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4,694 results for “data analysis”
An intracochlear electrocochleography dataset: From raw data to objective analysis using deep learning
<p>Electrocochleography (ECochG) measures electrophysiological inner ear potentials in response to acoustic stimulation. These potentials reflect the state of the inner ear and provide important information about its residual function. For cochlear implant (CI) recipients, we can measure ECochG signals directly within the cochlea using the implant electrode. We are able to perform these recordings during and at any point after implantation.<br>However, the analysis and interpretation of ECochG signals are not trivial. To assist the scientific community, we provide our intracochlear ECochG data set, which consists of approximately 5,000 signals recorded from 46 ears with a cochlear implant. We collected data either immediately after electrode insertion or postoperatively in subjects with residual acoustic hearing. This data descriptor aims to provide the research community access to our comprehensive electrophysiological data set and algorithms. It includes all steps from raw data acquisition to signal processing and objective analysis using Deep Learning. In addition, we collected subject demographic data, hearing thresholds, subjective loudness levels, impedance telemetry, radiographic findings, and classification of ECochG signals.</p>
Architecture-based Uncertainty Impact Analysis to ensure Confidentiality - Data Set
<p>Data set of the Paper "Architecture-based Uncertainty Impact Analysis to ensure Confidentiality". For more information, please see the README.md. For more information please visit https://abunai.dev</p>
Data from: Potentials of closed contour analysis in species differentiation and holotype designation: a case study on lower Norian (Upper Triassic) conodonts
<p><span>Geometric morphometric approaches become increasingly applied in the fields of biology and paleontology. Taxonomy is a good example, where a long-standing intention of scientists is to eliminate subjectivity as much as possible. In the case of biostratigraphically important conodont elements, the application of such methods is not widespread. Indeed, only a handful of studies attempted to deal with the morphological variance of conodont elements from this aspect. The detailed description of five lower Norian (Upper Triassic) taxa (<em>Ancyrogondolella quadrata, A. rigoi, A. triangularis, A. uniformis</em> and <em>Metapolygnathus mazzai</em>) is presented here based on landmarks and Fourier analysis of the P1 element and keel outlines. Both methods led to similar outcomes regarding taxonomic differentiation and exposing shape variability. Consensus shapes were generated to objectively reveal the typical contour shape of each taxon, which allowed their comparison with each other, and with the members of their respective sample population including the holotypes. The results pointed out that the holotype of a taxon is generally not an average representative, but rather a peripheral form with well-separable morphological characteristics. <em>Ancyrogondolella quadrata</em> and <em>A. rigoi</em> turned out to represent a morphological continuum with ample transitional forms between these two end-members that may cause bias in their biostratigraphic applicability; however, their combined shape variance seems to be too large for uniting them into a single species. Given the results that may be too subtle to realize based solely on qualitative observations, future taxonomic studies and type material designation could greatly benefit from the application of similar methodologies.</span></p>
Additional Data: Mapping the Evolution of Computational Thinking in Education: A Bibliometrics Analysis of Scopus Database from 1987 to 2023
<p>The following is a selection of figures and tables from a bibliometric study that will be released later. The title of this study is Mapping the Evolution of Computational Thinking in Education: A Bibliometrics Analysis of Scopus Database from 1987 to 2023.</p> <p>In the online listing of the appendix, we will find three figures (Figure 5, Figure 6, and Figure 12) and three tables (Table 3, Table 4, and Table 4), also several references related to this research. It was important to us that the core of the study that is now being carried out not be diminished in any way, which is why we chose the photos and tables we did. This study was conceived and supported by the Indonesia Endowment Fund for Education (LPDP), which the Ministry of Finance administers in the Republic of Indonesia, to evaluate current trends and research problems in computational thinking for education. The Scopus database was used, and its range of coverage was from 1987 to 2023.</p> <p> </p>
