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6,782 results for “project”
NextGenProteins Project video & Event Film
Open the record for dataset details and reuse information.
Global mean sea-level projections from MimiBRICK forced by uncertain emissions trajectories
<p>Model output for Darnell et al (2025), . Output contains csv files for components of GMSLR from 1850--2300, as well as parameter values for the simulations. There are two files: emissions_parameters.zip, which contains the parameter values used to generate the emissions trajectories, and ensemble_output.zip, which contains the model outputs. Rows correspond to ensemble members and columns correspond to years.</p>
In-depth Analysis: Vitrified Tiles Manufacturing Plant Project Report 2024
<p><span><a href="https://www.imarcgroup.com/vitrified-tiles-manufacturing-plant-project-report"><strong>Vitrified tiles manufacturing plant</strong></a> report covers various aspects, ranging from a broad market overview to intricate details like unit operations, raw material and utility requirements, infrastructure necessities, machinery requirements.</span></p>
Comparative basolateral amygdala connectomics reveals dissociable single-neuron projection patterns to frontal cortex in macaques and mice
<p>Zeisler et al., (2024) <em>Current Biology</em></p> <p> </p> <p>This repository contains data and code necessary to generate the main figures contained in the paper.</p> <p> </p> <p>The macaque data used is available at: https://zenodo.org/records/8319819</p>
Data for Nicolas & Boos, "Sensitivity of tropical orographic precipitation to wind speed with implications for future projections"
<p><span>This directory contains all data used in producing the plots in Nicolas & Boos (2024), "Sensitivity of tropical orographic precipitation to wind speed with implications for future projections". It is divided in four subdirectories:</span></p> <p><span> - wrfData contains processed simulation output needed to reproduced figures 1, 2, and SI figure 1.</span></p> <p><span> - regionsData and globalData contain processed observational (APHRODITE and IMERG) and ERA5 data necessary to reproduce figure 3 and SI figures 2 and 3.</span></p> <p><span> - cmipData contains processed CMIP surface wind data necessary to reproduce figure 4.</span></p> <p><span>Code used in producing these figures will be made available and linked to this dataset once any needed revisions are complete.</span></p>
SHIFT Project Series "7 Myths About Beyond-5G Debunked with Evidence"
<p>SHIFT's conrotium has addressed and debunked some of the most common misconceptions surrounding Beyond-5G technology. To wrap up this enlightening series, a video was released that summarizes all the myths and the research-based facts.<br>Watch this and get a comprehensive overview of how Beyond-5G is set to transform our world positively and sustainably.</p>
Technology Acquisition Plans to Foster Supply Chain Risk Management Learning Outcomes in Project-Based Software Development Courses: Raw Data and TAP Template
<p>This is a data set and TAP template to accompany the paper</p> <p>"Technology Acquisition Plans to Foster<span> </span>Supply Chain Risk Management Learning Outcomes in Project-Based Software Development Courses" by Tenbergen and Mead published at the 36th International Conference on Software Engineering Education and Training.</p>
Dataset related to ADHD phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to ADHD phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
Dataset related to diabetes phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to diabetes phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
Dataset related to epilepsy phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).
<p>Dataset related to epilepsy phase 3 of the project "Transition care between adolescent and adult services for young people with chronic health needs in Italy", funded by the Italian Ministry of Health (RF-2019-12371228).</p>
Kaltbrunner Manuel du Voyageur- Training session in Le Mans -SciCoMove project
<p>Presentation of the videos/podcasts produced by the Ph.D students during the training session in Le Mans. Here: Kaltbrunner, Manuel du voyageur.</p>
Observation-constrained projections reveal longer-than-expected dry spells
<p>The dataset is a collection of INPUT and OUTPUT data participating in the study "Observation-constrained projections reveal longer-than-expected dry spells" by I.Y. Petrova, Diego G.M., Florent B., Markus G.D., Seung-Ki M., Yeon-Hee K., Margot B. (2024)<br><br>The data is used to reproduce the main Figures of the study using the Python package available from https://github.com/klawa/EC4CDD_CMIPmodels package: <u><a href="https://doi.org/10.5281/zenodo.5886608">https://doi.org/</a>10.5281/zenodo.10869605</u><br><br>The data is provided in netCDF and Python pickle formats, and is archived using tar.gz extension. </p>
Resources for BMF Collaborative Project 64: Predictors of premium for bio-based clothes and discount for second-hand clothes
Open the record for dataset details and reuse information.
