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9,786 results for “selection”
Linked collectors and determiners for: Select Specimens from the Hanover High School Insect Collection.
Natural history specimen data linked to collectors and determiners held within, "Select Specimens from the Hanover High School Insect Collection". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/8e8472a9-d604-4c97-9f2f-4aee2076a140">https://bionomia.net/dataset/8e8472a9-d604-4c97-9f2f-4aee2076a140</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/8e8472a9-d604-4c97-9f2f-4aee2076a140">https://gbif.org/dataset/8e8472a9-d604-4c97-9f2f-4aee2076a140</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Plant species occurrences recorded from selected sites in the lower Tana River Basin, Kenya.
Natural history specimen data linked to collectors and determiners held within, "Plant species occurrences recorded from selected sites in the lower Tana River Basin, Kenya". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/cbc3c3cb-4351-42cd-8209-70c3f12f4dad">https://bionomia.net/dataset/cbc3c3cb-4351-42cd-8209-70c3f12f4dad</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/cbc3c3cb-4351-42cd-8209-70c3f12f4dad">https://gbif.org/dataset/cbc3c3cb-4351-42cd-8209-70c3f12f4dad</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Select Insect Specimens from Malaise Traps in Orleans County, Vermont, USA.
Natural history specimen data linked to collectors and determiners held within, "Select Insect Specimens from Malaise Traps in Orleans County, Vermont, USA". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/30968d94-12c8-48d6-9cc7-1a407f4c5e1b">https://bionomia.net/dataset/30968d94-12c8-48d6-9cc7-1a407f4c5e1b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/30968d94-12c8-48d6-9cc7-1a407f4c5e1b">https://gbif.org/dataset/30968d94-12c8-48d6-9cc7-1a407f4c5e1b</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Plant species occurrences recorded from selected sites in the mid Tana River Basin, Kenya.
Natural history specimen data linked to collectors and determiners held within, "Plant species occurrences recorded from selected sites in the mid Tana River Basin, Kenya". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/88e15b3d-e8fa-4527-b164-83078901de99">https://bionomia.net/dataset/88e15b3d-e8fa-4527-b164-83078901de99</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/88e15b3d-e8fa-4527-b164-83078901de99">https://gbif.org/dataset/88e15b3d-e8fa-4527-b164-83078901de99</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Bitunicate ascomycetes (Dothideomycetes and Chaetothyriomycetidae) on bark and wood of selected hosts in Norway.
Natural history specimen data linked to collectors and determiners held within, "Bitunicate ascomycetes (Dothideomycetes and Chaetothyriomycetidae) on bark and wood of selected hosts in Norway". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/ca0d8107-a2bd-47a1-91a1-250179b534ec">https://bionomia.net/dataset/ca0d8107-a2bd-47a1-91a1-250179b534ec</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/ca0d8107-a2bd-47a1-91a1-250179b534ec">https://gbif.org/dataset/ca0d8107-a2bd-47a1-91a1-250179b534ec</a>. Formatted as a Frictionless Data package.
Research Data for the Journal Article: Metal-free catalytic systems based on imidazolium chloride and strong bases for selective oxidative esterification of furfural to methyl furoate
Open the record for dataset details and reuse information.
Data and code for FishMIP global marine ecosystem model ensemble projections summarised by countries and territories and other selected marine spatial regions.