The Grainsize analysis data sheets in the Yeongil Bay, Southeastern Korea
<p><strong>The Grainsize analysis data sheets for "Incised-Valley Filling Sedimentation in a Small River Valley of a Wave-Dominated, Embayed Coast in Response to Holocene Sea Level Rise, Yeongil Bay, Southeastern Korea" The filenames show the drill-core names.</strong></p>
ipaast project - community stakeholder survey data and basic analysis
<p>This data provides the basis for the report titled</p> <p>"Ready for integrated sustainable agricultural land management? </p> <p>Are practitioners in archaeology and agriculture informed, willing, enabled, and motivated to change how they work with remote and near-surface sensing data to collaboratively address contemporary challenges in sustainable agricultural land management? "</p> <p>Data were collected in compliance with the University of Glasgow's Research Ethics Policy (Application #100200154).</p> <p>As stated in the Methods section of this report:</p> <p>"The participatory survey was conducted between May 2021 and October 2022. </p> <p>Location: The preponderance of stakeholders engaged with are professional practitioners or researchers based in the UK, Belgium, Italy, Cyprus, Spain and France. Sessions occurred remotely (online/phone), as well as on site, during workshops at the University of Glasgow, the Dalswinton Estate, Dumfries, and Manor Farm, Yedingham. </p> <p>Participants </p> <p>Selection: A sub-group of 51 high-level participants were selected from a greater network of 86 stakeholders who were engaged with during the ipaast project. </p> <p>Sector: Farmers, researchers, heritage managers, geophysicists, remote sensing specialists, statisticians, soil scientists, service providers, sensor developers, and data archivists, who all deal directly, or indirectly with datasets relating to the measurement of soil and/or plant properties (physical, chemical, microbial) were represented (Table 1) </p> <p>Expertise: Engagement with mid- to late- career specialists was prioritised, with many participants having over 20 years of experience and most having over 10 years of experience (including time during the PhD). </p> <p> </p> <p>Interview method </p> <p>Engagement with stakeholders was primarily through one-to-one interviews and structured workshop discussions, conducted either in person, or remotely over video conference or phone. In some instances, participants provided written input (see Table 2 summary). Follow-up interviews or written exchanges were used to clarify or continue discussions when required. A semi-structured approach to interviews and discussions was preferred, with a mix of general questions (see sample questions), as well as questions specifically tailored to the participants specialist background and experience. </p> <p>Sample Questions: </p> <ul> <li> <p>What types of sensing data do you use/collect? </p> </li> </ul> <ul> <li> <p>Where/how do you access/collect these data? </p> </li> <li> <p>What are your main aims/applications in using or collecting these data? </p> </li> <li> <p>How often do you access/collect, or anticipate accessing/collecting, these data to be useful to you? </p> </li> <li> <p>What spatial resolution is necessary for these data to be useful to you? </p> </li> <li> <p>What, if anything, would encourage/discourage you from sharing your data? </p> </li> </ul> <ul> <li> <p>What kinds of additional data types or additional information (metadata) might help you to better understand and use data which you have previously collected or received? </p> </li> <li> <p>What do you see as the main impacts, if any, of ecosystem service frameworks and/or recent changes to rural/environmental regulations on your work? </p> </li> <li> <p>What attitudes to sensing data do you see from other stakeholders in rural affairs? </p> </li> </ul> <p>Documentation: Where viable, interviews and workshop discussions were recorded and transcribed; alternatively, notes were made during engagement by either the interviewer and/or dedicated participant observers (e.g. at workshops). Where notes were used, specific quotes and summary reports were checked with the participants for accuracy. "</p>
Microscopy data for the paper: Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.
