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6,452 results for “Application”
Crosswalks IUCLID 6 v9 EU PPP Microorganisms - active substance application (product) to Data Requirements
<p>The <strong>Excel file</strong> provides detailed crosswalks from the current Table of Content (ToC) for Microbial Plant Protection Product (PPP) dossier in <a href="https://iuclid6.echa.europa.eu/it/home">IUCLID 6 v.9</a> to Commission Regulation (EU) 283/2013 and Commission Regulation (EU) 284/2013 as amended by Commission Regulation (EU) 2022/1439 & Commission Regulation (EU) 2022/1440.</p> <p>There are two worksheets:</p> <ul> <li><strong>ACTIVE SUBSTANCE</strong> (283-2013): mapping between the current IUCLID working context "EU PPP Microorganisms - active substance information" to the Commission Regulation (EU) No 283/2013 as amended by Commission Regulation (EU) 2022/1439.</li> <li><strong>PRODUCT</strong> (284-2013): mapping between the the current IUCLID working context "EU PPP Microorganisms - active substance application (product)" to the Commission Regulation (EU) No 284/2013 as amended by Commission Regulation (EU) 2022/1440.</li> </ul> <p>The spreadsheets contain the following columns:</p> <ul> <li><strong>Data Requirements Section (Commission Reguation (EU) 2022/1439 or 2022/1440)</strong>: the name of the ToC section (in accordance with the new data requirements).</li> <li><strong>IUCLID section</strong>: the name of the ToC section in IUCLID.</li> <li><strong>Endpoint study record</strong>: name of the document template used to report individual studies of the section. These usually correspond to <a href="https://www.oecd.org/en/topics/sub-issues/assessment-of-chemicals/harmonised-templates.html">OECD Harmonised Templates (OHT)</a>. </li> <li><strong>Endpoint summary</strong>: name of the document template used to report the summary information for the section endpoints.</li> <li><strong>Other IUCLID document</strong>: name of any other document template in IUCLID used to report information of the section. </li> <li><strong>OHT</strong>: number of the OECD Harmonised Template used in the section.</li> <li><strong>Additional context</strong>: fulI IUCLID paths indicating the section of the respective document where information needs to be provided and/or specific values to be indicated. </li> </ul> <p>Note: <span>in cases where an IUCLID document is not included in the updated ToC this will be found in a specific section 'Documents applicable to the former data requirements' which can be found at the end of the dataset.</span></p> <p><strong>Version 5 </strong>includes changes in the table of contents of <a href="https://iuclid6.echa.europa.eu/it/home">IUCLID 6 v9</a>.</p> <p> </p> <p> </p>
Crosswalks IUCLID 6 v9 EU PPP Active substance application (product) to KCA&KCP
<p>This Excel file provides detailed crosswalks from the <a href="https://esubmission.croplifeeurope.eu/registration-for-download/">EU Table of Contents (SANCO/10181/2013)</a> for plant protection product (PPP) dossiers to <a href="https://iuclid6.echa.europa.eu/it/home">IUCLID 6 v9</a>. It includes two spreadsheets, containing the mappings for active substance (as laid out in Commission Regulation (EU) No 283/2013) and representative product (Commission Regulation (EU) No 284/2013):</p> <ul> <li><strong>EU_PPP_ActiveSubstance</strong>: mapping between the "KCA" ToC and IUCLID 6 v9 working context "EU PPP Active substance information"</li> <li><strong>EU_PPP_Product</strong>: mapping between the "KCP" ToC and IUCLID 6 v9 working context "EU PPP Active substance application (product)"</li> </ul> <p>The spreadsheets map each section of the original EU ToC to:</p> <ul> <li><strong><em>IUCLID section</em></strong>: name of the section in IUCLID where to input the corresponding data/information</li> <li><em><strong>Endpoint study record</strong></em>: name of the document template used to report individual studies of the section (if exists). These usually correspond to <a href="https://www.oecd.org/en/topics/sub-issues/assessment-of-chemicals/harmonised-templates.html">OECD Harmonised Templates (OHT)</a>.</li> <li><em><strong>Endpoint summary</strong></em>: name of the document template used to report the summary information of the studies presented in the section (if available)</li> <li><em><strong>Other IUCLID document</strong></em>: name of any other document template in IUCLID used to report information of the section (if available)</li> <li><em><strong>OHT</strong>:</em> name/id of the OECD Harmonised Template used for the endpoint study record document (if available)</li> <li><em><strong>Additional context</strong></em>: fulI IUCLID paths indicating the section of the respective document where information needs to be provided and/or specific values to be indicated. If more than one path is provided, these are separated by ";". Operators "=" and "!=" are used to indicate values to be used and to be avoided, respectively. When more than one value is possible, "IN" and "NOT IN" operators are used, followed by the exhaustive list of possible values. This nomenclature was defined with the aim of making contents machine-readable.</li> </ul> <p><strong>Version 6 </strong>includes changes in the table of contents of IUCLID 6 v9.</p>