Addressing bias in digital cultural heritage collections metadata: the example of the DE-BIAS project
Open the record for dataset details and reuse information.
Project Package BSc Thesis M de Beijer - University of Amsterdam
<p>This package contains the data that is made for the Bachelor's project of Marit de Beijer (University of Amsterdam). The research explores geodiversity variations in coral reef ecosystems in Southeast Asia and Australia.</p>
AI4Life Open Calls: PROJECT OC1-10
<p>This dataset includes flow cytometry data (FCS files) for several plankton species, plus label data indicating which event in the FCS file is an alive sample. </p>
DMI Klimaatlas v2024a - Fremskrivninger af det danske klima (Projections of climate indicators in Denmark)
<p>See below for English version</p> <p> ---</p> <p><strong>DMI Klimaatlas v2024a - Fremskrivninger af det danske klima</strong></p> <p>Klimaatlas leverer ét samlet datagrundlag for det fremtidige danske klima. Klimaatlas er udarbejdet på baggrund af DMI's egne data, internationale samarbejder og viden fra rapporter fra FN’s Klimapanel (IPCC). Finansieringen kommer fra Finansloven 2018 og 2022. Klimaindikatorer er udregnet og samlet for hele Danmark, kommuner, alle vandoplande (afvandingsområder) og kyststrækninger, i et højtopløsningsgitter (1x1 km). </p> <p>Denne opdatering tilføjer 6 nye indikatorer for brandfare og tørre perioder:</p> <ul> <li>Brandfare <ul> <li>Gennemsnit af brandfareindekset.</li> <li>Dage med ’meget høj’ brandfare.</li> <li>Dage med ’ekstrem’ brandfare.</li> </ul> </li> <li>Tørre perioder <ul> <li>Andel af år/årstider som er ’tørre’.</li> <li>Tørre perioder på minimum 5 døgn.</li> <li>Tørre perioder på minimum 10 døgn.</li> </ul> </li> </ul> <p> Alle indikatorer fra tidligere udgaver findes også i dette datasæt.</p> <p>Ud over dette er 7 nye klimamodeller blevet tilføjet til ensemblet og antallet af modeller dermed steget til 72. Alle indikatorer er blevet genberegnet med den udvidet ensemble.</p> <p>Formatet af Excel-regnearkerne er blevet opdateret med fokus på maskinlæsbarhed.</p> <p>Datasættet indeholder de følgende elementer:</p> <ul> <li><strong>DMI_Klimaatlas_v2024a_Danmark_rapport.pdf</strong> – Klimaatlas rapport som giver et overblik over klimaforandring i Danmark.</li> <li><strong>DMIrapport_24_11.pdf</strong> - "Methods used in the Danish Climate Atlas", tekniske rapport som beskriver hvordan Klimaatlas data beregnes (på engelsk).</li> <li><strong>DMI_Klimaatlas_v2024a_Alle_indikator.xlsx</strong> – Microsoft Excel regneark med alle Klimaatlas indikatorer.</li> <li><strong>DMI_Klimaatlas_v2024a_Kommune_rapporter.zip</strong> - PDF rapporter om klimaforandringer i alle 98 kommuner i Danmark.</li> <li><strong>DMI_Klimaatlas_v2024a_Excel_regnearker.zip</strong> - Microsoft Excel-regneark med indikatorer opdelt efter kommuner, vandopleande eller kystrækninger.</li> <li><strong>DMI_Klimaatlas_v2024a_Udvidet_havniveau.xlsx</strong> – Udvidet havniveau datasæt til ekspertbrugere som har behov for en højere tidsopløsning, længere tidsdækning og eller andre scenarier for havniveaustigning.</li> <li><strong>DMI_Klimaatlas_v2024a_NetCDF_indicators.zip</strong> – Indikatorer på en 1km gitter over Danmark i NetCDF format.