<p>Microscopy data for the paper: Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.</p> <p> </p> <p>List of files used for each dataset.</p> <p> </p> <p>Dataset 0 : MS2-CY5_Cyto543_560_woStim</p> <p> Images in the dataset :</p> <p> ROI001_XY1657814108_Z00_T0_merged.tif - Image Id Number: 0</p> <p> ROI002_XY1657815441_Z00_T0_merged.tif - Image Id Number: 1</p> <p> ROI003_XY1657814110_Z00_T0_merged.tif - Image Id Number: 2</p> <p> ROI004_XY1657814111_Z00_T0_merged.tif - Image Id Number: 3</p> <p> ROI005_XY1657814112_Z00_T0_merged.tif - Image Id Number: 4</p> <p> ROI006_XY1657814113_Z00_T0_merged.tif - Image Id Number: 5</p> <p> ROI007_XY1657814114_Z00_T0_merged.tif - Image Id Number: 6</p> <p> ROI008_XY1657814115_Z00_T0_merged.tif - Image Id Number: 7</p> <p> ROI009_XY1657814116_Z00_T0_merged.tif - Image Id Number: 8</p> <p> ROI010_XY1657814117_Z00_T0_merged.tif - Image Id Number: 9</p> <p> ROI011_XY1657814118_Z00_T0_merged.tif - Image Id Number: 10</p> <p> ROI012_XY1657814119_Z00_T0_merged.tif - Image Id Number: 11</p> <p> </p> <p>Datset 1 : MS2-CY5_Cyto543_560_18minTPL_5uM</p> <p> Images in the dataset :</p> <p> ROI001 - Position 1_XY1657818948_Z00_T0_merged.tif - Image Id Number: 0</p> <p> ROI001 - Position 2_XY1657818949_Z00_T0_merged.tif - Image Id Number: 1</p> <p> ROI001 - Position 4_XY1657818951_Z00_T0_merged.tif - Image Id Number: 2</p> <p> ROI001 - Position 5_XY1657818952_Z00_T0_merged.tif - Image Id Number: 3</p> <p> ROI001 - Position 6_XY1657818953_Z00_T0_merged.tif - Image Id Number: 4</p> <p> ROI001 - Position 7_XY1657818954_Z00_T0_merged.tif - Image Id Number: 5</p> <p> ROI001 - Position 8_XY1657818955_Z00_T0_merged.tif - Image Id Number: 6</p> <p> ROI001 - Position 9_XY1657818956_Z00_T0_merged.tif - Image Id Number: 7</p> <p> ROI001 - Position 10_XY1657818957_Z00_T0_merged.tif - Image Id Number: 8</p> <p> ROI001 - Position 11_XY1657818958_Z00_T0_merged.tif - Image Id Number: 9</p> <p> ROI001 - Position 12_XY1657818959_Z00_T0_merged.tif - Image Id Number: 10</p> <p> </p> <p>Dataset 2: MS2-CY5_Cyto543_560_5hTPL_5uM</p> <p> Images in the datset :</p> <p> ROI001_XY1657822809_Z00_T0_merged.tif - Image Id Number: 0</p> <p> ROI002_XY1657822933_Z00_T0_merged.tif - Image Id Number: 1</p> <p> ROI003_XY1657822934_Z00_T0_merged.tif - Image Id Number: 2</p> <p> ROI005_XY1657822936_Z00_T0_merged.tif - Image Id Number: 3</p> <p> ROI006_XY1657822937_Z00_T0_merged.tif - Image Id Number: 4</p> <p> ROI007_XY1657822938_Z00_T0_merged.tif - Image Id Number: 5</p> <p> ROI008_XY1657822939_Z00_T0_merged.tif - Image Id Number: 6</p> <p> ROI010_XY1657822941_Z00_T0_merged.tif - Image Id Number: 7</p> <p> ROI013_XY1657822944_Z00_T0_merged.tif - Image Id Number: 8</p> <p> ROI014_XY1657822945_Z00_T0_merged.tif - Image Id Number: 9</p> <p> ROI015_XY1657822946_Z00_T0_merged.tif - Image Id Number: 10</p> <p> ROI016_XY1657822947_Z00_T0_merged.tif - Image Id Number: 11</p> <p> ROI017_XY1657822948_Z00_T0_merged.tif - Image Id Number: 12</p> <p> ROI018_XY1657822949_Z00_T0_merged.tif - Image Id Number: 13</p> <p> </p> <p> </p>
Data from: Hydrodynamic analysis of bioinspired vortical cross-step filtration by computational modelling
<p><span><span>Research on the suspension-feeding apparatus of fishes has led recently to the identification of novel filtration mechanisms involving vortices. Structures inside fish mouths form a series of 'backward-facing steps' by protruding medially into the mouth cavity. In paddlefish and basking shark mouths, porous gill rakers lie inside 'slots' between the protruding branchial arches. Vortical flows inside the slots of physical models have been shown to be important for the filtration process, but the complex flow patterns have not been visualized fully. Here we resolve the three-dimensional hydrodynamics by computational fluid dynamics simulation of a simplified mouth cavity including realistic flow dynamics at the porous layer. We developed and validated a modelling protocol in ANSYS Fluent software that combines a porous media model and permeability direction vector mapping. We found that vortex shape and confinement to the medial side of the gill rakers result from flow resistance by the porous gill raker surfaces. Anteriorly directed vortical flow shears the porous layer in the centre of slots. Flow patterns also indicate that slot entrances should remain unblocked, except for the posterior-most slot. This new modelling approach will enable future design exploration of fish-inspired filters.</span></span></p>
