: A systematic mapping and systematic literature review study has been conducted. We searched for the studies reported between 2000 and 2016. We reviewed 52 studies and analyzed the trend and demographics, maturity of state-of-research, in-depth characteristics, success and benefits of ESDP models. <br> Results: We found that categorical models that rely on requirement and design phase metrics, and few continuous models including metrics from requirements phase are very successful. We also found that most studies reported qualitative benefits of using ESDP models.<br> Conclusion: We have highlighted the most preferred prediction methods, metrics, datasets and performance evaluation methods, as well as the addressed SDLC phases. We expect the results will be useful for software teams by guiding them to use early predictors effectively in practice, and for researchers in directing their future efforts.</p>
mTOR-iMCD-Blood_Article_Data_Repository
<p>This permanent Zenodo entry corresponds to the original data used for the manuscript: "<strong>Increased mTOR activation in idiopathic multicentric Castleman disease" </strong>accepted for publication by <a href="https://ashpublications.org/blood">Blood</a>. </p> <p>Original data for Figure 5 (proteomics) of the manuscript is available upon reasonable request to the corresponding author of the article.</p>
POM of 2326 archetypes in Maven central repository
<p>This dataset contains <em>pom.xml </em>files of 2326 archetypes in Maven central repository.</p> <p>The format of file name of each <em>pom.xml</em> is "<groupId>=<artifactId>=<version>=<orginal file name>", where "." in groupId is replace as "=".</p> <p>For example, the file name "am=ik=archetype=spring-boot-blank-archetype=0.9.0=spring-boot-blank-archetype-0.9.0.pom" represents: </p> <ul> <li>groupId: am.ik.archetype</li> <li>artifactId: spring-boot-blank-archetype</li> <li>version: 0.9.0</li> <li>pom.xml original filename: spring-boot-blank-archetype-0.9.0.pom</li> </ul> <p> </p>
Partnerships with TNCs to Better Serve the Transportation Disadvantaged Populations Data Repository
<p>Data repository for the STRIDE project A2 "Changing Access to Public Transportation and the Potential for Increased Travel," Thrust 2---Access for Transportation Disadvantaged Populations. </p>
Model data repository of "How sediment thickness influences subduction dynamics and seismicity"
<p>This repository provides the code and data to run the Seismo-Thermo-Mechanical model with a sediment thickness T<sub>sed</sub> of 4 km on a cluster using executables.</p>
The RISE of the Repository Rodeo and Developer Track
<p>Recording of OR2020 Repository Rodeo:<strong> </strong></p> <ul> <li> <p>Dataverse: Danny Brooke, Harvard University</p> </li> <li> <p>DSpace: Maureen Walsh, The Ohio State University</p> </li> <li> <p>EPrints: John Salter, University of Leeds</p> </li> <li> <p>Fedora: David Wilcox, LYRASIS</p> </li> <li> <p>Haplo: Tom Renner, Haplo</p> </li> <li> <p>Invenio: Lars Holm Nielsen, CERN</p> </li> <li> <p>Islandora: Mark Jordan, Simon Fraser University</p> </li> <li> <p>Samvera: Jon Dunn, Indiana University</p> </li> </ul> <p>Slides: http://doi.org/10.5281/zenodo.3875490</p> <p>And Developer track: </p> <ul> <li> <p>Developing COrDa: The COmmunity Orcid Dashboard; The powerful chamber: an institutional overview of the ORCID registry – Adam Vials Moore, JISC</p> </li> <li> <p>API driven applications with GraphQL; Building a modern repository UI with Elasticsearch, React and IIIF – Adam J. Arling, Northwestern University Libraries</p> </li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.