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HOSENG trial – HOme-based oral SElf-testiNG for absent and refusing individuals during a door-to-door HIV testing campaign: a cluster randomised clinical trial in rural Lesotho
<p>These are pseudo-anonymised data from the HOSENG randomized trial: " HOSENG trial – HOme-based oral SElf-testiNG for absent and refusing individuals during a door-to-door HIV testing campaign: a cluster randomised clinical trial in rural Lesotho". The data dictionary explains the data available in the dataset. Between July 2018 and December 2018, 10516 eligible individuals from 106 consenting villages were enrolled from two districts of Lesotho, and followed up for a maximum of 120 days. Main manuscript reference, DOI: <a href="https://doi.org/10.1016/s2352-3018(20)30233-2">10.1016/S2352-3018(20)30233-2. </a>The protocol was published, DOI:10.1186/s13063-019-3469-2.</p>
A Dataset of sEMG and Self-Perceived Fatigue Levels for Muscle Fatigue Analysis
<p>Muscle fatigue is a risk factor for injuries in athletes and workers. This brings relevance to the study of this biochemical process to allow its identification and prevention.</p> <p>This dataset contains raw surface electromyographic (sEMG) data collected using the Delsys Trigno system, focusing on eight muscles, four per arm, from 13 healthy adult participants. Participants performed a series of 12 upper-body dynamic movements, consisting of 4 uni-articular and 2 complex/compound movements per arm. In addition to raw sEMG data, the dataset includes participants' self-reported fatigue levels. </p> <p><strong>Data Structure:</strong></p> <ul> <li><strong>sEMG Data.zip:</strong> Recorded in 1259 Hz, formatted as .csv.</li> <li><strong>self_perceived_fatigue_index.zip:</strong> Time-stamped fatigue ratings in 0-2 level, recorded at 50hz.</li> <li><strong>Protocol:</strong> Includes trial description, movements illustration and sampling frequencies.</li> <li><strong>Code</strong>: Jupyter Notebook file containing the base code to read and compute classic fatigue metrics such as Median Frequency and Mean Frequency.</li> <li><strong>Metadata:</strong> Includes participant anthropometrics, exercise habits and caffeine intake on the day of the trials.</li> </ul> <p>This dataset may contribute to the testing of new fatigue detection algorithms and analysis of the underlying mechanisms.</p>
Wearable data and self reported fatigue scores from a remote observational study in Sjogren's disease, SLE and healthy participants
<p>Fatigue is a subjective, complex, and multi-faceted phenomenon, commonly experienced as tiredness. However, pathological fatigue is a major debilitating symptom associated with overwhelming feelings of physical and mental exhaustion. To date, there is no consensus about reliable quantitative assessments of fatigue.</p> <p>We collected observational data for a period of one month from 296 participants (healthy volunteers, Sjogren’s Syndrome, and Systemic Lupus Erythematosus patients) in the United States. Data comprised continuous multimodal digital data from Fitbit, including heart rate, physical activity, and sleep daily features, and app-based daily and weekly questions (e.g., pain, mood, general physical activity, and fatigue). When matching both sensor data and PROs, and excluding missing data, the dataset contains data from 183 subjects and 3950 recording days.</p> <p>The analysis of the association of digital data to self-reported fatigue was published at <em><strong>Rao C., et. al. (2023), Association of digital measures and self-reported fatigue: a remote observational study in healthy participants and participants with chronic inflammatory rheumatic disease, Frontiers in Digital Health</strong></em>.</p> <p>Demographics, digital parameters, and other information on this dataset can be found in the aforementioned manuscript and related supplementary material. Details on the data files can be found under README.txt.</p>
Laboratory Dataset on Self-ignition of Carbon-Rich Soil