<p>R code to extract and create data tables and summary plots of FishMIP mean ensemble projections provided are for percentage change in "exploitable fish biomass", which is a proxy for the biomass available to fisheries, consisting of marine animals spanning the size range 10 g to 100 kg: this is typically dominated by fish, but is also inclusive of other animals such as crustaceans and cephalopods.</p> <p>This release contains scripts and summary data for producing figures in Part A of the following report:</p> <p>Blanchard, J.L., Novaglio, C., eds. (2024). Climate change risks to marine ecosystems and fisheries: Future projections from the Fisheries and Marine Ecosystems Model Intercomparison Project. FAO Fisheries and Aquaculture Technical Paper No. 707. Rome, FAO.</p> <p>Please refer to the above report to cite and for more information.</p> <p>The summary data are here:</p> <p>https://github.com/Fish-MIP/FAO_Report/blob/main/data/table_stats_formatted_admin_full.csv</p> <p>Where the column 'spatial_scale' refers to the type of aggregation:</p> <p>FAO_area = High Sea areas grouped by FAO Major Fishing Areas</p> <p>countries = Exclusive Economic Zones</p> <p>countries_admin = Exclusive Economic Zones results aggregated into Administrative Countries</p> <p>Please note that these results can also be visualised and downloaded from our shiny app: https://rstudio.global-ecosystem-model.cloud.edu.au/shiny/FAO_report_shiny/</p> <p> </p>
Raw data for UV-Selective Optically Transparent Zn(O,S)-Based Solar Cells
<p>In the following the raw data lying the foundation of the paper “UV-Selective Optically Transparent Zn(O,S)-Based Solar Cells” (Lopez-Garcia et al.) published in Solar Rapid Research Letters Vol. 4, 2000470, 2020, are described. They were obtained under the funding provided by the European Union H2020 Framework Programme under Grant Agreement no. 826002 (Tech4Win) and by the Basque Country PI2018-08 (PISCES).</p> <p>UV–vis measurements were acquired with a dual-beam spectrophotometer setup (Perkin Elmer Lambda L35) in transmittance mode (light source and detector normal to sample’s surface (i.e., 0<sup>o</sup>)) scanning from 300 to 800 nm.</p> <p>Raman spectroscopy was performed with a FHR640 Horiba Jobin–Yvon spectrometer coupled to a Raman probe developed at Institut de Recerca en Energia de Catalunya (IREC) and a cryogenically cooled charge coupled device detector. Measurements were carried out in backscattering configuration and with a 325 nm UV laser as the excitation wavelength. An excitation power density of about 25W/cm<sup>2</sup> was used to inhibit thermal effects on the samples. The Raman shift was calibrated using a Si monocrystal reference and adjusting the Raman shift for the main Si band at 520 cm<sup>-1</sup>.</p> <p>FE-SEM images were acquired with a ZEISS Auriga Series system. The images were acquired at 5 kV, aperture of 20 μm, and working distance of around 4mm with the InLens detector.</p> <p>J–V measurements under illumination were carried out using a homemade setup consisting on a 150W xenon broadband arc Lamp (Thorlabs SLS401) calibrated using a NREL-certified Si reference solar cell (Abet Technologies, Model 15150). Electrical measurements were carried out with a source-measure unit (Keithley 2400) in four-wire sense mode, controlled by the software Tracer (ReRa solutions) using a IEEE 488 GPIB Instrument Control Device (National Instruments GPIB-USB-HS).</p> <p>EQE curves were obtained using a spectral response system (Bentham PVE300) calibrated with a Si photodiode.</p>
Learning How to Search: Generating Effective Test Cases Through Adaptive Fitness Function Selection
<p>Data Package for "Learning How to Search: Generating Effective Test Cases Through Adaptive Fitness Function Selection"</p> <p>This package contains data generated as part of our experiments on adaptive fitness function selection as part of unit test generation for Java systems.</p> <p>This paper is currently under submission. A draft of the paper is included in the data package.</p> <p>This package contains experimental data (in folder "experiment_data"), including goal attainment, fault detection, time per generation, and choices made by the reinforcement learning algorithm. In the folder "test_suites", the suites generated by each technique are included for each project. </p> <p>If you have questions, please contact Gregory Gay at greg@greggay.com.</p> <p>NOTE: A small number of items are currently missing from this data package and will be added shortly. Please make sure you have the latest version of this package.</p>