Data for 'Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis'
<p>Additional data for 'Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis'</p>
Red deer growth data and R code for analysis
<p>Dataset and R code (Rmd-file) for analysis of seasonal growth of body weight in red deer. </p> <p>Supplementary material for the paper "Shifting seasonality of annual growth through ontogeny for red deer at northern latitudes".</p> <p>This study was part of the AgriDeer project (318575), funded by the Research Council of Norway.</p>
RNAseq data: Analysis of circRNA expression in human neuronal differentiation
<p>This dataset contains sequencing read count data related to samples from differentiating human neuroepithelial stem cells (NES) collected at days zero (NES), five (D5) and 28 (D28) of differentiation. Details on how samples were collected and how data was generated and processed are described below.</p> <p> </p> <p><em>Sample preparation</em></p> <p>NES were seeded on tissue culture flasks coated with 20 μg/ml poly-L-ornithine (Sigma-Aldrich P3655), and 1 μg/ml laminin (Sigma-Aldrich L2020). Cells were grown in DMEM/F12+GlutaMAX medium (ThermoFisher 31331093) supplemented with 0.05X B27 (ThermoFisher 17504044), 1X N2 (ThermoFisher 17502001), 10 ng/ml bFGF (fisher scientific CTP0261), 10 ng/ml EGF (PeproTech AF-100-15) and 10 U/ml penicillin/streptomycin (ThermoFisher 15140122). Medium was exchanged 50% daily and cells maintained in 5% CO2 at 37ºC, passaging once 100% confluent and seeding at a density of 5x104 cells/cm2. Neural differentiation was induced by growth factor withdrawal the day after plating with media B27 concentration increased to 0.5X. Media was exchanged 50% every second day up until D15, after which media was supplemented with 0.4 ug/ml laminin and exchanged 50% every three days. </p> <p> </p> <p><em>RNA extraction and sequencing</em></p> <p>Cells were lysed in TRIzol reagent (ThermoFischer 15596026) before separating with chloroform and mixing the aqueous phase with isopropanol as per manufacturer directions. RNA was then isolated from the isopropanol/chloroform solution using the ReliaPrep RNA Cell Miniprep kit (Promega Z6010). Libraries were prepared with Illumina Truseq Stranded total RNA RiboZero GOLD kit and sequenced on the NovaSeq6000 platform with a 2x151 setup using NovaSeqXp workflow in S4 mode flowcell.</p> <p> </p> <p><em>Data generation</em></p> <p>Raw reads were processed using cutadapt v3.2 to trim adaptor sequences and low-quality base pairs and discard short reads (options: -m 20 -e 0.1 -q 20 -O 1). The GRCh37 genome assembly was used for all alignment, annotation, and downstream analysis steps. Trimmed read weres alignment to the GRCh37 genome assembly using TopHat v2.0.9 tophat_fusion (with Bowtie v1.1.2 and Samtools v0.1.19) with –fusion-min-dist 200. BAM files have been anonymised by removal of potentially identifiable genetic variant information using BAMboozle v0.5.0 (Ziegenhain & Sandberg, 2021) with default settings. This BAM files and corresponding index (.bai) files are provided here with naming convention "<em>label.</em>bam" Information on sample labels and corresponding conditions is provided in the file 'metadata.txt'.</p> <p><br> </p>
Data for: Human atlastin-3 is a constitutive ER membrane fusion catalyst (phylogenetic and sequence analysis)
<p>Homotypic membrane fusion catalyzed by the atlastin (ATL) GTPase sustains the branched endoplasmic reticulum (ER) network in metazoans. Our recent discovery that two of the three human ATL paralogs (ATL1/2) are C-terminally autoinhibited implied that relief of autoinhibition would be integral to the ATL fusion mechanism. An alternative hypothesis is that the third paralog ATL3 promotes constitutive ER fusion with relief of ATL1/2 autoinhibition used conditionally. However, published studies suggest ATL3 is a weak fusogen at best. Contrary to expectations, we demonstrate here that purified human ATL3 catalyzes efficient membrane fusion in vitro and is sufficient to sustain the ER network in triple knockout cells. Strikingly, ATL3 lacks any detectable C-terminal autoinhibition, like the invertebrate <em>Drosophila</em> ATL ortholog. Phylogenetic analysis of ATL C-termini indicates that C-terminal autoinhibition is a recent evolutionary innovation. We suggest that ATL3 is a constitutive ER fusion catalyst and that ATL1/2 autoinhibition likely evolved in vertebrates as a means of upregulating ER fusion activity on demand.</p>