Computationally directed manipulation of cross-linked covalent organic frameworks for membrane applications - PCCP
<p>The dataset uploaded herein is associated with the paper published under the title "<i>Computationally directed manipulation of cross-linked covalent organic frameworks for membrane applications</i>" with the Royal Society of Chemistry - Physical Chemistry Chemical Physics Journal. This dataset includes the .vasp files for all modeled structures, an example dftb_in.hsd file, which is the instructional file for geometry optimization with DFTB+, an Excel spreadsheet with atom number densities and total energy values for all modeled geometries, and, finally, a Python script that was used to calculate the Enthalpy of Formation and Cohesive Energies for all structures. This data has been made available to the scientific community in the interest of open-source and accessible data. The authors request that you please cite the associated paper and Zenodo dataset if used.</p><p><strong>Abstract</strong></p><p>Two-dimensional covalent organic frameworks (2D-COFs) exhibit characteristics ideal for membrane applications, such as high stability, tunability and porosity along with well-ordered nanopores. However, one of the many challenges with fabricating these materials into membranes is that membrane wetting can result in layer swelling. This allows molecules that would be excluded based on pore size to flow around the layers of the COF, resulting reduced separation. Cross-linking between these layers inhibits swelling to improve the selectivity of these membranes. In this work, computational models were generated for a quinoxaline-based COF cross-linked with oxalyl chloride (OC) and hexafluoroglutaryl chloride (HFG). Enthalpy of formation and cohesive energy calculations from these models show that formation of these COFs is thermodynamically favorable and the resulting materials are stable. The cross-linked COF with HFG was synthesized and characterized with Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis with differential scanning calorimetry (TGA-DSC), and water contact angles. Additionally, these frameworks were fabricated into membranes for permeance testing. The experimental data supports the presence of cross-linking and demonstrates that varying the amount of HFG used in the reaction does not change the amount of cross-linking present. Computational models indicate that the effect of varying cross-linking concentration on the framework stability is negligible and less cross-linking still results in stable materials. This work sheds light on the nature of the cross-linking in these 2D-COFs and their application in membrane separations.</p>
Dataset: Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications
<p>Dataset used in the paper "Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications"</p>
Replication data for An Empirical Approximation of the Effects of Trade Sanctions with an Application to Russia
<p>This is the dataset to replicate all the tables and figures in the paper <a href="https://doi.org/10.1093/epolic/eiad027">"An Empirical Approximation of the Effects of Trade Sanctions with an Application to Russia"</a>, published in <i>Economic Policy</i>, 2023, by Jean Imbs and Laurent Pauwels. All data manipulations and programming are detailed on the GitHub site:<a href="https://github.com/laurentpauwels/sanctionpaper"> https://github.com/laurentpauwels/sanctionpaper</a>. The raw and processed data are in this <i>sanctionpaperdata_v1/matlab/data folder. </i>For convenience the simulation output <i>(simulationoutput.txt) </i>required to build the scatter plots in Figure 1 with STATA is available in<i> sanctionpaperdata</i>_v1<i>/matlab/output</i>.