Data and Software associated with the paper "``A New Likelihood-based Test for Natural Selection''"
<p>Data and Software associated with the paper ``A New Likelihood-based Test for Natural Selection''</p>
Data release: Whole-genome sequencing of Schistosoma mansoni reveals extensive diversity with limited selection despite mass drug administration
<p>Source data used in the publication: Berger et al. (2021) - Provisional title: 'Whole-genome sequencing of <em>Schistosoma mansoni</em> reveals extensive diversity with limited selection despite mass drug administration'. These data were used to generate all figures used in the publication and all files are organised and labelled specifically to run with the custom code that uses these data can be found at: http://doi.org/10.5281/zenodo.4975908. </p> <p><br> <strong>File descriptions:</strong></p> <p><strong>SOURCE DATA.zip - All source data for all figures. </strong></p> <p><strong>Figure 1b:</strong></p> <ul> <li>supplementary_data_9.txt - Metadata</li> </ul> <p><strong>Figure 2a&b:</strong></p> <ul> <li>207_PCA.eigenvec - PCA eigenvectors</li> <li>207_PCA.eigenval - PCA eigenvalues</li> </ul> <p><strong>Figure 2c:</strong></p> <ul> <li>autosomes.mdist - PLINK distance matrix used to build the neighbour joining phylogeny</li> </ul> <p><strong>Figure 2d:</strong></p> <ul> <li>all.pi.pixy.schools.txt - Nucleotide diversity results for each school subpopulation.</li> </ul> <p><strong>Figure 2e:</strong></p> <ul> <li>autosomes.dxy.5kb.schools.txt - Autosomal D<sub>XY</sub> results between school subpopulations. </li> <li>autosomes.fst.5kb.schools.txt - Autosomal F<sub>ST</sub> results between school subpopulations.</li> </ul> <p><strong>Figure 2f:</strong></p> <ul> <li>admixture_all.txt - ADMIXTURE results for each sample and population sizes, column 1 represents number of populations (K), columns 3-8 represent admixture values for each population. </li> </ul> <p><strong>Figure 3a, Supplementary figure 10a:</strong></p> <ul> <li>sfs.csv - Site frequency spectra (allelic proportions at each frequency bin) for each school. </li> </ul> <p><strong>Figure 3b:</strong></p> <ul> <li>TD.all.txt - Tajima's D values calculated in 5 kb windows for each school subpopulation. </li> </ul> <p><strong>Figure 4a, Supplementary figures 13-18: </strong></p> <ul> <li>ALL.MAYUGE.IHS.ihs.out.100bins.norm.txt.zip - Normalised iHS scores for the Mayuge district parasite populations (Selscan output).</li> </ul> <p><strong>Figure 4b, Supplementary figures 13-18: </strong></p> <ul> <li>ALL.TORORO.IHS.ihs.out.100bins.norm.txt.zip -<strong> - </strong>Normalised iHS scores for the Tororo district parasite populations (Selscan output).</li> </ul> <p><strong>Figure 4c, Supplementary figures 13-18: </strong></p> <ul> <li>ALL.MAYUGEvsTORORO.xpehh.xpehh.out.norm.txt.zip - - Normalised XP-EHH scores between Mayuge and Tororo parasite populations.</li> </ul> <p><strong>Figure 4d, Supplementary figures 13-18:</strong></p> <ul> <li>MAYUGE_TORORO_2000.windowed.weir.txt.zip - F<sub>ST</sub> values calculated between Mayuge and Tororo populations in 2kb windows. </li> </ul> <p><strong>Figure 4e, Supplementary figures 12a&c:</strong></p> <ul> <li>MAYUGE_PI.windowed.pi.zip - Nucleotide diversity values calculated in 2 kb windows for Mayuge populations. </li> <li>TORORO_PI.windowed.pi.zip - Nucleotide diversity values calculated in 2 kb windows for Kocoge populations (Tororo district).</li> </ul> <p><strong>Figure 5a:</strong></p> <ul> <li>all.pi.treat.fix.txt.zip - Nucleotide diversity results for each treatment subpopulation</li> </ul> <p><strong>Figure 5b</strong></p> <ul> <li>autosomes.dxy.5kb.treatment.txt - <strong> </strong>- Autosomal D<sub>XY</sub> results between clearance phenotype subpopulations. </li> <li>autosomes.fst.5kb.treatment.txt<strong> </strong>- Autosomal F<sub>ST</sub> results between clearance phenotype subpopulations. </li> </ul> <p><strong>Figure 5c:</strong></p> <ul> <li>fst.windows.2kb.treatment.txt.zip - F<sub>ST</sub> values for comparisons between different treatment groups (Pre-treatment, post-treatment (good clearers), post-treatment (poor clearers))</li> </ul> <p><strong>Figure 5d: </strong></p> <ul> <li>assoc_err_binary.txt.zip - Results of binary trait association between miracidia sampled from hosts with good clearance phenotypes (where treatment appeared to be highly effective) and miracidia isolated post-treatment from hosts with poor clearance phenotypes (where miracidia are potentially derived from parasites that survived treatment.