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443 results for “performance assessment”
Novel health system strategies for tuberculin skin testing at primary care clinics: performance assessment and health economic evaluation.
<p>Datasets for journal paper.</p>
Data from: Assessing risks of invasion through gamete performance: farm Atlantic salmon sperm and eggs show equivalence in function, fertility, compatibility and competitiveness to wild Atlantic salmon
Adaptations at the gamete level (a) evolve quickly, (b) appear sensitive to inbreeding and outbreeding and (c) have important influences on potential to reproduce. We apply this understanding to problems posed by escaped farm salmon and measure their potential to reproduce in the wild. Farm Atlantic salmon (Salmo salar) are a threat to biodiversity, because they escape in large numbers and can introgress, dilute or disrupt locally adapted wild gene pools. Experiments at the whole fish level have found farm reproductive potential to be significant, but inferior compared to wild adults, especially for males. Here, we assess reproductive performance at the gamete level through detailed in vitro comparisons of the form, function, fertility, compatibility and competitiveness of farm versus wild Atlantic salmon sperm and eggs, in conditions mimicking the natural gametic microenvironment, using fish raised under similar environmental conditions. Despite selective domestication and reduced genetic diversity, we find functional equivalence in all farm fish gamete traits compared with their wild ancestral strain. Our results identify a clear threat of farm salmon reproduction with wild fish and therefore encourage further consideration of using triploid farm strains with optimized traits for aquaculture and fish welfare, as triploid fish remain reproductively sterile following escape.
Data from: Testing weed risk assessment paradigms: intraspecific differences in performance and naturalisation risk outweigh interspecific differences in alien Brassica
1.Risk assessments of alien species are usually conducted at species level, assuming that all individuals of a given species pose similar risks. However, this may not be the case if there is substantial within-species variation that could influence invasion success. 2.We used a seed addition experiment, comprising 25 taxonomically stratified varieties of three Brassica species introduced to roadside habitats in Canterbury, New Zealand, to quantify variation in performance among species, subspecies and varieties. We aimed to assess if species was the most appropriate taxonomic level at which to evaluate invasion risk. 3.Differences among varieties within species explained approximately 30 times more of the variation in performance (number of individuals/quadrat) than differences among species. Some of the variation among varieties was attributable to differences in seed viability. 4.Nevertheless, differences among taxonomic groups explained only 7% of the total variation in performance; 28% was attributable to differences among plots, reflecting broad-scale environmental variation, while 65% was attributable to differences among quadrats nested within plots, highlighting the importance of fine-scale variation in the availability of suitable microsites. 5.Policy Implications. Our seed addition experiment quantified variation in performance of 25 taxonomically stratified Brassica taxa introduced to roadside habitats. Varieties (nested within species) differed in performance far more than did species. This suggests risk assessments carried out at species level may overlook important subspecific variation in invasion risk. This is particularly true for conventionally bred and genetically modified species, which may contain taxa posing risks different to that at which the species is assessed. Consideration should be given to subjecting unassessed subspecies and varieties of plants to risk assessments similar to those applied to species.
Data used in the extension of the conference paper "An Experimental Performance Assessment of Galileo OSNMA"
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Molecular docking analysis was performed to assess the affinity of onalespib for their targets LOX, elucidating binding poses, protein interactions, and associated binding energies.