<p>The file attached contains a complete set of experimental data from carbon-rich soil self-heating ignition cubic basket experiments for a range of soil inorganic content (IC) ranging from 3% to 86%. The experiments were carried out in a thermostatically controlled oven with thermocouples for measuring the ambient and soil temperatures. The data reported includes the dates of experiments, volume of soil baskets being tested, oven ambient temperature, inorganic content present in the sample, bulk density of the soil and if the sample ignited or not. This data is in support of the journal paper:</p> <p>F. Restuccia, X. Huang, G. Rein, <strong>Self-ignition of Natural Fuels: Can Wildfires of Carbon-Rich Soil Start by Self-heating?</strong>, <em>Fire Safety Journal </em>2017, http://doi.org/10.1016/j.firesaf.2017.03.052.</p>
Case study of self-compacting, fiber reinforced, lightweight concrete, intended for production of precast elements
<p>This a dataset set to paper entitled: "Case study of self-compacting, fiber reinforced, lightweight concrete, intended for production of precast elements".</p> <p>Dataset is one excel file divided in various sheets containing:</p> <ol> <li>Properties of used aggregates</li> <li>Initial properties of concrete</li> <li>Composition of concrete</li> <li>Concrete with fibres</li> <li>Final concrete</li> </ol>
Economical routes to size-specific assembly of self-closing structures
<p>This data contains images related to a publication on the self-assembly of DNA origami particles (<a href="https://www.science.org/doi/10.1126/sciadv.ado5979">https://www.science.org/doi/10.1126/sciadv.ado5979</a>). In this work, we conduct self-assembly experiments with various unique subunit types that target two different diameters of tubule structures.</p> <p>We provide image data of tubules that are associated with the probability distributions reported across several figures in the main text. Images of tubules are in the ZIP archives and show the section of tubules we analyzed to produce the probability distributions in the manuscript. Each folder of images has an associated CSV file that relates an image name to the type of tubule that the image was identified as. Tubule types have "m" and "n" values.</p> <p>We provide full tomogram reconstruction data for the multicomponent tubules that are shown in Figure 2 of the main text. In the ZIP archive, each tubule image has two files associated with it: a REC file that contains the tomogram reconstruction data and an MDOC file that contains imaging metadata. REC files can be opened with the open-source software IMOD.</p> <p>We provide raw image data of pitch- and width-controlled tubules that have been labeled with gold nanoparticles. These accompany the representative images in Figure 4 in the main text. (Pitch Controlled 4-color with GNPs.zip, Width Controlled 4-color with GNPs.zip).</p> <p>We provide raw image data of length-controlled tubules. These images accompany Figure 5 in the main text. (Length Controlled Tubule Images.zip)</p> <p><strong>Associated publication citation:</strong></p> <div> <p><span>Thomas E. Videbæk <em>et al., </em></span><span>Economical routes to size-specific assembly of self-closing structures. </span><span><em>Sci. Adv. </em></span><span><strong>10</strong>, </span><span>eado5979 </span><span>(2024). </span><span>DOI:<a href="https://doi.org/10.1126/sciadv.ado5979">10.1126/sciadv.ado5979</a></span></p> </div>
EPR Characterization of the Heme Domain of a Self-Sufficient Cytochrome P450 (CYP116B5)
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation.</li> <li>Files are with filename extensions: .<strong>DSC</strong>, .<strong>DAT</strong>, .<strong>m</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions .<strong>DSC</strong>, .<strong>DTA.</strong></li> <li>EPR spectroscopic simulation with filename extension .<strong>m</strong>.</li> </ul> <ul> <li>CW and Pulse X-band experiments were performed on a Bruker Elexys E580 X-band spectrometer (microwave frequency 9.68 GHz) equipped with a cylindrical dielectric cavity and a helium gas-flow cryostat from Oxford Inc.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP5_20220225_CW</strong> folder includes X-band CW-EPR spectroscopic measurements, original data are in DTA/DSC/txt.</li> <li>Files in <strong>PARACAT_WP5_20220225_HYSCORE</strong> folder includes HYSCORE spectroscopic measurements, data are in .DTA, .DSC, .txt formats.</li> <li>Files in <strong>PARACAT_WP5_20220225_MATLAB</strong> folder includes computer simulations/analyses of the EPR measurements, data are in .m formats.</li> </ul> </li> </ul>
