Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
11,177
datasets available to search
ShareScore release 0.9.0
Dataset results
11,177 results for “identify”
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Marcelo Devecchi, <a href="https://orcid.org/0000-0002-1998-7664">https://orcid.org/0000-0002-1998-7664</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target
<p>Microwell-based (BD Rhapsody) scRNA-seq of Hairy Cell Leukemia Patients published in </p> <blockquote> <p><strong>Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target, Jan-Paul Bohn et al. Submitted.</strong></p> </blockquote> <p>The files will be made available upon publication. <br></p> <h4><strong>Description of the files</strong></h4> <ul> <li><strong>01_raw_counts: </strong>count matrices as CSV as generated by the BD Rhapsody WTA analysis pipeline</li> <li><strong>10_prepare_adata</strong>: Load BD Rhapsody WTA analysis pipeline outputs into AnnData objects and add metadata.</li> <li><strong>20_scrnaseq_qc</strong>: Use a nextflow pipeline (stored in lib/single-cell-analysis-nf) to perform threshold-based filtering of single-cell data and apply SOLO for doublet detection.</li> <li><strong>30_merge_adata</strong>: Merge samples into a single AnnData object, train a scVI model for batch effect removal, and annotate cell-types based on unsupervised clustering</li> <li><strong>40_cluster_analysis</strong>: Identify and investigate subclusters representing cell-states that go beyond the major cell-types</li> <li><strong>50_de_analysis</strong>: Generate pseudobulk and perform differential gene expression analysis using DESeq2 (based on a wrapper script stored in lib/deseq2_workflow)</li> <li><strong>70_downstream_analysis</strong>: Perform pathway analyses and generate figures for publication based on the data generated in the previous steps</li> <li><strong>containers:</strong> Conda environments used for the analysis packed up as singularity containers. </li> </ul>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Petr Hubatka, <a href="https://orcid.org/0000-0001-8178-5230">https://orcid.org/0000-0001-8178-5230</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by James Lucas da Costa-Lima, <a href="https://orcid.org/0000-0002-2967-5322">https://orcid.org/0000-0002-2967-5322</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Andriy Novikov, <a href="https://orcid.org/0000-0002-0112-5070">https://orcid.org/0000-0002-0112-5070</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Andrius Petrašiūnas, <a href="https://orcid.org/0000-0003-3270-3694">https://orcid.org/0000-0003-3270-3694</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
BioMates - WP1: Novel pyrolysis oil from non-food/feed biomass - Data sheet: Identifiers for samples and blends
<p>Ablative fast pyrolysis (AFP) is the first step in the BioMates-concept for producing bio-based intermediates (BioMates) with reliable properties from stalk-type biomass. These intermediates are designed for co-processing within conventional refineries. The H2020-project BioMates validates the process concept (<a href="http://www.biomates.eu">www.biomates.eu</a>).</p> <p>AFP produces bio-oil to be mildly hydrotreated towards said intermediates afterwards. This document provides identifiers for samples and blends produced in TRL4 via AFP from wheat-barley-straw and Miscanthus within the BioMates-project.</p>
Raw PKPD data for "Identifying an optimal dihydroartemisinin-piperaquine dosing regimen for malaria prevention in young Ugandan children"
<p>Raw pharmacokinetic and malaria outcomes data for manuscript "Identifying an optimal dihydroartemisinin-piperaquine dosing regimen for malaria prevention in young Ugandan children"</p>
A HKU5-related Coronavirus identified and assembled from Short-Read sequencing data of Gossypium Barbadense
<p>This is the complete annotated genome of the Merbecovirus identified from Gossypium Barbadense sequencing data, <a href="https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR5885860">SRR5885860</a></p>
Acute pseudo-landmarking and Constellation homologies: A generalized workflow to identify and track segmented structures in plant time series images
<p>Assessing plant phenotypes throughout the lifecycle is integral to exploring the development, genetics, and evolution of morphology, and can be critical for agronomic and basic research studies. Although various automated or semi-automated phenomic approaches have been developed, it has been challenging to analyze differential growth because of difficulties in segmenting and annotating specific structures or positions in the plant body and maintaining their identities throughout time-series data. To address this gap, we have developed a generalized workflow linking our previously published function, <i>Acute</i>, with a companion homology workflow, <i>Constellation</i>, in the PlantCV environment. <i>Acute</i> identifies acute shapes (pseudo-landmarks) in the plant body, most often corresponding to leaf tips and ligular regions. <i>Constellation</i> uses a strategy of dimensionality reduction via <i>starscape</i> followed by hierarchical clustering through <i>constella </i>to identify 'constellations' of segments in eigenspace that represent the same landmark in consecutive images of a time-series. We devised a quality control function, <i>constellaQC</i>, to test the accuracy of the clustering approach, and use it to show that the approach appropriately clusters the pseudo-landmarks derived from <i>Acute</i>, with 80-90% accuracy. We discuss the reasons for and consequences of this lack of 100% accuracy in automated workflows and suggest how to develop these functions for other phenomics datasets that may vary in dimensional complexity.</p>
Data for "Measurement report: Comparison of airborne in-situ measured, lidar-based, and modeled aerosol optical properties in the Central European background – identifying sources of deviations"
<p>A unique set of data is presented, derived from measurements conducted at the rural central European observatory at Melpitz, Germany. Data derived from remote sensing (lidar), airborne platforms (helicopter, balloon), and ground-based in-situ methods is included. Measured and Mie-modeled optical aerosol parameters are presented in the dry- and ambient state. Modeled optical parameters are based on Mie-theory. For ambient state hygroscopic growth simulations are utilized.</p>