<p>To analyze the binding affinities and interaction modes between the drug candidates and their targets, we employed the Autodock Vina software [21]. Molecular structures of the candidate drugs and targets of hub genes were retrieved from Pubchem (https://pubchem.ncbi.nlm.nih.gov/) and Protein Data Bank database (http://www.rcsb.org/), respectively. <span>In the analysis of docking, the files for all proteins and molecules were converted to PDBQT format. Water molecules were removed and polar hydrogen atoms were added. The grid box was positioned at the center to encompass the protein domain, allowing for unrestricted movement of molecules.</span></p>
Data supporting: Assessment of the performance of nonfouling polymer hydrogels utilizing citizen scientists
<p>This dataset contains all data collected by citizen scientists in support of the publication: "Hansen N, Bryant A, McCormack R, Johnson H, Lindsay T, Stelck K, et al. (2021) Assessment of the performance of nonfouling polymer hydrogels utilizing citizen scientists. PLoS ONE 16(12): e0261817. https://doi.org/10.1371/journal.pone.0261817".</p> <p>This study evaluates the performance of several commercially available nonfouling polymers using citizen science, to identify the best performing chemistry for future applications as bacteria resistant coatings.<b> </b>The specific polymer chemistries tested were zwitterionic sulfobetaine methacrylate (SBMA), and polyampholytes composed of [2-(acryloyloxy)ethyl] trimethylammonium chloride and 2-carboxyethyl acrylate (TMA:CAA) or TMA and 3-sulfopropyl methacrylate (TMA:SA). Each polymer chemistry is known to exhibit bacteria resistance, and this study utilizes a citizen science approach to compare the performance of these chemistries.</p>
Rivermead assessment of somatosensory performance: Italian normative data
<p>Raw data collected and analysed for the paper 'Rivermead assessment of somatosensory performance: Italian normative data', published in Neurological Sciences (2021) 42:5149–5156</p>
Raw data for "Assessing Cover Crop and Intercrop Performance Along a Farm Management Gradient" (2022)
<p>This dataset accompanies the publication "Assessing Cover Crop and Intercrop Performance Along a Farm Management Gradient" by Stratton et al. in the journal Agriculture, Ecosystems, and Environment (2022). <a href="https://doi.org/10.1016/j.agee.2022.107925">https://doi.org/10.1016/j.agee.2022.107925</a></p> <p>METHODS:</p> <p>We conducted our experiment between May 2018 and December 2019 on 14 farms in the eastern coastal highlands region of Santa Catarina, Brazil. The mean altitude of sites was 467 m (+/- 161 m). Eastern Santa Catarina has a subtropical climatic pattern, with mean annual rainfall ranging from 1,500-1,700 mm (Wrege et al., 2012). While 2018 had typical weather patterns for the region, 2019 was a dry year, particularly during the spring months (Appendix B, Table B.1). All farms were located in the Colonial Serrana Catarinense soil microregion, one of 16 designated microregions in the state of Santa Catarina (EMBRAPA, 2004). Primary soil types in our study site are associations of dystric Cambisols and haplic Acrisols (typic Dystrocryepts and typic Paleudults in the USDA Soil Taxonomy), which tend to be moderately to highly acidic, with limited soil nutrient availability and moisture retention (EMBRAPA, 2004; IUSS Working Group WRB, 2015; USDA, 2010). To support crop production, farmers in the region typically apply lime (calcium and magnesium carbonate) to agricultural fields to increase soil pH from <5.5 to 6 (Comissão de Química e Fertilidade do Solo - RS/SC, 2016). Exact farm locations within the region are not given and farmer identities have been anonymized.</p> <p><em>Experimental design</em></p> <p>The fully factorial experiment had six treatments (Figure 2): (1) cover crop + pea-cucumber intercrop, (2) cover crop + pea monocrop, (3) cover crop + cucumber monocrop, (4) fallow + pea-cucumber intercrop, (5) fallow + pea monocrop, and (6) fallow + cucumber monocrop. Due to the timing of farm recruitment, only conventional and transitioning farms participated in the first year of cover cropping (2018); agroecological farms were added to the study during the vegetable intercropping period of 2018 and had their first round of cover cropping in 2019. The cover crop mixture treatment was designed to emulate traditional practices in the region, as well as to include functionally complementary legume and grass species: common vetch (<em>Vicia sativa </em>L.) and black oat (<em>Avena strigosa </em>Schreb). We also selected vegetables with distinct ecological functional traits, such that intercropping represented an increase in functional diversity relative to mono-cropped vegetables. Snow peas are N-fixing legumes with a vining, upright structure and a deep root system, whereas cucumbers are low-lying, non-legume cucurbits that provide groundcover and have a relatively shallow, extensive root system.