Experimental data for "Yu-Shiba-Rusinov bands in a self-assembled kagome lattice of magnetic molecules"
<p>Here, we provide all original data used in the manuscript "Yu-Shiba-Rusinov bands in a self-assembled kagome lattice of magnetic molecules"</p> <p>We acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through projects 277101999 (CRC 183, project C03) and FR2726/10-1.</p>
Data of the article Analysis of the self-archiving policies of journals in the highest rank category of the Finnish journal classification system within computer science, physics and electronic engineering
<p>The publication forum level three journals representing the three fields of science of computer science, computer science and electrical engineering were identified by utilizing the MinEdu field search filter while searching for the top-ranked journals from the publication channel search (https://www.tsv.fi/julkaisufoorumi/haku.php?lang=en), which is based on Field of Science, Statistics Finland classification (https://www.stat.fi/meta/luokitukset/tieteenala/001-2010/index_en.html). The data were extracted during august 2017 consists of total of 127 individual journals. It is worth noting that circa 30 journals were classified into more than one fields of sciences under scrutiny. First, the journals were divided into representing gold and hybrid model journals. Second, green open access policies of the identified hybrid journals were analyzed using Laakso’s (2014) publisher policy coding framework. Also publishers of the individual journals were identified and subsequently added to the data.</p> <p>NOTE! The data includes the shortest embargo to either institutional or subject repositories. For example, Elsevier had no embargo to opening accepted manuscripts from arXiv subject repository and thus no embargoes to Elsevier's journals are included within this datasheet.</p> <p>Data is in CSV. format</p> <p> </p> <p> </p>
Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality Dataset
<p>This dataset accompanies the paper “Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality” published in the Computer and Graphics journal from Elsevier. It contains 3 datasets related to the experiments described in the paper. All datasets are in “.csv” format and can be easily loaded by statistical analysis tools (e.g. a dataset can be loaded in r using the command read.csv(“filename.csv”)). It also contains the C# Unity implementation of the distortion function presented in the paper.</p> <p>Paper reference:</p> <p>Galvan Debarba H, Boulic R, Salomon R, Blanke O, Herbelin B. Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality. Computers & Graphics. 2018; ISSN 0097-8493. Elsevier.</p> <p>DOI: doi.org/10.1016/j.cag.2018.09.001</p>
Data for manuscript Marmet, Studer, Lemoine, Grazioli, Bertholet & Gmel (2019). Reconsidering the associations between self-reported alcohol use disorder and mental health problems in the light of co-occurring addictions in young Swiss men. Plos One. DOI: 10.1371/journal.pone.0222806.
<p>Dataset for the manuscript Marmet, Studer, Lemoine, Grazioli, Bertholet & Gmel (2019). Reconsidering the associations between self-reported alcohol use disorder and mental health problems in the light of co-occurring addictions in young Swiss men. Plos One. DOI: 10.1371/journal.pone.0222806.</p> <p>The dataset contains all data needed to reproduce the results in the above cited manuscript. Variable description and labels can be found in the codebook. For further information on the instruments used please refer to the manuscript.</p> <p>The data was collected between April 2016 and March 2018 in Switzerland by the C-SURF study (<a href="http://www.c-surf.ch">www.c-surf.ch</a>). Participants were on average 25 years old when they answered the questionnaires. The final sample size used in the manuscript is 5516. Please note that the dataset contains 25 datasets created with multiple imputation, therefore there are no missing values in the dataset.</p> <p>The research protocol for this study was approved by the Human Research Ethics Committee of the Canton Vaud (Protocol No. 15/07). Data collection was funded by the Swiss National Science Foundation (FN 33CSC0-122679, FN 33CS30_139467, FN 33CS30_148493)</p>