Datasets corresponding to publication: Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes
<p>This repository contains all dataset that correspond to the publication "Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes". Furthermore, Python scripts are provided which allow to reproduce all analyses shown in the manuscript. Execution of the scripts requires a Python environment with packages as stated in the Methods section of the manuscript, or by using PyBox 0.1.0. PyBox is a readily installed Python environment containing all packages at the required version. PyBox is publicly available on GitHub: <a href="https://github.com/maikherbig/PyBox">https://github.com/maikherbig/PyBox</a>.</p>
Identifying behavioral and attitudinal barriers and drivers to promote consumption of pulses: A quantitative survey across five European countries
<p>Even though pulses are nutritious and environmentally friendly high-protein crops, they are commonly regarded as old-fashioned. Consumption of pulses is low in developed countries, and it has received very limited attention in agricultural and behavioral research over the past years. Based on a pan-European survey, the present study aims to provide extensive insights into consumer perceptions towards pulses. The objective is to identify effective measures to increase consumer acceptance of pulses, and thus potentially increase their share in the daily diets across Europe. Quantitative data was collected in May 2020 through a web-based survey conducted in five different European countries, Germany, Denmark, Spain, Poland, and the United Kingdom (N = 4,916). Quota-based sampling was used to ensure comparability across samples and wide coverage in terms of age, gender, and locality of residence. Using cluster analysis with a subsequent logistic regression, cross-cultural differences were found in terms of relatively high consumption of pulses among Spanish respondents, somewhat lower for Polish, German and UK respondents, and even lower among Danish respondents. Drivers of consumption significantly differed across surveyed samples, mainly being health first, followed by sensory preferences. For respondents from Poland, Spain, and Germany the main reason for not consuming pulses related to problems with digestion, whereas in the Danish and UK samples a neglect related to preparation. Future product development as well as increased information about nutrition and cooking of pulses could potentially increase consumer acceptance. Though, considering cross-cultural differences is important when developing strategies to promote consumption of pulses.</p>
Biologically informed machine learning for identifying human placental cellular heterogeneity and preeclampsia discovery
<p>Single-cell transcripts of 20,518 placental cells and identified 12 major placental cell clusters were collected from the European Bioinformatics Institute (EBI; accession no. EGAS00001002449) (29). Based on the same parameters, we clustered and visualized highly similar cells using t-distribution random neighbourhood embedding (T-SNE) to identify 17 cell subpopulations. we selected nine placental cell clusters that have received more attention from biologists for our study according to the literature survey (Table 1). Considering the sample balance, 7178 single-cell transcriptome data were used to identify human placental cell subpopulations. The samples were randomly divided into a 4809-sample training set and a 2369-sample testing set. The same strategy was applied to split single-cell transcriptomic datasets from healthy and preeclampsia patients (EBI; accession no. EGAS00001002449), with 9852 samples (healthy 4705, preeclampsia 5147) in the training set and 5305 samples (healthy 2473, preeclampsia 2832) in the independent test set</p>
Identifying traits that enable lizard adaptation to different habitats
<p><b>Aim: </b>Species adapt differently to contrasting environments, such as open habitats with sparse vegetation and forested habitats with dense forest cover. We investigated colonization patterns in the open and forested environments in the Diagonal of Open Formations and surrounding rain forests (i.e., Amazon and Atlantic Forest) in Brazil, tested whether the diversification rates were affected by the environmental conditions, and identified traits that enabled species to persist in those environments.</p> <p><b>Location: </b>South America, Brazil.</p> <p><b>Taxon: </b>Squamata, Lizards</p> <p><b>Methods: </b>We estimated ancestral ranges to identify range shifts relative to traditional open and forested habitats for all species. We used phylogenetic information and the current distribution of species in open and forested environments. To evaluate whether these environments influenced species diversification, we tested 12 models using a Hidden Geographic State Speciation and Extinction analysis. Finally, we combined phylogenetic relatedness and species traits in a machine learning framework to identify the traits permitting adaptation in those contrasting environments.</p> <p><b>Results: </b>We identified 41 total transitions between open and forested habitats, of which 80% were from the forested habitats to the open habitats. Widely distributed species had lower speciation and extinction rates than species in forested or open habitats, with the latter having the higher overall rates. Mean body temperature, microhabitat, female SVL, and diet were identified as putative traits that enabled adaptation to different environments, and phylogenetic relatedness was an important predictor of species occurrence.</p> <p><b>Main conclusions:</b> Our results indicate that transitions from forested to open habitats are most common. The combination of phylogenetic reconstruction of ancestral distributions and the machine learning framework enables us to integrate organismal trait data, environmental data, and evolutionary history in a manner that could be applied on a global scale.</p>
10000 most identified plant species on Pl@ntNet