</p><p><strong>Instructions</strong> </p><p> If you clone the GitHub repository:</p><p>1. Place the downloaded <i>data</i> folder (located in <i>sanctionpaperdata_v1/matlab/)</i> in the <i>matlab</i> folder of the GitHub repository. </p><p>2. Place the downloaded <i>simulation_output.txt</i> I(located in <i>sanctionpaperdata_v1/matlab/output/) </i>in the <i>matlab/output </i>folder of the GitHub repository if you do not want to run the simulations as detailed on GitHub.</p><p><strong>Description</strong></p><p>The <i>matlab/data/raw</i> folder contains an <i>ICIO21</i> folder with the ICIO21 data, and a <i>WIOD</i> folder with the SEA16 data (in <i>data/raw/WIOD/SEA16</i>) and the WIOT16 data in CSV format (in <i>data/raw/WIOD/WIOT16</i>).</p><p>NOTE: WIOD provides the data in XLSB format. The XLSB WIOD data is in the <i>WIOT_in_EXCEL.zip</i> located in the <i>matlab/data/raw/WIOD/</i>. Python is used to convert XLSB into CSV files. See python code in GitHub repository for unzipping and conversion to CSV. The converted CSV files are provided for convenience.</p><p>The parsed and pre-processed ICIO21, SEA16, and WIOT16 data are stored in the <i>/matlab/data/processed</i> folder into three separate .mat structure files:</p><p><i>icio21_strc.mat</i> contains:</p><ul><li>the meta data (<i>icio21_text</i>), i.e., the information about the structure of the numerical data such as lists of countrycode, countries, industrycode, industries, isic_rev4 codes, years covered, name of final categories, etc.</li><li>the numerical data (<i>icio21_data</i>):<ul><li>Z (<i>icio21_data.Z</i>), the intermediate IO data for the listed industries (R), countries (N), and years (T). Its structure is 3-dimensionsal: (NxR)x(NxR)xT.</li><li>F (<i>icio21_data.F</i>), the final demand data for the same countries, industries and years. Its structure is 3-dimension: (NxR)x(NxC)xT. The columns are NxC where C are the number of final demand categories.</li></ul></li></ul><p><br><i>wiod16_strc.mat</i> has the same structure as <i>icio21_strc.ma</i>t with the meta data in <i>wiot16_text</i> and the numerical data in <i>wiot16_data</i>.</p><p><i>sea16_strc.mat</i> has the meta data in <i>sea16_text</i> and the numerical data in <i>sea16_data</i>. SEA16 contains 16 variables instead of Input-Output type data. The country, industry, and year coverage is not the same as ICIO21.</p><p>NOTE: <i>matlab/scripts/convertMatlabStruc2data.m</i> in the GitHub repository converts <i>MATLAB v7.3 </i>format ("structure data") to an updated format without structure so that it is more easily compatible with other software. All data parsing and preprocessing are done with MATLAB, see GitHub repository for details.</p><p><strong>Sources</strong></p><p>The raw data come from these sources:</p><p>1. OECD Inter-Country Input-Output (ICIO) data November 2021 release (downloaded on 2 July 2023)</p><p>- Source: OECD-ICIO 2021 release data is available at <a href="http://oe.cd/icio">http://oe.cd/icio</a></p><p>2. WIOD Socio-Economic Accounts (SEA) data 2016 release (downloaded on 30 May 2023)</p><p>- Source: <a href="https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release">https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release</a></p><p>3. WIOD World Input-Output Tables (WIOT) data November 2016 (downloaded on 23 June 2023)</p><p>- Source: <a href="https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release">https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release</a> </p>
CAIRT/IASI-NG/CAIRT+IASI-NG FL2S results of Case Study Scenario 4 for limb-nadir application
<p>Results of the fast level-2 simulator (FL2S) for Case Study Scenarios 4 (only SO2 files) for limb-nadir application. The files contain altitude-time cross-sections of atmospheric parameters along simulated CAIRT-orbits. The variable extensions denote the original field ('_ori'), the application of the averaging kernel ('_ak') and additional application of noise ('_aknoi') for CAIRT alone, IASI-NG alone, and the combined product CAIRT+IASI-NG.</p>
Optimized SMRT-UMI protocol produces highly accurate sequence datasets from diverse populations – application to HIV-1 quasispecies