Geometric Frustration Directs the Self-assembly of Nanoparticles with Crystallized Ligand Bundles
<p>This is the supporting dataset of the publication "Geometric Frustration Directs the Self-assembly of Nanoparticles with Crystallized Ligand Bundles".</p> <p><a href="https://doi.org/10.1021/acs.jpcb.4c04562">https://doi.org/10.1021/acs.jpcb.4c04562</a></p> <p>The description of the dataset can be found in the file README.txt</p>
Ionic conductivity, viscosity, and self-diffusion coefficients of novel imidazole salts for lithium-ion battery electrolytes
<p>This entry contains the data related to the publication<br><strong>A. Szczęsna-Chrzan <em>et al.</em>, “Ionic conductivity, viscosity, and self-diffusion coefficients of novel imidazole salts for lithium-ion battery electrolytes,”<em> J. Mater. Chem. A</em>, vol. 11, no. 25, pp. 13483–13492, 2023, doi: 10.1039/D3TA01217D.</strong><br><br>It contains experimentally determined conductivity, viscosity and self-diffusion coefficients of anions of the Hückel-type salts lithium 4,5-dicyano-2-(trifluoromethyl)imidazolide (LiTDI), lithium 4,5-dicyano-2-(pentafluoroethyl)imidazolide (LiPDI) and lithium 4,5-dicyano-2-(n‑heptafluoropropyl)imidazolide (LiHDI) for various concentrations of the conducting salts (0 M - 1.5 M) in a solvent mixture containing ethylene carbonate (EC) and ethyl methyl carbonate (EMC) in a ratio of 3:7 by weight.</p> <p>The Python scripts used for the analysis of the NMR data are also included in the dataset.</p>
Local Governance in Ukraine during the full-scale Russian invasion. – Merged data from online surveys of local self-government authorities by the Congress of Local and Regional Authorities of the Council of Europe in 2022 and Kyiv School of Economics in 2024.
The dataset includes responses from two waves of online surveys targeting local self-government representatives in Ukraine, with a focus on crisis governance during the ongoing Russian war. The first wave was conducted from August 30 to September 20, 2022, by the Congress of Local and Regional Authorities of the Council of Europe, yielding 241 responses (16% of all Ukrainian local communities). The second wave was conducted by Kyiv School of Economics from January 1 to March 12, 2024, with 181 responses (14% of government-controlled municipalities). Data formats include CSV and SAV files, along with an XSL codebook for both waves. The merged dataset comprises 442 responses from small, medium, and large municipalities under varied security conditions, with a total file size of approximately 4 MB.
Dataset for "Self-assembly of dodecagonal and octagonal quasicrystals in hard spheres on a plane"
<p>This dataset contains supplementary data for the publication:<br> <em>Self-assembly of dodecagonal and octagonal quasicrystals in hard spheres on a plane</em><br> E. Fayen, M. Impéror-Clerc, L. Filion, G. Foffi, and F. Smallenburg</p> <p> </p> <p><strong>Contents:</strong><br> The folder Data contains subfolders for each of the simulations performed for the construction of Fig. 3 of the main paper. Each folder name contains the size ratio q, the fraction of large particles x_L, and the packing fraction e in the file name. Note that the fraction of large particles x_L is related to the quantity x_S used in the paper via x_L = 1 - x_S.</p> <p>For simulations that were run for longer times, an additional folder with the same naming convention is included in the subfolder Long.</p> <p>Each simulation subfolder includes:</p> <p>- A coordinate file "last.sph" representing the final configuration of the simulation in plain text format. In this file, the first line specifies the number of particles, the second line the box size and non-additivity parameter Delta, and the remaining lines the coordinates of the particles. Each line containing coordinates consists of a letter indicating particle species (a or b), three spatial coordinates (with the z-coordinate always zero), and the particle radius. All lengths are given in units of the large-particle diameter.</p> <p>- An image of the final particle configuration "snapshot.png".</p> <p>- An image representing the associated scattering pattern, obtained by taking the Fourier transform of the particle coordinates and plotting the result as a function of the 2D wave vector on a logarithmic color scale.</p> <p> </p> <p>Additionally, the main folder contains a set of HTML files ("table_q*.html") that provide an overview of the snapshots and scattering patterns for each size ratio (specified in the file name). The HTML table for each size ratio uses the images from the "Data" and "Data/Long" subfolders as appropriate, and depends on the included "SAtable.css" and "SAtable.js" files. Within each table, clicking on one of the entries will enlarge the associated images.<br> <br> </p>