</li> </ul> <p><strong>Figure 5e:</strong></p> <ul> <li>assoc_err_linear.txt.zip - - Results of linear regression genome-wide association study with the ERR estimates for all 198 samples, using the mean of the posterior ERR estimates from Crellen et al. (2016) as a quantitative trait.</li> </ul> <p><strong>Supplementary figure 1:</strong></p> <ul> <li>median.coverage.txt - Normalised depth of read coverage (column 4) calculated in 25 kb windows (columns 2&3) across all samples for all chromosomes (column 1).</li> </ul> <p><strong>Supplementary figure 2a-f: </strong></p> <ul> <li>cohort.genotyped.txt.zip - <strong> </strong>- Variant quality site values (used to inform variant site retention or removal). </li> </ul> <p><strong>Supplementary figure 2g:</strong></p> <ul> <li>hard_filtered.imiss.txt - Per sample variant missingness (used to inform quality control).</li> </ul> <p><strong>Supplementary figure 2h:</strong></p> <ul> <li>hard_filtered_filtindv.lmiss.txt.zip - Per site missingness (used to inform quality control).</li> </ul> <p><strong>Supplementary figure 3a, 4a, 4b:</strong></p> <ul> <li>prunedData.eigenvec - PCA eigenvectors</li> <li>prunedData.eigenval - PCA eigenvalues</li> </ul> <p><strong>Supplementary figure 3b:</strong></p> <ul> <li>pruned_data.mdist.csv - Distance matrix used as the basis for the neighbour joining phylogeny.</li> </ul> <p><strong>Supplementary figure 5:</strong></p> <ul> <li>cv_scores.txt - ADMIXTURE coefficient of variation scores (column 2) for each population size (1).</li> </ul> <p><strong>Supplementary figure 6:</strong></p> <ul> <li>*_SMC_SE.csv - SMC++ results (from 25 subsampled replicates) for each school subpopulation and outgroup samples. </li> </ul> <p><strong>Supplementary Figure 7:</strong></p> <ul> <li>smcpp.csv - SMC++ results for each school subpopulation and outgroup samples. </li> </ul> <p><strong>Supplementary Figure 8a-d</strong></p> <ul> <li>pi.per_host.txt.zip - Nucleotide diversity values for each host infrapopulation. </li> </ul> <p><strong>Supplementary Figure 9:</strong></p> <ul> <li>sexing.csv - inferred sex (based on differential read coverage over pseudoautosomal and Z-specific regions of the Z chromosome). </li> </ul> <p><strong>Supplementary Figure 10b:</strong></p> <ul> <li>sfs_res.csv - residuals for the SFS analysis in 3a/10a.</li> </ul> <p><strong>Supplementary Figure 11:</strong></p> <ul> <li>MAYUGE_TAJIMA_D.Tajima.D.2kb.txt.zip - Tajima's D values calculated for the Mayuge population in 2kb windows. </li> <li>Tororo_TAJIMA_D.Tajima.D.2kb.txt.zip - Tajima's D values calculated for the Tororo population in 2kb windows. </li> </ul> <p><strong>Supplementary Figures 13-18:</strong></p> <ul> <li>genes.bed - Coordinates of gene models (<em>S. mansoni </em>v7 annotation).</li> <li>KOCOGE_SITE_PI.sites.pi.txt.zip - Per site nucleotide diversity values</li> <li>MAYUGE_TORORO_sites.weir.fst.txt.zip - Per site F<sub>ST</sub> values between Mayuge and Tororo populations. </li> <li>coverage_5kb.windows.txt.zip - Per sample depth of read coverage in 5 kb windows. Columns 4,5,6 represent the median, mean and sstev of coverage for each 5kb window (columns 2&3) along each chromosome (column 1). </li> <li>median.sample.coverage.txt - Median chromosomal depth of read coverage for each sample. </li> </ul> <p><strong>Supplementary Figure 19:</strong></p> <ul> <li>kocoge_median.ld.txt.zip - <strong> </strong>- The decay of linkage disequilibrium with genomic distance between all sites within 50 kb for the Kocoge parasite samples. Chromosomes are shown in column 1, distance in column 2, median values in column 3. </li> <li>mayuge_median.ld.txt.zip - The decay of linkage disequilibrium with genomic distance between all sites within 50 kb for the Mayuge parasite samples. Chromosomes are shown in column 1, distance in column 2, median values in column 3. </li> </ul> <p><strong>Misc files:</strong></p> <p>schools.list - List of samples and schools where they were sampled. </p> <p> </p>