<p>Pathogen diversity resulting in quasispecies can enable persistence and adaptation to host defenses and therapies. However, accurate quasispecies characterization can be impeded by errors introduced during sample handling and sequencing which can require extensive optimizations to overcome. We present complete laboratory and bioinformatics workflows to overcome many of these hurdles. The Pacific Biosciences single molecule real-time platform was used to sequence PCR amplicons derived from cDNA templates tagged with universal molecular identifiers (SMRT-UMI). Optimized laboratory protocols were developed through extensive testing of different sample preparation conditions to minimize between-template recombination during PCR and the use of UMI allowed accurate template quantitation as well as removal of point mutations introduced during PCR and sequencing to produce a highly accurate consensus sequence from each template. Handling of the large datasets produced from SMRT-UMI sequencing was facilitated by a novel bioinformatic pipeline, Probabilistic Offspring Resolver for Primer IDs (PORPIDpipeline), that automatically filters and parses reads by sample, identifies and discards reads with UMIs likely created from PCR and sequencing errors, generates consensus sequences, checks for contamination within the dataset, and removes any sequence with evidence of PCR recombination or early cycle PCR errors, resulting in highly accurate sequence datasets. The optimized SMRT-UMI sequencing method presented here represents a highly adaptable and established starting point for accurate sequencing of diverse pathogens. These methods are illustrated through characterization of human immunodeficiency virus (HIV) quasispecies.</p>
Application of Close-Range SfM Photogrammetry on three bases of Etruscan Bronze Candelabra from Spina Necropolises
<p>Application of Close-Range SfM Photogrammetry on three bases of Etruscan Bronze Candelabra from Spina Necropolises. </p> <p>This dataset contains the photogrammetry made 3D models of three Etruscan Bronze Candelabra found at Spina, dated to the 4th cent. BC, kept in the Archaeological Museum of Ferrara.</p> <p>The models were made through Agisoft Metashape from previously taken RAW photos, at the National Archaeological Museum of Ferrara, for master thesis purposes. Some minor issues of the meshes were solved using Blender before texturing the models.</p> <p>The folders are divided according to the tombs of provenance of the candelabra (T. 545, T. 1122, T. Unknown) and every folder contain:</p> <ul> <li>The OBJ</li> <li>The texture</li> <li>An additional folder with the reports generated from Agisoft Metashape</li> </ul>
Predators of Japanese myriapods — survey using literature, social media, Web, and mobile application —
<p>I provide a database reviewing information on predators of Japanese myriapods. The information within this database was compiled from published accounts in Japan along with visual media accumulated on the Internet.</p>
DREAMING - Diminished Reality for Emerging Applications in Medicine through Inpainting Dataset
<p>Dataset for the <strong>DREAMING - Diminished Reality for Emerging Applications in Medicine through Inpainting Challenge</strong></p> <p>More information about the challenge and challenge registration can be found on the <a href="https://dreaming.grand-challenge.org/" target="_blank" rel="noopener">challenge platform</a>!</p> <p> </p> <p><em><strong>Timeline:</strong></em></p> <p><strong>8th January 2024: </strong>First subset of training & validation data available (scenes 0000-0009).</p> <p><strong>22nd January 2024:</strong> Second subset of training & validation data available (scenes 0010-0049).</p> <p><strong>26th January 2024:</strong> Full training & validation data available (scenes 0050-0100).</p> <p> </p> <p><strong>Description:</strong></p> <p>The dataset was created using <a href="https://www.unrealengine.com">Unreal Engine 5.1</a>, <a href="https://www.unrealengine.com/en-US/metahuman">Unreal MetaHumans</a>, <a href="../records/10091715" target="_blank" rel="noopener">3D-COSI surgical instruments</a>, <a href="https://github.com/BatFaceWayne/POV_Surgery" target="_blank" rel="noopener">POV-Surgery grasp generation</a> and <a href="https://github.com/ydrive/EasySynth" target="_blank" rel="noopener">EasySynth</a>.</p> <p>Each scene contains:</p> <ul> <li>"color": RGB input images</li> <li>"gt": RGB ground truth images</li> <li>"mask": Mask defining the area to be inpainted. White -> Background, Black -> Inpainting area</li> <li>"CameraPoses.csv": Camera poses</li> <li>"CameraRig.json": Camera intrinsics</li> </ul> <p>To cite the dataset, please use</p> <pre><code>@online{gsaxner_2024_10570773, author = {Gsaxner, Christina and van Meegdenburg, Timo and Luijten, Gijs and Vu, Viet Duc and Puladi, Behrus and Egger, Jan}, title = {{DREAMING - Diminished Reality for Emerging Applications in Medicine through Inpainting Dataset}}, month = jan, year = 2024, publisher = {Zenodo}, doi = {10.5281/zenodo.10570773}, url = {https://doi.org/10.5281/zenodo.10570773} }</code></pre> <p> </p>
Application of LiDAR to assess the habitat selection of an endangered small mammal in an estuarine wetland environment
