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410 results for “Data Repositories”
juan9715/MRA-Bender-Element-data: Bender data public repository release
<p>Bender data public repository release</p>
Data repository for Jarvis et al. (2023): The probabilistic nature of dune collisions in 2D"
<p>This is a data repository associated with Jarvis et al. (2023) "The probabilistic nature of dune collisions in 2D" published in Earth Surface Dynamics.<br> </p>
Data accompanying the GitHub repository bartonlab/paper-HIV-latent-reservoir
<p>This dataset contains data from simulations of the HIV-1 dynamics that accompany the GitHub repository bartonlab/paper-HIV-latent-reservoir. The GitHub repository contains code for reproducing results described in the manuscript 'Clonal heterogeneity and antigenic stimulation shape persistence of the latent reservoir of HIV'. See the GitHub repository for details.</p>
Polycystic Kidney Disease Data Repository
ClinicalTrials.gov study NCT00792155. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Cardiac Acute Transitioning Care to Home (CATCH) App Data Repository
ClinicalTrials.gov study NCT04498728. IPD Sharing: NO. Countries: 1. Publications: 3.
Collection of Samples and Data for the National Marrow Donor Program Repository
ClinicalTrials.gov study NCT00495300. IPD Sharing: Not stated. Countries: 1. Publications: 3.
SC2i Tissue and Data Repository Protocol
ClinicalTrials.gov study NCT02182180. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Ballistic Microscopy (BaM) data repository
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Data from: A collection of non-human primate computed tomography scans housed in MorphoSource, a repository for 3D data
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Repositories for taxonomic data: Where we are and what is missing
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Data repository: Cytotype distributions of the common Dandelion (Taraxacum section Ruderalia) and the Cuckoo flower (Cardamine pratensis) in Europe and how these are affected by climate change
<p>This map contains all datasets and R-scripts used to write the paper with the same name. This map also contains the original research proposal on which the datasets are based. The datasets contain location data on the cytotypes of the common Dandelion and the Cuckoo flower in Europe. The data has been collected using a meta-analysis. The R-scripts present are for data preparation, SDM's for current and future scenarios and bioclimatic variable preparation.</p>
Data repository from Boudewijn van Lieshout' thesis A comparison between Normalized Difference Vegetation Indices calculated from Sentinel 2A satellite data and high-resolution UAV imagery in Tanzania
<p>Monitoring vegetation is imperative for policy design and efficiency measurements. Frequent data collection, easy and inexpensive accessibility of images and the possibility of large area analysis, makes it still valuable to use satellite imagery. This study aims to examine how a Normalized Difference Vegetation Index (NDVI) measured with Sentinel-2A satellite data relates to an NDVI from Unmanned Aerial Vehicles (UAV henceforth) in study areas Chamwino Mlimwa and Chemba Waida, Tanzania.</p>
Systematic Mapping Study on Domain-Specific Language Development Tools - Data Repository
<p>Domain-specific languages (DSL) are programming or modeling languages devoted to a given application domain. There are many tools used to support the implementation of a DSL, making hard the decision-making process for one or another. In this sense, identifying and mapping their features is relevant for decision-making by academic and industrial initiative on DSL development. <br> Objective: The goal of this work is to identify and map the tools, Language Workbenches (LW), or frameworks that were proposed to develop DSLs discussed and referenced in publications between 2012 and 2019. <br> Method: A Systematic Mapping Study (SMS) of the literature scoping tools for DSL development. <br> Results: We identified 59 tools, including 9 under a commercial license and 41 with non-commercial licenses, and analyzed their features from 230 papers. <br> Conclusion: There is a substantial amount of tools that cover a large number of features. Furthermore, we observed that usually, the developer adopts one type of notation to implement the DSL: textual or graphical. We also discussed research gaps, such as a lack of tools that allow meta-meta model transformations and that support modeling tools interoperability. </p>
Audience data from 17 research data repositories between 2015 and 2020
<p>The file compiles audience data from 17 research data repositories between 2015 and 2020. It was generated as part of a study to quantify the consultation of data deposited in scientific repositories. This study gave rise to a paper at the seventh conference "Digital Document and Society" (Document numérique & Société), held on 28 and 29 September 2020 in Nancy (France).</p> <p>This table gathers the following information for each of the 17 repositories:</p> <ul> <li>Number of data available in the repository;</li> <li>Number of pages views per month and per year;</li> <li>Number of users per month and per year;</li> <li>Number of downloads per month and per year;</li> <li>Number of data citations in the scientific literature per year.</li> </ul> <p>The collection method is described in the above-mentioned paper.</p>
Data Repository: Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes
<p>Data repository for the scMappR manuscript:</p> <p>Abstract from biorXiv (https://www.biorxiv.org/content/10.1101/2020.08.24.265298v1.full).</p> <p>RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by integrating cell-type expression data generated by scRNA-seq and existing deconvolution methods. After benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. We found that scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small proportion of immune cells. While scMappR can work with any user supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its use with bulk RNA-seq data alone. Overall, scMappR is a user-friendly R package that complements traditional differential expression analysis available at CRAN.</p>
Data from: The location of the citation: changing practices in how publications cite original data in the Dryad Digital Repository
While stakeholders in scholarly communication generally agree on the importance of data citation, there is not consensus on where those citations should be placed within the publication – particularly when the publication is citing original data. Recently, CrossRef and the Digital Curation Center (DCC) have recommended as a best practice that original data citations appear in the works cited sections of the article. In some fields, such as the life sciences, this contrasts with the common practice of only listing data identifier(s) within the article body (intratextually). We inquired whether data citation practice has been changing in light of the guidance from CrossRef and the DCC. We examined data citation practices from 2011 to 2014 in a corpus of 1,125 articles associated with original data in the Dryad Digital Repository. The percentage of articles that include no reference to the original data has declined each year, from 31% in 2011 to 15% in 2014. The percentage of articles that include data identifiers intratextually has grown from 69% to 83%, while the percentage that cite data in the works cited section has grown from 5% to 8%. If the proportions continue to grow at the current rate of 19-20% annually, the proportion of articles with data citations in the works cited section will not exceed 90% until 2030.
