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410 results for “Data Repositories”
Data repository - BSc thesis Jara Schrandt
<p>This repository contains all data necessary to reproduce data of the BSc. thesis of Jara Schrandt [12645028]. Bsc. Future Planet Studies at the University of Amsterdam. Additionally, the thesis can be requested by contacting the author.</p>
Data repository for Pore Pressure Drop during Dynamic Rupture and Conditions for Dilatancy Hardening
<p>Simulation data to accompany publication Pore pressure drop during dynamic rupture and conditions for dilatancy hardening, submitted to Journal of Geophysical Research: Solid Earth. See Readme.txt for content of data files. The data were generated by 'GrandFrix' software and postprocessed by scripts written MATLAB – see Related identifiers.</p>
Simulated diagenesis of the iron-silica precipitates in banded iron formations: Data Repository
<p>This XRD dataset derives from experiments we performed bubbling 49 ppm O2 into simulated Archean seawater and then aging the produced precipitates at 25 degrees C, 80C, 150C, and 220C. Precipitate slurries were extracted from experimental samples and pipetted as 20 µL subsamples into Cole-Parmer Kapton tubes to keep anoxic during XRD analysis. Samples were sent to McMaster Analytical X-Ray Diffraction Facility (MAX) for XRD analysis using a Bruker D8 DISCOVER cobalt source tube (Co-XRD) with a DAVINCI.DESIGN diffractometer. More details on methods in associated article. Resultant XRD measurements of our samples yielded patterns showing increasing crystallinity with temperature. The bubbled experiment aged for 40 days at 25 °C produced a large and diffuse diffraction peak corresponding to the Kapton tube but no other sharp diffraction peaks, suggesting an amorphous to minimally crystalline product. A broad peak in the 25 °C precipitate, that persisted through the higher-temperature aging treatments, may correspond to ferrihydrite. The bubbled experiment aged at 80C contained diffraction peaks consistent with a serpentine group silicate and a spinel group oxide (like magnetite). After the 150 °C treatment, samples showed sharper peaks consistent with a serpentine group silicate and spinel group oxide. After 220 °C aging, the precipitates showed a continued narrowing of the diffraction peaks for a spinel group oxide, reflecting an increase in crystal size and/or crystallinity, but smaller and less sharp serpentine group peaks. </p>
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>
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.
Repository Analytics and Metrics Portal (RAMP) 2020 data
Open the record for dataset details and reuse information.
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>
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>
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>
Data accompanying the GitHub repository bartonlab/paper-MPL-inference
<p>This dataset contains data from simulations and analysis of within-host HIV-1 evolution that accompany the GitHub repository bartonlab/paper-MPL-inference. The GitHub repository contains code and data for reproducing results described in the manuscript "Fitness inference from complex evolutionary histories with genetic linkage." See the GitHub repository for details on the interpretation and analysis of this data.</p>
Data mining tool to discover DevOps trends from public repositories: Predicting Release Candidates with gthbmining.rc
<p>Public repositories have been performing an essential role in bring- ing software and services to technical communities and general users. Most of the cases, public repositories have a DevOps tool, with a live and historical database behind it, to support delivering and all steps this software or service should adopt before going to production. This paper introduces gthbmining, a data mining set of tools to discover DevOps trends from public repositories, and presents the module gthbmining.rc. Considering the premise of a GitHub public repository, the main contribution here is pre- dicting release candidates, an important label a software release has. The methodology, architecture, components and interfaces are explained, as well as potential users. The results show a reliable and flexible tool, as classifiers metrics and graphics are provided, along with the possibility to add new data mining algorithms in the open source module presented. Related works are also supplied, and a conclusion shows the outcomes gthbmining.rc can provide.</p>
EPSRC HEED Data Repository: Individual Appliance Monitoring System
<p>The dataset deposited here was prepared under the EPSRC-funded <a href="http://heed-refugee.coventry.ac.uk/">Humanitarian Engineering and Energy for Displacement</a> research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people. </p> <p>As part of the project, we deployed an Individual Appliance Monitoring (IAM) System in the Uttargaya settlement in Nepal. The Individual Appliance Monitoring System provides a simple, cost-effective and unobtrusive method of collecting data on the energy usage of connected appliances with the aim to: Evaluate energy consumption of different appliances and Evaluate usage patterns for different appliances.</p> <p>The monitoring system comprises of 2 types of devices – Energenie MiHome Smart Plugs MIHO005 (i.e. Individual Appliance Monitors (IAM)) to sense data relating to power and voltage drawn by the connected appliance, and gateway nodes to collect data from IAM. The unit cost of a MIHO005 adaptor and Raspberry Pi-based gateway is £34.99 and £74.17 respectively. The main component of the gateway node is a Raspberry Pi fitted with an Energenie ENER314-RT (receiver-transmitter) add-on board to allow the Pi to communicate with the smart plugs. The data collected by the gateway is stored locally in an SD card as well as sent to the heed-data server hosted in Coventry University.</p> <p>Post Deployment Challenges:</p> <p>· <strong>Power outages:</strong> These are common in the camp. Data is lost during this time as the devices have no access to power.</p> <p>· <strong>Internet connectivity: </strong>The availability and reliability of Wi-Fi continue to be an issue for the transmission of data to heed-data server.</p>
Data from: Multi-modal ultra-high resolution structural 7-Tesla MRI data repository
Structural brain data is key for the understanding of brain function and networks, i.e., connectomics. Here we present data sets available from the 'atlasing of the basal ganglia (ATAG)' project, which provides ultra-high resolution 7Tesla (T) magnetic resonance imaging (MRI) scans from young, middle-aged, and elderly participants. The ATAG data set includes whole-brain and reduced field-of-view MP2RAGE and T2*-weighted scans of the subcortex and brainstem with ultra-high resolution at a sub-millimeter scale. The data can be used to develop new algorithms that help building high-resolution atlases both relevant for the basic and clinical neurosciences. Importantly, the present data repository may also be used to inform the exact positioning of electrodes used for deep-brain-stimulation in patients with Parkinson's disease and neuropsychiatric diseases.
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.