</p> <p> </p> <p>Cover crop treatments consisted of two adjacent 50 m2 plots in each field, one of which was planted with the cover crop mixture; the other served as a weedy fallow control. In 2018, the cover crop mixture seeding rate was 72 kg/ha black oat and 36 kg/ha common vetch. Due to poor vetch performance in mixtures at this rate, we increased the vetch seeding rate to 60 kg/ha in 2019, maintaining the black oat rate from 2018. Cover crop seeds were inoculated with the Brazilian strain <em>Rhizobium etli </em>(SEMIA 384; source: FEPAGRO) at 4 g/kg vetch seed prior to planting. Cover crops were grown until peak flowering, and then cover crops (and weeds in the fallow) were incorporated into the soil by rototiller (<em>n</em> = 7 farms) or by hand hoeing (<em>n</em> = 7 farms), based on farms’ available machinery, between September 5-10 in 2018 and September 10-18 in 2019 (approximately one week following cover crop sampling on each farm).</p> <p> </p> <p>Vegetables were planted two weeks following cover crop and weed biomass incorporation within a period of 7-10 days across sites. Harvest dates were spaced such that crops were growing for approximately the same period across farms. The 50 m2 plots were each divided into three intercrop treatments with a ~1 m2 pathway between each treatment, for a total of 6 treatments randomly assigned to plots per 100 m2. We planted a climbing variety of snow peas (<em>Pisum sativum</em> subsp. <em>sativum</em> var. <em>macrocarpum</em>,<strong> </strong>“<em>Torta de flor roxa”</em>) and pickling cucumber (<em>Cucumis sativa </em>L. var. <em>Pepino HT </em>05) in intercrops and in their respective monocrops, using a replacement design (i.e., equivalent crop densities in all treatments). Snow pea seeds were inoculated with <em>Rhizobium leguminosarum</em> var. <em>viceae</em> (SEMIA 3007/BR 619, source: UFSC ENR/CCA) at a rate of 4 g/kg directly prior to planting. There were five rows of crops per treatment, with only the three middle rows harvested to limit edge effects. In-row spacing was 60 cm for cucumber and 20 cm for peas, with 60 cm between rows in both intercrops and monocrops. Cucumbers were grown as starts for 2.5 weeks before planting, and peas were planted from seed on the same planting date as cucumber starts.</p> <p> </p> <p>In the summer between January and May 2019 all fields were planted to a sunflower (<em>Helianthus annuus</em> L.) crop, which was incorporated into the soil during flowering approximately two weeks prior to cover crop planting in 2019. Because we sought to understand the effects of crop diversification given existing water and nutrient limitations on working farms, the experiment was entirely rainfed and legume N fixation was the sole external N source.</p> <p><em>Soil sampling and analysis</em></p> <p>Prior to the first cover cropping period, we collected a composite sample of 15-20 soil cores (2.5 cm diameter, 20 cm depth) on both the cover crop and fallow sides of each experimental field (<em>n</em> = 28) for analysis of baseline conditions (see Appendix B for full details). Briefly, soil was analyzed for pH, macro- and micronutrients, and soil organic matter (SOM) by the Santa Catarina State Agricultural Agency (EPAGRI) in Ituporanga, Santa Catarina, Brazil, using standard protocols (Comissão de Química e Fertilidade do Solo - RS/SC, 2016). pH was measured with a glass electrode both with and without Sikora’s buffer, and buffered pH is used throughout this paper (Tecnal TEC-11 MP). Soil organic C and total soil N to 20 cm were determined by dry combustion on a Leco TruMac CN Analyzer (Leco Corporation, St. Joseph, Michigan, USA). We measured soil texture (% clay, sand, and silt) using a total dispersion method with sodium hexametaphosphate (Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA), 1997). Bulk density was estimated from the mass of 10 fresh soil cores per treatment, with subsequent accounting for soil moisture.</p> <p> </p> <p>We measured C mineralization as a baseline indicator of soil microbial activity and biological soil fertility at the start of the experiment, and N mineralization as a response variable following the second year of cover crop treatments. Specifically, using the baseline soil sample, we conducted a short-term (24-hour) C mineralization assay to determine potentially mineralizable C (PMC), which measures the flux of CO2 following re-wetting of previously air-dried, sieved soil using a Li-Cor (Franzluebbers et al., 2000; Hurisso et al., 2016). To measure potentially mineralizable N (PMN), we conducted a two-week aerobic incubation using fresh soil collected at vegetable crop planting in the second year of the experiment (spring 2019), two weeks after cover crop and weed biomass incorporation (Drinkwater et al., 1996; Appendix B.2). PMN was calculated as the difference between extractable soil inorganic N (NH4+ and NO3-) at the start and end of the incubation. We used pre-incubation extractable inorganic N concentration (mg/kg) as a measure of soil inorganic N availability at vegetable crop planting.