Data from: Effect of arsenate substitution on phosphate repository of cell: a computational study
The structural analogy with phosphate derives arsenate into various metabolic processes associated with phosphate inside the organisms. But it is difficult to evaluate the effect of arsenate substitution on the stability of individual biological phosphate species, which span from a simpler monoester form like pyrophosphate to a more complex phosphodiester variant like DNA. In this study, we have classified the physiological phosphate esters into three different classes on the basis of their structural differences.This classification has helped us to present a concise theoretical study on the kinetic stability of phosphate analogue species of arsenate against hydrolysis. All the calculations have been carried out using QM/MM methods of our Own N-layer Integrated molecular Orbital molecular Mechanics. For quantum mechanical region we have used M06-2X density functional with 6-31+G(2d,2p) basis set and for molecular mechanics region AMBER force field. The calculated rate constants for hydrolysis show that none of the phosphate analogue species of arsenate has a reasonable stability against hydrolysis.
Nordic44 - 2015 Powerflow Data: An Open Data Repository of an Equivalent Nordic Grid Model Matched to Historical Electricity Market Data for 2015
<p>This repository is used to provide documentation related to the model and data development process, provide source (raw) data for the model in different forms (i.e. Modelica, CIM 14, and PSS/E) for an equivalent Nordic grid model that has been matched to historical power flow data.</p> <p>The repository is documented in the paper below, see [Ref00].</p> <p><strong>Using this model, data or related software = cite our publications!</strong></p> <p>We are happy to contribute with this dataset, however, if you use any of the data or software provided, we will appreciate if you cite the following publications, as follows:</p> <p>A) Cite that "the raw and processed data files corresponding to the model are available as an open data set and documented in [Ref00]."</p> <p>B) Cite that the first appearance of the model, i.e. "the model is first presented in [Ref01]"</p> <p>[Ref00] L. Vanfretti, S.H. Olsen, V. S. Narasimham Arava, G. Laera, A. Bibadafar, T. Rabuzin, H. Jackobsen, J. Lavenius, and M. Baudette, "An Open Data Repository and a Data Processing Software Toolset of an Equivalent Nordic Grid Model Matched to Historical Electricity Market Data," submitted for publication, Data in Brief, 2016.</p> <p>[Ref01] L. Vanfretti, T. Rabuzin, M. Baudette, M. Murad, iTesla Power Systems Library (iPSL): A Modelica library for phasor time-domain simulations, SoftwareX, Available online 18 May 2016, ISSN 2352-7110, http://dx.doi.org/10.1016/j.softx.2016.05.001.</p> <p><strong>Acknowledgment:</strong></p> <p>This model was originally developed in the context of the FP7 iTesla project, and further extended within the ITEA3 openCPSproject.</p> <p>Structure of the repository:</p> <p><strong>01_PSSE_Resources</strong>:</p> <ol> <li> <p><strong>Models</strong> :</p> <ul> <li> <p>A folder with PSS/E files of the base case</p> </li> <li> <p>A folder with a 7zip archive containing files of the original N44 system that has been modified to have the PSS/E base case</p> </li> </ul> </li> <li> <p><strong>Snapshots</strong> :</p> <ul> <li> <p><strong>N44_2015xxxx</strong> are folders named according to the day they refer to (for example <em>N44_20150401</em> refers to the 1st of April 2015). In each folder there are Excel files (<em>Consumption_xx.xlsx</em>, <em>Exchange_xx.xlsx</em>, <em>Production_xx.xlsx</em>) with data downloaded from Nord Pool website, an Excel file (<em>PSSE_in_out.xlsx</em>) summarizing the results from the Python script <em>Nordic44.py</em> in the folder <strong>04_Python_Resources</strong>, PSS/E snapshots for each hour before solving the power flow (<em>hx_before_PF.raw</em>) and after solving the power flow (<em>hx_after_PF.raw</em>)</p> </li> <li> <p><em>N44_BC.sav</em> is the PSS/E solved base case that Python script <em>Nordic44.py</em> (put the reference)</p> </li> </ul> </li> </ol> <p><strong>02_CIM14_Snapshots</strong>:</p> <ul> <li> <p><strong>N44_2015xxxx</strong> are folders named according to the day they refer to (e.g. <strong>N44_20150401</strong> refers to the 1st of April 2015). In each folder there are CIM files for each hour (<em>N44_hx_EQ.xml</em>, <em>N44_hx_SV.xml_, _N44_hx_TP.xml</em>)</p> </li> <li> <p><strong>N44_noOL_RDFIDMAP.xml</strong> is the file with IDs mapping of those cases (<em>N44_hx_noOL_EQ.xml</em>, <em>N44_hx_noOL_SV.xml</em>, <em>N44_hx_noOL_TP.xml</em>) with fixed overloading problems.