Simulated pp collisions at 13 TeV with 2 leptons + 1 b jet final state and selected benchmark Beyond the Standard Model signals
<p>This data-set is comprised of simulated events of pp collisions at 13 TeV with 2 leptons + 1 bottom jet sinal state, with HT > 500 GeV. It includes the following samples</p> <ul> <li>Standard-Model background (bkg), generated at leading order includes the sub-samples Z+Jets, ttbar, WW, WZ, and ZZ. <ul> <li>The processes were generated in kinematic regions to ensure good statistics across the whole phase space. The sampling was carried out using event generation filters at parton level as follows <ul> <li>ttbar: pT <100 GeV; pT in [100, 250] GeV; pT > 250 GeV</li> <li>The scalar sum of the pT of outgoing particles for Z+Jet: ST < 250 Gev; ST in [250, 500] GeV; ST > 500 GeV</li> <li>W/Z pT for dibosons: pT < 250 GeV; pT in [250, 500] GeV; pT > 500 GeV</li> </ul> </li> </ul> </li> <li>Vector-like T-quarks with masses 1.0, 1.2, 1.4 TeV (hq1000, hq1200, hq14000) pair produced either through the Standard-Model gluon (wohg) or through a BSM 3TeV heavy gluon (hg3000)</li> <li>tZ production through a Flavour Changing Neutral Current (fcnc) vertex</li> </ul> <p>The samples are provided with both a full set of features, or with a sanitised set of features. The sanitised features remove some accumulation at zeros from non-reconstructed objects (i.e. missing values). All samples were generated using MadGraph5 2.6.5 and the detector was simulated using Delphes 3 with the default CMS card. For the Standard-Model background, both Pythia 8.2 (with CMS CUETP8M1 underlying event tune and NNPDF 2.3 parton distribution functions) (pythia) and Herwig 7 (herwig) hadronisations are provided to compare the background simulation. For the BSM signals only Pythia is provided.</p> <p>For the details of the generation and on the differences between the two feature sets please refer to <a href="https://link.springer.com/article/10.1140%2Fepjc%2Fs10052-020-08807-w">Finding new physics without learning about it: anomaly detection as a tool for searches at colliders</a> for more details. Each file provides a train:validation:split with the ratios 1:1:1 to ensure equal statistical description of the events at each step of the machine learning workflow.</p>
Data for: PickMe: sample selection for species tree reconstruction using coalescent weighted quartets
<p>After collecting large data sets of many genes for many species for phylogenomics studies, researchers may make ad hoc decisions about which genes or samples to include in a species tree reconstruction analysis based on various parameters, including the amount of missing data. Optimally, sampling would be maximized, but it can be difficult for empiricists to determine where to draw the line for sample inclusion when data sets are incomplete. Under the multispecies coalescent model, in which the dominant quartet topology displayed across gene trees matches the topology of that quartet on the species tree, we propose a Bayesian framework to select samples for which there is support for inclusion in a species tree analysis. Given a collection of gene trees, a posterior probability is assigned to each quartet topology, describing the likelihood that the species tree displays this topology. From this, individual samples are assigned reliability scores computed as the average of a rescaling of the posterior probabilities. These weights are used in a Bayesian framework in an algorithm called PickM}, which determines which individuals should be included in a species tree analysis. To illustrate the efficacy of this tool, PickMe is applied to gene trees generated from target capture data from milkweeds. PickMe indicates that more samples could have reliably been included in a previous milkweed phylogenomic analysis than the authors analyzed, without access to a formal decision-making procedure. Thus, PickMe will be a valuable addition to data analysis pipelines for phylogenomics studies.</p>
FIG. 7. — Instar V in Tingidae (Insecta, Heteroptera) from the Argentinan Yungas: new records and descriptions of selected fifth instars
FIG. 7. — Instar V of Sphaerocysta inflata Monte, 1941: A, habitus; B, cephalic tubercles; C, abdominal protuberence. Scale bars: A, 1 mm; B, 100 μm; C, 10 μm.