</p> <p> <em>Cover crop sampling and analysis</em></p> <p>Cover crop biomass sampling took place from August 28-September 2 in 2018 and September 4-10 in 2019. During peak flowering of both common vetch and black oat, we destructively harvested the aboveground biomass of cover crop mixtures and weedy fallows from two 0.5 x 0.5 m quadrats of each treatment per field. We took care to avoid treatment edges, cut plant material to the soil surface, and separated harvested plant material by species, grouping all weeds together. Aboveground biomass was dried in a forced-air oven at 60 °C for 48 hours. Following grinding in a Wiley mill to 2 mm, % N and C content was determined by dry combustion on an elemental analyzer (Leco, as above). Community-weighted means were calculated for total aboveground biomass C and N in cover crop species and weeds, to determine the overall C and N inputs to soil following incorporation of biomass on each farm. We measured biological N2 fixation in inoculated common vetch from the cover crop phase of the experiment in 2018 and 2019. Vetch N fixation was estimated using the 15N natural abundance method (Shearer and Kohl, 1986), which compares stable N isotope ratios in the legume and reference species (oat monocultures) (Appendix C).</p> <p> <em>Vegetable crop sampling and analysis</em></p> <p>To capture the full production period of both cucumber and pea crops, yield was measured in two harvests, which were approximately 14 days apart on each farm. Harvest dates ran from November 16-December 6 in 2018 and November 20-December 4 in 2019. We measured yield by weighing all harvestable fruit from three designated, representative row sections (6 plants on average per row) per crop type per treatment. Rows were sampled from the center of each treatment to reduce edge effects. We calculated yield as total crop production (g) per plant harvested in each row. Mean yield for each crop type was calculated as the average of the three harvested rows per treatment on a per-plant basis and was then aggregated to the plot and hectare level based on experimental planting densities. Total N harvested, or “N yield”, was calculated for all treatments by multiplying the % N in each vegetable crop by its yield (kg/ha) after accounting for crop water content. Using plot-level yield data, we subsequently calculated the relative yield total (Land Equivalent Ratio, LER) for intercrop treatments by farm using the standard equation (Vandermeer, 1989) (Table 1). As a relative measure of total crop production per area, when mean LER > 1, intercrops were considered to have “overyielded” compared to their component monocrops. We calculated the LER for N yield (LERN in kg N/ha) using the same formula.</p> <p> </p> <p>At the second vegetable harvest, we destructively sampled whole aboveground crop biomass, including residues and remaining fruits, from the designated experimental rows. Following the harvest, a minimum of six representative cucumbers per treatment (from different plants) per farm were washed in deionized water, air-dried, sliced, and the middle sections were combined into a homogenized, composite sample of ~100 g and then dried for one week at 60 ºC. All peas from each treatment’s subplot were washed in deionized water, air-dried, de-stemmed, chopped, and each homogenized sample (35-60 g fresh material) was subsequently dried at 60 ºC in a forced-air oven for 48 h to one week, until fully desiccated. Dried vegetable biomass residues were ground using a Wiley mill; vegetable crop samples were ground in a coffee grinder; and all vegetable samples were analyzed for % C and N on a LECO elemental analyzer.</p> <p><strong>See</strong><strong> supplemental material from Stratton et al. 2022 for further detailed information on methods.</strong></p>
Assessing the performance and efficiency of environmental DNA/RNA capture methodologies under controlled experimental conditions
<p>Growing interest and affordability of environmental DNA and RNA (eDNA and eRNA) approaches for biodiversity assessments and monitoring of complex ecosystems have led to the emergence of manifold protocols for nucleic acids (NAs) isolation and processing. Although there is no consensus on a standardized workflow, the common practice for water samples is to concentrate NAs via filtration using varying pore size membranes. Using the smallest pore is assumed to be most efficient for NAs capture from a wide range of material (including sub-cellular particles), however a trade-off must occur between detection of a meaningful molecular signal and cost/time effort when processing samples using fine pore membranes.