</p> </li> <li> <p><strong>N44_RDFIDMAP_2015-1.xml</strong> and <strong>N44_RDFIDMAP_2015-2.xml</strong> are the files with IDs mapping of the remaining snapshots from 2015</p> </li> </ul> <p><strong>03_Modelica</strong>:</p> <ol> <li> <p><strong>iTesla_Platform</strong></p> <ul> <li> <p><strong>iPSL</strong> folder contains the version of the library which can be used to simulate snapshots generated from the iTesla Platform</p> </li> <li> <p><strong>Modelica_snapshots</strong> Modelica models generated from the snapshots by iTesla Platform</p> </li> </ul> </li> <li> <p><strong>SmarTSLab</strong></p> <ul> <li> <p><strong>OpenIPSL</strong> folder contains the version of the forked iPSL library which can be used to simulate the manually generated Modelica model of N44 with the record structures corresponding to the snapshots</p> </li> <li> <p><strong>Snapshots</strong> folder contains Modelica records automatically generated from the PSS/E records</p> </li> <li> <p><em>N44_Base_Case.mo</em> is the handmade N44 model with the loaded record of the power flow results from the PSS/E base case. It can be used to load other PF results from the folder <strong>03_Modelica/Snapshots</strong></p> </li> </ul> </li> </ol>
Data Repository - From net-zero to zero-fossil in transforming the EU energy system
<p>This is the data repository to reproduce the analysis of the manuscript "From net-zero to zero-fossil in transforming the EU energy system", which is currently under review for publication in a scientific journal. </p> <p>The source code for the REMIND version used in this study is available at <a href="https://github.com/fschreyer/remind/tree/FossilFree_master">https://github.com/fschreyer/remind/tree/FossilFree_master.</a> The scenario config file that was used to start the specific model runs of the analysis including all scenario-specific model settings can be found in the repository under <a href="https://github.com/fschreyer/remind/blob/FossilFree_master/config/scenario_config_fossilfree.csv">./config/scenario_config_fossilfree.csv</a>. The repository is a fork with slight changes relative to the main release version available at <a href="https://github.com/remindmodel/remind/tree/v3.3.1">https://github.com/remindmodel/remind/tree/v3.3.1</a> and <a href="https://zenodo.org/records/12104410">https://zenodo.org/records/12104410</a>. A general model documentation can be found at <a href="https://rse.pik-potsdam.de/doc/remind/3.2.0">https://rse.pik-potsdam.de/doc/remind/3.2.0</a>. </p> <p>The data repository contains the following files:</p> <p>data</p> <ul> <li>FossilFree_Plot.Rmd - R markdown file used for the analysis.</li> <li>AllScenarioData.mif - Data file containing REMIND scenario output data used for the analysis.</li> <li>MainFigures.xlsx - Data file containing all data plotted in main figures of the text.</li> <li>SIFigures.xlsx - Data file containing all data plotted in extended data figures and supplementary figures of the text.</li> <li>data folder: containing data and mapping files used for the FossilFree_Plot.Rmd script</li> <li>scripts folder: containing additional scripts and functions used in the FossilFree_Plot.Rmd script</li> </ul> <p>Note that we cannot provide the AR6 scenario data here. Please refer to <a href="https://zenodo.org/records/7197970">https://zenodo.org/records/7197970</a>. </p> <p>Disclaimer: We here publish a comprehensive dataset of our model output which includes more data than what is needed to reproduce the figures of the paper. Those data can be helpful to compare and contextualize our scenarios or use them for further analyses. However, due to the scope and complexity of our modeling framework, these data need to be used with care. The data used for the analysis of this study have been thoroughly validated. However, we cannot always perform such validation for the whole dataset and data need to treated with caution in particular at high regional or sectoral resolution and with respect to aspects that were not in the focus of the study as there maybe artefacts or limitations of our modeling approach. Please contact us in case you would like to use our scenarios for further analyses. We welcome open and constructive exchange on our data. </p> <p>Contact:<br>Felix Schreyer<br>Potsdam Institute for Climate Impact Research<br>felix.schreyer@pik-potsdam.de</p>
Data repository for 'Parity-conserving Cooper-pair transport and ideal superconducting diode in planar Germanium'
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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.