<p>Light detection and ranging (lidar) has emerged as a valuable tool for examining the fine-scale characteristics of vegetation. However, lidar is rarely used to examine coastal wetland vegetation or the habitat selection of small mammals. Extensive anthropogenic modification has threatened the endemic species in the estuarine wetlands of the California coast, such as the endangered salt marsh harvest mouse (<em>Reithrodontomys raviventris</em>; SMHM). A better understanding of SMHM habitat selection could help managers better protect this species. We assessed the ability of airborne topographic lidar imagery in measuring the vegetation structure of SMHM habitats in a coastal wetland with a narrow range of vegetation heights. We also aimed to better understand the role of vegetation structure in habitat selection at different spatial scales. Habitat selection was modeled from data compiled from 15 small mammal trapping grids collected in the highly urbanized San Francisco Estuary in California, USA. Analyses were conducted at three spatial scales: microhabitat (25 m<sup>2</sup>), mesohabitat (2,025 m<sup>2</sup>), and macrohabitat (10,000 m<sup>2</sup>). A suite of structural covariates was derived from raw lidar data to examine vegetation complexity. We found that adding structural covariates to conventional habitat selection variables significantly improved our models. At the microhabitat scale in managed wetlands, SMHM preferred areas with denser and shorter vegetation, and selected for proximity to levees and taller vegetation in tidal wetlands. At the mesohabitat scale, SMHM were associated with a lower percentage of bare ground and with pickleweed (<em>Salicornia pacifica</em>) presence. All covariates were insignificant at the macrohabitat scale.<em> </em>Our results suggest that SMHM preferentially selected microhabitats with access to tidal refugia and mesohabitats with consistent food sources. Our findings showed that lidar can contribute to improving our understanding of habitat selection of wildlife in coastal wetlands and help to guide future conservation of an endangered species.</p>
Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal Passive Acoustic Monitoring datasets
<p>Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches, and indices are best suited for characterizing marine and terrestrial acoustic environments.</p> <p>Here, we describe the application of multiple machine-learning techniques to the analysis of a large PAM dataset. We combine pre-trained acoustic classification models (VGGish, NOAA & Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine environment.</p> <p>The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labelled sounds in the 8 kHz range, however, low and high-frequency sounds could not be classified using this approach.</p> <p>The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow for establishing a link between ecologically relevant information and PAM recordings at multiple scales.</p> <p>The datasets and scripts provided in this repository allow replicating the results presented in the publication. </p>
Data and code for figures: Design, fabrication and characterization of kinetic-inductive force sensors for scanning probe applications
<p>This directory contains the datasets, code (if applicable) for measurement libraries, data processing and figure generation for the research article "Design, fabrication and characterization of kinetic-inductive force sensors for scanning probe applications", Beilstein J. Nanotechnol. 2024, 15, 242-255.</p>
An Approach Based on an Increased Bandpass for Enabling the Use of Internal Standards in Single Particle ICP-MS: Application to AuNPs Characterization
<p>This dataset contains the raw data corresponding to the figures of the publication https://doi.org/10.3390/nano13121838</p>
Datasets for Paper "MetagenomicKG: a knowledge graph for metagenomic applications"
<p>This repository contains some required data that is used for building MetagenomicKG. Please see more details in <a href="https://github.com/KoslickiLab/MetagenomicKG">https://github.com/KoslickiLab/MetagenomicKG</a>.</p>
Dataset used in the publication "Using of Transformers Models for Text Classification to Mobile Educational Applications"
<p>Dataset used in the publication "Using of Transformers Models for Text Classification to Mobile Educational Applications".</p> <p>More info about the dataset can be found in the published article.</p>
On the use of a consumer-grade 360-degree camera as a radiometer for scientific applications: calibration dataset