Data from: A brain-wide analysis maps structural evolution to distinct anatomical modules
<p>Brain anatomy is highly variable and it is widely accepted that anatomical variation impacts brain function and ultimately behavior. The structural complexity of the brain, including differences in volume and shape, presents an enormous barrier to define how variability underlies differences in function. In this study, we sought to investigate the evolution of brain anatomy in relation to brain region volume and shape across the brain of a single species with variable genetic and anatomical morphs. We generated a high-resolution brain atlas for the blind Mexican cavefish and coupled the atlas with automated computational tools to directly assess variability in brain region shape and volume across all populations. We measured the volume and shape of every neuroanatomical region of the brain and assessed correlations between anatomical regions in surface fish, cavefish, and surface to cave F2 hybrids, whose phenotypes span the range of surface to cave. We find that dorsal regions of the brain are contracted in cavefish, while ventral regions have expanded. This trend is true for both volume and shape, suggesting that these two parameters share developmental mechanisms necessary for remodeling the entire brain. Given the high conservation of brain anatomy and function among vertebrate species, we expect these data to reveal generalized principles of brain evolution and show that Astyanax provides a system for functionally determining basic principles of brain evolution by utilizing the independent genetic diversity of different morphs, to test how genes influence early patterning events to drive brain-wide anatomical evolution. </p>
Raw data and analysis code for "Higher-order Process Matrix Tomography of a passively-stable Quantum SWITCH"
<p>This folder contains the raw data and analysis coded need to reproduce all of the major results in the manuscript "Higher-order Process Matrix Tomography of a passively-stable Quantum SWITCH". </p>
Test data and analysis script for manuscript: Uncovering the complex relationship between balding, testosterone and skin cancers in men
<p>Test data for the manuscript entitled: "<strong>Uncovering the complex relationship between balding, testosterone and skin cancers in men"</strong><br> <br> Includes: <br> --Readme.txt<br> --folder: example<br> --folder: script</p>
Data mining and sentiment analysis on Twitter and Facebook
<p>Les données récoltées sont sur le sujet "Data mining and sentiment analysis on Twitter and Facebook". Ce jeu de donnée contient la liste des attributs principaux suivants :</p> <ul> <li>titles, titre du fichier PDF,</li> <li>authors, auteurs du fichier PDF,</li> <li>years, année de création du fichier PDF,</li> <li>ncitedby, nombre de citation,</li> <li>linkfiles, liens du fichier PDF,</li> </ul> <p>mais également des métadonnées. </p> <p>La récupération du jeu de données a été récolté sur Google Scholar. Plusieurs recherches sur Google Scholar ont été faites pour ce dernier (voir liens ci-dessous) :</p> <ul> <li>https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=twitter+data+mining+filetype%3Apdf&btnG=</li> <li>https://scholar.google.com/scholar?start=490&q=facebook+data+mining+-Twitter+filetype:pdf&hl=en&as_sdt=0,5</li> <li>https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=seniment+analyse+twitter+filetype%3Apdf&btnG=</li> </ul>
Data and code to reproduce analysis in Sacchi et al. 2023: Sex-specific fitness consequences of mate change in Scopoli's shearwaters (Calonectris diomedea)