FIG. 5. — Instar V in Tingidae (Insecta, Heteroptera) from the Argentinan Yungas: new records and descriptions of selected fifth instars
FIG. 5. — Instar V of Leptopharsa firma Drake & Hambleton, 1938: A, habitus; B, pronotal tubercle. Scale bars: A, 1 mm; B, 10 μm.
FIG. 2. — Instar V in Tingidae (Insecta, Heteroptera) from the Argentinan Yungas: new records and descriptions of selected fifth instars
FIG. 2. — Instar V of Corythucha tapiensis Ajmat, 1991: A, habitus; B, microstructures on instar V dorsum; C, cephalic tubercle. Scale bars: A, 1 mm; B, 10 μm; C, 100 μm.
FIG. 1 in Tingidae (Insecta, Heteroptera) from the Argentinan Yungas: new records and descriptions of selected fifth instars
FIG. 1. — Distributional map of the species collected. See Table 2 for the list of species collected in localities 1-6. TABLE 1. — List of Tingidae collecting sites with coordinates.
Figure 6 in Key to Florida Alydidae (Hemiptera: Heteroptera) and selected exotic pest species
Figure 6. Images of diagnostic characters for couplets 16-18. (a) Burtinus notatipennis, head, dorsal view; distance between ocelli greater than distance between ocellus and eye; (b) Megalotomus quinquespinosus, head, dorsal view; distance between ocelli less than distance between ocellus and eye; (c) Megalotomus quinquespinosus, scent gland peritreme; arrows indicate two distinct lobes; (d) Alydus pilosulus; scent gland peritreme; arrow indicates single lobe; (e) Alydus pilosulus, head and thorax, lateral view; (f) Alydus eurinus, head and thorax, lateral view.
Figure 5 in Key to Florida Alydidae (Hemiptera: Heteroptera) and selected exotic pest species
Figure 5. Images of diagnostic characters for couplets 12, 14, and 15. (a) Riptortus linearis, head and thorax, lateral view; arrows indicate parallel-sided fascia and ridge-like scent-gland peritreme; (b) Neomegalotomus rufipes, thorax, lateral view; arrows indicate presence of humeral spine and absence of stridulatory apparatus on edge of corium; (c) Alydus eurinus, thorax, lateral view; arrows indicate absence of humeral spine and presence of stridulatory device on edge of corium; (d) Neomegalotomus parvus, scent gland peritreme; arrow indicates weak separation of anterior and posterior lobes; (e) Neomegalotomus rufipes, scent gland peritreme; arrow indicates deep and distinct separation of anterior and posterior lobes.
Figure 3 in Key to Florida Alydidae (Hemiptera: Heteroptera) and selected exotic pest species
Figure 3. Images of diagnostic characters for couplets 4, 5 and 7. (a) Leptocorisa oratorius, abdomen, lateral view; (b) Leptocorisa acuta, abdomen, lateral view; (c) Leptocorisa acuta, arrows indicate markings on collar and humeri; (d) Stenocoris tipuloides, arrows indicate presence of markings behind the eye and on the collar, and absence of markings on the humeri; (e) Stenocoris tipuloides, hemelytra dorsal view; (f) Stenocoris filiformis, hemelytra, dorsal view.
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.