<p>We have compiled a list of the most identified plant species since the beginning of Pl@ntNet. This represents more than 320 millions identification requests made by users within Pl@ntNet apps (mobile and website). More exactly, this is the first species in the list proposed to the user from his image (automatically identified).<br> <br> https://identify.plantnet.org/</p>
Increasing persistent identifier use across Asia
<p>Recording and slides from the event <em>Increasing persistent identifier use across Asia</em>.</p> <p>Persistent identifiers (PIDs) are seen as a cornerstone of modern open research infrastructure, however their governance and adoption are not uniform across different countries and regions. This event, as part of the initial activities of the PID Alliance, sought to understand the variation in the use of PIDs, such as DOIs and ORCID IDs, across Asia and any barriers to adoption.</p> <p>The recordings include presentations and a panel discussion and all include English subtitles generated by otter.ai. Due to a technical issue, not all presentations were recorded but slides are available instead. </p>
Data from: Sequential use of niche and occupancy models identifies conservation and research priority areas for two data-poor endemic birds from the Colombian Andes
<p>The lack of high-quality information on data-poor species can hinder efforts to inform conservation actions via spatial distribution modeling. This is particularly true for tropical birds of conservation concern, for which ecological studies and assessments of their conservation status have received limited funding. Here we use a cost- and time-efficient protocol for assessing the distribution of range-restricted taxa and to identify priority areas for their conservation based on a sequential application of Environmental Niche Models (ENMs) and Occupancy-Detection Models. This approach first uses available geographical information and niche-theory to prioritize potential study sites, which can later be surveyed to obtain high-quality presence-absence data to accurately model distributional ranges with limited resources. We apply this protocol to identify priority areas for two Neotropical birds of conservation concern endemic to the Colombian Andes: Yellow-headed Brush-finch (<i>Atlapetes flaviceps</i>) and Tolima Dove (<i>Leptotila conoveri</i>). We first fitted ENMs using spatially-filtered datasets containing all available records up to 2018. We then conducted field surveys across climatically suitable areas identified for both species, carrying out a total of 1750 counts to generate input data for the occupancy models. Overall, our results suggested more extended and more continuous distribution ranges for both species than previously reported, but also identified population strongholds that are not currently represented within the national protected areas system. Both species occupied a narrow elevational belt (~1300–2600) of the Central Andes of Colombia primarily on the slopes of the Magdalena River valley, with isolated populations in the Western and Eastern Andes; these areas have undergone some of the most marked landscape transformations in Colombia. This straightforward protocol maximizes available information and minimizes costs, while allowing for estimation of occurrence probabilities for range-restricted, data-poor taxa.</p>
Supporting data for identifying microclimate tree seedling refugia in post-wildfire landscapes
<p>High-severity wildfire in arid regions has caused ecological state change, transforming previously forested areas into shrublands. This dramatically alters the climatic envelope for tree seedlings, rendering the likelihood of returning post-wildlife landscapes to their previous state relatively low. We used a combination of sUAS imagery, satellite data and in-situ microclimate data recordings, together with a machine learning approach, to model monthly near-ground minimum, mean and max temperature as well as relative humidity and vapor pressure deficit in a previously forested area, which is now dominated by shrubs species. Spatially explicit models predicted recorded microclimate well (<i>r</i> = <span>0.73 to 0.97), and projections of models highlighted the solar buffering capacity of existing vegetation to alter the maximum temperature in the hottest month by ~12<sup>o</sup>C, increase relative humidity by ~20% and reduced vapor pressure deficit by 0.3mbar in locations 2m apart. By harnessing these microclimate refugia, the success rate of reforestation efforts in post-wildfire landscapes could be substantially increased and mitigate seedlings from climate warming at local scales.</span></p>
Data from: Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction
<p>The end-Permian mass extinction occurred alongside a large swathe of environmental changes that are often invoked as extinction mechanisms, even when a direct link is lacking. One way to elucidate the cause(s) of a mass extinction is to investigate extinction selectivity as it can reveal critical information on organismic traits as key determinants of extinction and survival. Here we show that machine learning algorithms, specifically gradient boosted decision trees, can be used to identify determinants of extinction as well as predict extinction risk. To understand which factors led to the end-Permian mass extinction during an extreme global warming event, we quantified the ecological selectivity of marine extinctions in the well-studied South China region. We find that extinction selectivity varies between different groups of organisms and that a synergy of multiple environmental stressors best explains the overall end-Permian extinction selectivity pattern. Extinction risk was greater for genera that had a low species richness, had narrow bathymetric ranges limited to deep-water habitats, had a stationary mode of life, possessed a siliceous skeleton or, less critically, had calcitic skeletons. These selective losses directly link the extinction to the environmental effects of rapid injections of carbon dioxide into the ocean-atmosphere system, specifically the combined effects of expanded oxygen minimum zones, rapid warming, and potentially ocean acidification.</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.