</p> <p>Comparative studies involving formal efficiency assessments are lacking, which restricts informed decision-making around an optimized sampling approach for applications such as biosurveillance (i.e., detection and monitoring of target taxa - nuisance organisms, endangered and indicator taxa or other species of economic or cultural importance). Here, we present an experimental study using an easily cultured microalgal species (<em>Alexandrium pacificum</em>) to test different filter membranes for capturing NAs in the context of cost/time effort and cell fractions encountered in nature (whole cells, partially lysed, and naked NAs).</p> <p>The results showed no statistically significant difference between membrane types for capturing target eDNA signal from intact and partially lysed cell treatments. In terms of time effort and volume processed, higher efficiency ratings were obtained with the larger pore size (5 µm) cellulose membranes. Positively charged nylon demonstrated enhanced capture of naked NAs, and especially eRNA signal, across treatments.</p> <p>Our findings support using coarse pore size filters for adequate capture of target NA signal (from both eDNA and eRNA) with less processing time. The framework presented here can provide a quick and robust feasibility check and comparative assessment of new and existing NA processing technologies, and allows sufficient control over multiple parameters, including physical-chemical water properties, temporal scales, and concentration and type of input material.</p>
Assessment_SpacetimeConvectiveCorrections_AcousticMetacontinua_Performances_and_limitations_files
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A Quantitative Approach for the Assessment of Microservice Architecture Deployment Alternatives by Automated Performance Testing
<p>Reprodicibility package, raw data and results for the paper "A Quantitative Approach for the Assessment of Microservice Architecture Deployment Alternatives by Automated Performance Testing". </p>
PRPs' performance on the assessment of periodontitis severity and extent
<p>Partial recording protocols performance on the assessment of periodontitis severity and extent: bias magnitudes, sensibility, and specificity data</p>
How Tertiary Studies perform Quality Assessment of Secondary Studies in Software Engineering - Replication Package
<p>Replication Package for the paper:</p> <p>D. Costal, C. Farré, X. Franch, C. Quer. 2021. How Tertiary Studies perform Quality Assessment of Secondary Studies in Software Engineering. CIbSE 2021.</p> <p>Please refer to the above paper if you want to cite/use this data.</p>
Data for "Learning from mistakes - Assessing the performance and uncertainty in process-based models"
<p><strong>Data for the publication "Learning from mistakes - Assessing the performance and uncertainty in process-based models"</strong></p> <p>The corresponding python code can be found at <a href="http://github.com/MoritzFeigl/Learning-from-mistakes">github.com/MoritzFeigl/Learning-from-mistakes</a>.</p> <p>This dataset contains data of hydrological and meteorological observations of the Fortress Ski Area (Alberta, Canada) for August 8-26, 2019. It consists of input and output files for the HFLUX models calibration period (C) and the validation periods (V, V3). The input files containes additional data used in the learning from mistakes workflow. The meteorological data and part of the hydrological data were provided by John Pomeroy and the University of Saskatchewan’s Cold Water Laboratory.</p>
Blowing in the wind: Experimental assessment of clinging performance and behavior in Anolis lizards during hurricane-force winds
<p>1. Extreme weather events, such as hurricanes, can be ecologically devastating and cause widespread mortality. Recent studies in <em>Anolis</em> lizards report hurricane-induced phenotypic shifts and selection favoring morphological variation related to clinging performance. Although it is difficult to observe organismal responses during extreme events in nature, we can experimentally simulate the high-speed winds associated with hurricanes to evaluate the putative mechanism underlying observed patterns of natural selection.</p> <p>2. In this study, we used two laboratory experiments to better understand the clinging performance and behavior of <em>Anolis</em> lizards when experiencing hurricane-force winds. We assessed the physical ability of lizards when using the combined function of their claws, limbs, toepads, and other traits to resist forces pulling them off a perch. We also evaluated the combination of this physical clinging ability of lizards and their behavioral responses to avoid being blown off a perch during high winds. We assessed behavior that could decrease exposure of lizards to wind and increase their clinging ability.