</li> </ul> <p>----</p> <p><strong>DMI Klimaatlas v2024a - Projections of climate indicators in Denmark</strong></p> <p>DMI’s <em>Klimaatlas</em> provides data for the future Danish climate. Klimaatlas is based on DMI's own data, international collaborations and knowledge from reports by the Intergovernmental Panel on Climate Change (IPCC). Funding comes from the Danish Finance Act 2018 and 2022. Climate indicators are calculated and compiled for all of Denmark, municipalities, all water basins (catchment areas) and coastlines, on a high-resolution grid (1x1 km). </p> <p>This update adds 6 new indicators for fire danger and dry periods:</p> <ul> <li>Fire danger <ul> <li>Average of the fire danger index.</li> <li>Days with ‘very high’ fire danger.</li> <li>Days with ‘extreme’ fire danger.</li> </ul> </li> <li>Dry periods <ul> <li>Dry periods of minimum 5 days.</li> <li>Dry periods of minimum 10 days. </li> <li>Proportion of years/seasons that are ‘dry’. </li> </ul> </li> </ul> <p>All indicators from previous versions are also available in this dataset.</p> <p>In addition, 7 new climate models have been added to the ensemble, increasing the number of models to 72. All indicators have been recalculated with the extended ensemble.</p> <p>The format of the Excel spreadsheets has been updated with a focus on machine readability.</p> <p>The dataset consists of the following elements:</p> <ul> <li><strong>DMI_Klimaatlas_v2024a_Danmark_rapport.pdf</strong> – <em>Klimaatlas</em> report describing the effects of climate change in Denmark (in Danish).</li> <li><strong>DMIrapport_24_11.pdf</strong> - "Methods used in the Danish Climate Atlas", technical report describing the methods used in generating <em>Klimaatlas</em> data.</li> <li><strong>DMI_Klimaatlas_v2024a_Alle_indikator.xlsx</strong> – Microsoft Excel spreadsheet with all <em>Klimaatlas</em> indicators (in Danish).</li> <li><strong>DMI_Klimaatlas_v2024a_Kommune_rapporter.zip</strong> - PDF report summarising the findings of <em>Klimaatlas</em> for each of the 98 municipalities in Denmark (in Danish).</li> <li><strong>DMI_Klimaatlas_v2024a_ Excel_regnearker.zip</strong> - Microsoft Excel spreadsheet with indicators divided by municipalities ("<em>Kommune</em>"), coastal stretches ("<em>Kyststrækninger</em>") and drainages ("<em>Vandoplande</em>") (in Danish).</li> <li><strong>DMI_Klimaatlas_v2024a_Udvidet_havniveau.xlsx</strong> – Extended sea level rise dataset for expert uses that have a need for a higher time resolution, longer time coverage or other climate scenarios for sea level rise (in Danish).</li> <li><strong>DMI_Klimaatlas_v2024a_NetCDF_indicators.zip</strong> – Indicators on a 1km grid over Denmark in the NetCDF format (in Danish).</li> </ul>
Project Package Bsc thesis Christijn Roos 13385372
<p>This dataset contains the project package including all the ArcGIS data needed for the Bsc. thesis Future Planet Studies of Christijn Roos [13385372]. </p> <p>University of Amsterdam</p>
3D Reconstruction of Neuronal Allometry and Neuromuscular Projections in Asexual Planarians Using Expansion Tiling Light Sheet Microscopy dataset2
<p>wide type planarian 6G10 staining taken with TLSM</p>
Predicting critical transitions with surrogate data-based machine learning: Project
<p>This project accompanies the Github repository <a href="https://github.com/ZhiqinMa/surrogate_data_based_machine_learning">https://github.com/ZhiqinMa/surrogate_data_based_machine_learning</a>. It contains the model time series data that are used to train the machine learning algorithms, as well as all the process and result files. Please extract all <code>.rar</code> files to <code>G:/surrogate_data_based_machine_learning_v4.0.0</code> folder to run this project.</p>
ScienceDex guides
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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.