<p>This data package contains data and code needed to reproduce the analysis reported in <strong>Sex-specific fitness consequences of mate change in Scopoli’s shearwaters (Calonectris diomedea), doi </strong>10.1016/j.anbehav.2023.05.017</p> <p>Included are:</p> <p>1) Readme file - description of all other files and variables described in datasets</p> <p>2) Appendix.Rmd = R code file containing all the analysis reported in the paper</p> <p>3) breed.txt = this data table contains data for the analysis of fitness/breeding success</p> <p>4) skip.txt = this data table contains data for the analysis of skipping behaviour</p> <p>5) females.txt = this file contains data in headed format to build CR models for the female population. Covariate indicates if an individual's life history started with event 1 (partner known, first partner) or with event 3 (partner unknown).</p> <p>6) males.txt = this file contains data in headed format to build CR models for the male population. Covariate indicates if an individual's life history started with event 1 (partner known, first partner) or with event 3 (partner unknown).</p> <p>7) gepat.pat = this file contains the matrix design necessary to run CR models with e-surge (version 2.2.3)</p> <p> </p>
Analysis of the Ground Level Enhancement GLE 60 on April 15, 2001, and its Space Weather Effects: Comparison with Dosimetric Measurements - Data
<p>Computed data that was used within the "Analysis of the Ground Level Enhancement GLE 60 on April 15, 2001, and its Space Weather Effects: Comparison with Dosimetric Measurements" paper. Computations of cones were done by OTSO using TSY89 + IGRF13 magnetic field parameters. Contains the atmospheric yield functions used for radiation computation as well as the global radiation map at 35kft for GLE60. Data is provided in .csv format.</p>
Data and statistical analysis scripts for manuscript on pennycress roots & response to nitrate using 3D gel system
<p>Data and statistical analysis scripts for manuscript on pennycress roots & response to nitrate using 3Dgel system</p> <blockquote> <p><strong>A temporal analysis and response to nitrate availability of 3D root system architecture in diverse pennycress (<em>Thlaspi arvense</em> L.) accessions</strong> - [<a href="https://doi.org/10.3389/fpls.2023.1145389">https://doi.org/10.3389/fpls.2023.1145389</a>]</p> </blockquote> <p>The following files contains:</p> <ul> <li><code>gel_data_preprocessing_20221024.R</code> - R statistics script for pre-processing data files from 3Dgel system GIARoots & DynamicRoots raw output</li> <li><code>gel_dataprocessing_20221229.R</code> - R statistics script for data processing of pre-processed 3D gel data</li> <li><code>TaGNS_N_Spring32.zip</code> - CSV data files and R statistics script for Spring32 grown under high, low, trace and zero N treatments.</li> <li><code>TaGNE_N_Accessions.zip</code> - CSV data files and R statistics script for 3 accessions under high and trace N treatments.</li> <li><code>TaGAA_N_Accessions.zip</code> - CSV data files and R statistics script for 24 diverse pennycress lines grown under high N conditions.</li> </ul>
Data files for manuscript "Re-evaluation and Re-analysis of 152 research exomes five years after the initial report reveals clinically relevant changes in 18%"
<p>#2023-06-16<br> #Summary<br> This ZIP-file contains the data files used for all analyses for the manuscript "Re-evaluation and Re-analysis of 152 research exomes five years after the initial report reveals clinically relevant changes in 18%".</p> <p><br> #File structure<br> README.txt This README file.<br> File S02 ("FileS2_conNDD-cohort.xlsx") All variants identified by Reuter et al. previously with reevaluated variants and addition variants identified in this <br> project togetehr with information about the families, individuals, samplesand the BAM files assessed in this project.<br> File S03 ("FileS3_conNDD-variants.xlsx") All variant data analyzed from the cohort. Including a sheet with thresholdes for in silico predictions tools used to predict effect of variants, <br> a table with exome wide homozygous variants in 4 categories (A45, LGD, Missense, Splice), a table with exome wide variants in 4 categories (A45, LGD, Missense, Splice)<br> filtered for domiant genes associated with neurodevelopmental disorders in SysID (Prime and Candidate list), a table with exome wide variants in 4 categories (A45, LGD, Missense, Splice) filtered for recessive genes associated with neurodevelopmental disorders in SysID (Prime and Candidate list), a table withcopy number (CN) calls for the cohort and a table withcalls for runs of homozygosity (RoH) regions.</p> <p>#Files and checksums<br> 29c4b2f3dd8985d268f50dd3e0265798 ./FileS2_conNDD-cohort.xlsx<br> a054334637b8b22a9bf743db1e348663 ./FileS3_conNDD-variants.xlsx<br> </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.