<p>Studying the geometric distribution of the light field in terms of absolute radiometry required expensive and complex instruments. New types of compact 360-degree cameras have recently appeared on the consumer technology market. Some of these allow users to access raw imagery, offering sensor-level data that can be directly exploited for absolute light quantification. This paves the way for easy-to-use, inexpensive and accessible radiance cameras that can be operated in a wide range of natural environments. </p> <p>This dataset presents raw format images captured with the camera Insta360 ONE for its calibration and characterization. These experiments include geometric calibration, relative illumination evaluation, spectral response determination, absolute spectral radiance calibration, as well as linearity and dark frame analysis. In addition, we are providing data from a calibration validation experiment based on co-located measurements of the sky's downward radiance using a 360-degree calibrated camera and a scientific radiometer: the Compact Optical Profiling System (C-OPS, Biospherical Instruments Inc.).</p> <p>This repository contains raw files as taken by the camera's imaging sensor. In most cases, no data processing has been carried out. The entire data set is contained in a .zip file which includes the following sub-folders (in alphabetical order):</p> <ul> <li><strong>absolute-radiance</strong>: data (.dng, .tsv , .hdf5) acquired for <em>absolute spectral radiance</em> calibration</li> <li><strong>darkframe: </strong>DNG raw images taken for <em>dark frame</em> analysis</li> <li><strong>geometric: </strong>DNG images used for the <em>geometric calibration</em></li> <li><strong>immersion-factor: </strong>DNG images for the calculation of the immersion factor</li> <li><strong>linearity:</strong> DNG images for <em>linearity</em> assessment (gain and exposure time)</li> <li><strong>relative-illumination: </strong>DNG images for <em>roll-off (relative illumination) </em>characterization</li> <li><strong>relative-spectral-response: </strong>data (.asc, .dng) taken for relative spectral response characterization</li> <li><strong>verification&validation: </strong>time series data (.tsv, .dng) of the calibration validation experiment </li> </ul> <p>Each folder contains a <strong>README </strong>file explaining the additional subfolders and their files. As you may notice, some of the subfolders are named "lensclose" or "lensfar". They refers to the data acquired with the fish-eye optic assembly that is closer or farther from the top of the 360-degree camera respectively. Some calibrations were performed both in water and in air (geometric calibration, relative-illumination). In that regard, the images were placed in folders refering to "air" and "water". The routines (coded in python) for the data processing can be found in the following <a href="https://github.com/RaphaelLarouche/radiance_camera_insta360/tree/master_v01">Github repository</a> (master_v01) or the <a href="../records/4660994">Zenodo stored version</a>. The useful scripts are located in the <em>calibration</em> directory, and the<strong> README</strong> for each folder points to the revelant code for analysis of the files they contain. For additional information, all the methodologies are described in the <a href="https://arxiv.org/abs/2305.07103">arXiv preprint</a>. </p>
Data for a publication: "A new promising material for biological applications: multi-level physical modification of AgNPs-decorated PEEK"
<p>The data set contains the data that were used within the article "A New Promising Material for Biological Applications: Multilevel Physical Modification of AgNP-Decorated PEEK" published in the journal Nanomaterials.</p> <p><strong>Versions:</strong></p> <p><strong>V1&2</strong>: The dataset contained AFM and UV VIS data.</p> <p><strong>V3</strong>: All the data used in the article are sorted in the file structure, which description is thoroughly described in the READ ME file. Additionally, the preprint DOI was added.</p> <p> </p> <p> </p>
Supplementary Data for ZeroPM D2.3 - Lessons learnt from previous applications of the essential-use concept
<p>This dataset contains the data collected for all the studies which are mentioned in the ZeroPM deliverable D2.3 titled "Lessons learnt from previous applications of the essential-use concept".</p>
REACH_OpenCall_ Applicants_Open_Database
<p>This anonymised database covers Open Calls 1, 2 and 3 of REACH Incubator. </p>
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.