</p> <p>3. Clinging force measurements revealed variation in performance among species and substrates not reflected in clinging times for lizards experiencing hurricane-force winds, revealing the importance of behavior when experiencing high winds. The most arboreal species (<em>A</em>. <em>carolinensis</em>) had substantially longer clinging times on rough substrates compared to the other species, presumably due to its larger toepads for increased clinging as well as its shorter limbs that reduced drag.</p> <p>4. Under high-speed winds, lizards commonly shifted to the more protected leeward side of dowels, especially on broad and rough substrates, presumably to reduce exposure. This reveals how behavior can mediate factors influencing clinging ability during hurricanes and, in conjunction with ecologically relevant variation in morphology and substrate properties, contribute to clinging performance.</p> <p>5. Our experiments reveal that behavior strongly influences clinging performance during high winds beyond that predicted by physical traits alone. Thus, microhabitat selection of perches and the position of a lizard on its perch during a hurricane will likely have important consequences for clinging performance. This may alter how selection acts on morphological traits and influence the susceptibility of different species to these extreme weather events.</p>
Using Affine Combinations of BBOB Problems for Performance Assessment - Code and Data
<p>This repository contains the code and data for reproducibility of the paper 'Modular Differential Evolution'. </p> <p>The following files are included:</p> <p>- collect_data: Python files was used to access the affine function combinations and run the nevergrad algorithms. Resulting IOH-files are included in the 'raw_data' zipfile</p> <p>- Process_IOH_affine: code which takes the raw IOH data and turns it into the relevant csv-files which are used to create the figures. CSV files are included in 'combined_csvs.zip' (appended per setting to save space) and 'ERT_csv.zip'</p> <p>-iohana figure: code to make the per-alpha ERT figure using IOHanalyzer</p> <p>- Visualization*: notebooks which are used to generate the figures from the papers</p>
Simulated datasets used for assessing the performance of StrainIQ software
<p>StrainIQ (Strain Identification and Quantification) is a novel tool that implements a new <em>n</em>-gram based algorithm for predicting and quantifying strain-level taxa from whole genome metagenomic sequencing data. We used ten simulated dataset ( from GI tract reference genomes) to measure the sensitivity and specificity of StrainIQ software in strain identification.</p> <p>Please find the new link for this data at https://zenodo.org/record/8132164</p>
Assessment of the performance of the atmospheric correction algorithm MAJA for Sentinel-2 surface reflectance estimates
<p>Data associated to the paper "Assessment of the performance of the atmospheric correction algorithm MAJA for Sentinel-2 surface reflectance estimates", Colin, J. et al.</p> <p>Contact: jerome.colin[at]cnrs.fr<br> CESBIO Lab, Toulouse, France</p> <p>Content:<br> - APU_all_sites: APU plots for all the ACIX-II sites<br> - Maja_L2A_noadj_notopo: MAJA Level-2A subsets used to compare against ACIX-II reference reflectances for all sites and time steps<br> - quicklooks_all_sites: quicklooks for all sites</p> <p> </p>
Data inputs and analysis script to assess the performance of ASReview
<p>The aim of this analysis was to investigate the performance of ASReview for screening titles and abstracts when performing a systematic literature review of health economic evaluations. To do this, the simulation function within ASReview was used to determine the order in which articles were presented to the reviewer. Moreover, two types of stopping rules were applied on screening with ASReview, and the accuracy and efficiency were determined using retrospective analysis. The analyis is described in detail in: Oude Wolcherink MJ, Pouwels XGLV, van Dijk SHB, Doggen CJM, Koffijberg H. Health Economic Research Can artificial intelligence separate the wheat from the chaff in systematic reviews of health economic articles? Expert Review of Pharmacoeconomics & Outcomes Research. Augustus 2023 doi: 10.1080/14737167.2023.2234639.</p> <p>The 'Assess performance ASReview.zip' file includes input data used for the analysis described above and scripts to perform the analysis and to generate the figures shown in the article above. You are also able to adapt the scripts to perform your own analysis on your own data. Please, read the README file careful and follow the steps indicated there.</p>
Web Image Formats: Assessment of Their Real-World-Usage and Performance across Popular Web Browsers - Replication Package
<p>Replication package for the paper: Web Image Formats: Assessment of Their Real-World-Usage and Performance across Popular Web Browsers. </p>
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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.