Processing of MODIS-Aqua data with Self-Organizing Maps NeuroVaria method for the southern canary upwelling system
<p>Abstract</p> <p>This ocean color dataset is derived from MODIS_Aqua sensor measurements covering the Southern Canary upwelling system. The raw L1A measurements were downloaded from NASA's Ocean Color web site and then processed using the Ocean Biology Processing Group's (OBPG) Multi-Sensor Level-1 to Level-2 (MSL12) code. The l2gen program, based on its standard process, generates Level-2 parameters consisting of the top of atmosphere radiance, the radiance of each ocean and atmosphere component, the measurement angles, Level-2 flags, ... The top of atmosphere radiance is pre-corrected to keep only a dependence on the diffuse transmittance, the aerosol contribution and the water leaving radiance.</p> <p><br> The pre-corrected product and measurement angles are assimilated using the Self-Organizing Map<br> NeuroVaria (SOM-NV) code (Diouf et al., 2013). SOM-NV is an algorithm based on two statistical models<br> that classify a dataset into a map, and then use the information from that map to deliver atmospheric and oceanic parameters from the satellite observation.</p> <p>The parameters of interest are the remote sensing reflectance spectra (Rrs(λ)) and the aerosol optical thickness (AOT) at 869 nm (aot_869). The Rrs at blue (443 and 488 nm) and green (547 nm) are used to calculate chlorophyll-a concentration from the OBPG OCx algorithm (chl_ocx, O'Reilly et al., 1998; Mobley et al., 2016).</p> <p>These geophysical parameters are projected onto a fixed grid at 1/96° resolution and archived in a daily netcdf format files. Each file contains five visible reflectances Rrs(λ) (with λ = 412, 443, 488, 531, and 547 nm), chl_ocx, aot_869, and latitude and longitude coordinates. These parameters are described in the files, along with the global attributes.</p> <p><br> The netcdf files are formatted as follows: SOM-NV-Ayyyydddhhmmss.nc; where yyyy = year; ddd = Julian<br> day; hh = hour; mm = minute; ss = second. The extension "Ayyyydddhhmmss.nc", corresponds to the name<br> of the MODIS_aqua file of the day. When two input files exist for the same day, within 5 minutes, the two<br> scans are concatenated and the orbit keeps the name of the second file.<br> All files are compressed internally to a size of 4, to facilitate transfers.</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>Résumé</p> <p>Ce jeu de données de couleur de l’eau est issu des mesures du capteur MODIS_Aqua sur la partie sud du système d’upwelling des Canaries. Les mesures brutes L1A ont été téléchargées du site Ocean Color de la NASA, puis traitées à l’aide du code de traitement « Multi-Sensor Level-1 to Level-2 (MSL12) » du groupe Ocean Biology Processing Group (OBPG). La version standard du programme l2gen génère les paramètres de niveau 2 constitués de la luminance totale mesurée, de la luminance de chaque composante du système océan-atmosphère, des angles de mesures, des masques de niveau 2, …. La luminance totale est pré-corrigée pour ne garder qu’une dépendance à la transmittance diffuse, à la contribution des aérosols et à la luminance marine.<br> <br> Le produit pré-corrigé et les angles de mesure sont assimilés à l’aide du code Self-Organizing Map NeuroVaria (SOM-NV) de Diouf et al. (2013). SOM-NV est un algorithme basé sur deux modèles statistiques qui permettent de classer un ensemble de données sur une carte, puis d’utiliser les informations de cette carte pour restituer les paramètres atmosphériques et océaniques de l’observation satellite.<br> <br> Les paramètres restitués sont les spectres de réflectance marine (Rrs(λ)) et l’épaisseur optique des aérosols (AOT) à 869 nm (aot_869). Les Rrs au bleu (443 et 488 nm) et au vert (547 nm) servent à calculer la concentration en chlorophylle-a à partir de l’algorithme OCx de OBPG (chl_ocx).<br> <br> Ces paramètres géophysiques sont projetés sur une grille fixe à 1/96° de résolution et archivés au format de fichiers netcdf journaliers. Chaque fichier netcdf contient cinq réflectances du visible Rrs(λ) (avec λ = 412, 443, 488, 531 et 547 nm), la chl_ocx, l’aot_869, et les coordonnées latitude et longitude. Ces paramètres sont décrits dans les fichiers, ainsi que les attributs globaux.</p> <p><br> Les fichiers netcdf sont formatés comme suite : SOM-NV-Ayyyydddhhmmss.nc ; avec yyyy = année ; ddd =<br> jour julien ; hh = heure ; mm = minute ; ss = seconde. L'extension "Ayyyydddhhmmss.nc", correspond au<br> nom du fichier MODIS_aqua du jour. Dans le cas où deux fichiers existent pour un même jour, à 5 minutes<br> près, les deux scans sont concaténés et l'orbite garde le nom du deuxième fichier.<br> Tous les fichiers sont compressés en interne à un niveau 4, pour faciliter le transfert.</p>
Dataset for : A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification
<p>We present a novel solution combining Large Language Model (LLM) capabilities with Formal Verification strategies to falsify and automatically repair software vulnerabilities. Initially, we employ Bounded Model Checking (BMC) to locate the software vulnerability and derive a counterexample. Relying on mathematical proofs, counterexamples provide evidence that the system behaves incorrectly or contains a vulnerability, thereby preventing the generation of false positive alerts. The counterexample that has been detected, along with the source code, are provided to the LLM engine. Our approach involves establishing a specialized prompt language for conducting code debugging and generation to understand the vulnerability's root cause and repair the code. Finally, we use BMC to verify the corrected version of the code generated by the LLM. As a proof of concept, we create \esbmcai based on the Efficient SMT-based Context-Bounded Model Checker (ESBMC) and a pre-trained Transformer model, specifically gpt-3.5-turbo, to detect and fix errors in C programs. We generated a dataset comprising $1{,}000$ C code samples, each consisting of $20$ to $50$ lines of C code. Experimental results show that our proposed method achieved an impressive success rate of up to $80$\% in repairing vulnerable code, encompassing buffer overflow, arithmetic overflow, and pointer dereference failures. To our knowledge, \esbmcai represents the first proposal for a pioneering initiative to integrate a Large Language Model (LLM) with software model checking. We advocate that this automated approach has the potential to incorporate into the software development lifecycle's continuous integration and deployment (CI/CD) process. </p> <p> </p> <p>The uploaded dataset contains 1000 codes, each comprising 20 to 50 lines of C code generated with gpt-3.5-turbo. The material also consists of a version of ESBMC statically compiled with all dependencies, a classifier script, and the output file.</p> <p> </p> <p> </p>
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera I: Site Attribute Data 2022
This dataset contains site characteristics collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Data includes detailed site characteristics collected at the site level. Each site included three 10 m * 2 m plots (A, B, and C) laid in a single 30 m transect (or, where constrained, in parallel).
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera II: Tree Inventory Data 2022
This dataset contains tree combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Tree species, diameters (DBH where possible, otherwise BD), condition (living/dead, standing/fallen, etc), and component combustion are recorded for every tree in each 10 m * 2 m plot.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera III: Shrub Inventory Data
This dataset contains shrub combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Shrub species, stem diameters (BD), and component combustion were recorded for every shrub in each 10 m * 2 m plot.
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.