Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,696
datasets available to search
ShareScore release 0.9.0
Dataset results
3,696 results for “Reference”
SEMAFORA Semantic Reference Data Models
<p>To support the aim of the Semafora project, a series of Semantic Reference Data Models were created to provide a target semantic structure for the integration of standard archaeological survey data. </p> <p> </p> <p>The following models constitute the Semafora SRDM package:</p> <p> </p> <p>Place: This model is used to document any places associated with the archaeological survey.</p> <p> </p> <p>Institution: This model is used to document any institution associated with the survey.</p> <p> </p> <p>Period: This model is used to document the generic historical period assigned to the production of artefacts, existence of sites or other observable archaeological and historical events.</p> <p> </p> <p>Feature: This model is used to document any physical features, such as walls and other human-made structures observable on the field.</p> <p> </p> <p>Project: This model is used to document the overarching project, a part of which is the archaeological survey. Some projects may involve surveys, excavations, and other archaeological activities.</p> <p> </p> <p>Site: This model is used to document a site declared as archaeological as a result of the survey process.</p> <p> </p> <p>Digital Object: This model is used to document any type of digital asset associated with the survey.</p> <p> </p> <p>Survey Unit: This model is used to document a defined survey unit where the survey activity happens. It has both the properties of a place with dimensions and coordinates and of a physical thing from which samples can be collected.</p> <p> </p> <p>Collection: This model is used to document a collection of physical things, usually artefacts, collected while surveying.</p> <p> </p> <p>Artefact: This model is used to document individual artifacts collected from while surveying as a part of a larger collection of material things or as a singular artefact collection or documentation.</p> <p> </p> <p>Image: This model is used to document any image representing components of the archaeological survey, such as artefacts, features, places, people, etc.</p> <p> </p> <p>Observation: This model is used to document the act of observation usually associated with archaeological sites or survey units and the properties assigned to those as a result of the observation.</p> <p> </p> <p>Bibliography: This model is used to document any textual object associated with the survey or any components of it.</p> <p> </p> <p>Sample: This model is used to document a material sample of the survey unit. It partially overlaps with collection but acts as a parent sample that may contain other physical things besides human-made objects.</p> <p> </p> <p>Person: This model is used to document an individual person (alive or dead) involved in some way in the survey process.</p> <p> </p> <p>These models are intended to be used in order to guide semantic data mapping processes as well as to provide instructions for the creation of a target data semantic data management system.</p> <p> </p> <p>Each model’s semantic reference data model description is stored here as a csv. The ongoing curation and updating of these SRDMs is undertaken using the Zellij system and can be accessed here:</p> <p> </p> <p><a href="https://zellij.pythonanywhere.com/docs/list/appaCfYmUmH78z85c">https://zellij.pythonanywhere.com/docs/list/appaCfYmUmH78z85c</a></p>
Reference data set for a Norwegian medium voltage power distribution system
<p>This reference data set describes a representative Norwegian radial, medium voltage (MV) electric power distribution system operated at 22 kV. The data set is developed in the Norwegian research centre CINELDI and will in brief be referred to as the CINELDI MV reference system.</p> <p>Data for a real Norwegian distribution system were provided by a distribution grid company. The data have been anonymized and processed to obtain a simplified but still realistic grid model with 124 nodes. The data set consists of the following three parts:<br> 1. Grid data files: describe the base version of the reference system that represents the present-day state of the grid, including information about topology, electrical parameters, and existing load points.<br> 2. Load data files: comprise load demand time series for a year with hourly resolution and scenarios for the possible long-term development of peak load. These data describe an extended version of the reference system with information about possible new load points being added to the system in the future.<br> 3. Reliability data files: contain data necessary for carrying out reliability of supply analyses for the system.</p> <p>The data set is described in detail in the following data article:<br> I. B. Sperstad, O. B. Fosso, S. H. Jakobsen, A. O. Eggen, J. H. Evenstuen, and G. Kjølle, “Reference data set for a Norwegian medium voltage power distribution system,” Data in Brief, 109025, 2023, doi: 10.1016/j.dib.2023.109025.</p>
Hominid Palaeoproteomic Reference Dataset
<p>This dataset contains the 'Hominid Palaeoproteomic Reference Dataset'.We used PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline ) to generate a palaeoproteomic reference dataset of protein sequences from ancient and present-day hominids. Using the first two modules of PaleoProPhyler, we translated 176 publicly available whole genomes from extant and extinct hominid groups.</p> <p>We also translated 8 ancient hominin genomes from VCF files, including those of 3 Neanderthals and one Denisovan. Since the dataset is tailored for palaeoproteomic analyses, we chose to translate proteins that have previously been reported as present in either teeth or bones. We compiled a list of 1,696 proteins from previous works and successfully translated 1,543 of them. For each protein, both the canonical and all alternative protein coding isoforms were translated, leading to a total of around 10,058 protein sequences for each individual in the dataset.</p> <p>Details on the processing of the sequences can be found in the supplementary materials of PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline/blob/main/GitHub_Tutorial/Supplementary.pdf ). The full list of the proteins translated can be found here: https://github.com/johnpatramanis/Proteomic_Pipeline/blob/main/Reference_Protein_List.txt and a table with information on each sample included in the dataset can be found here: https://github.com/johnpatramanis/Proteomic_Pipeline/blob/main/Reference_Sample_List.csv</p> <p> </p> <p>Content: </p> <p>The zipped file contains 5 files: one txt file, two fasta files as well as two additional folders:</p> <p>- PalaeoProPhyler_Publication_Data_for_Tree.fa contains all of the sequences used to generate the phylogenetic tree presented at PalaeoProPhylers manuscript.</p> <p>- ALL_PROT_REFERENCE.fa contains all of the sequences generated as part of the Hominid Palaeoproteomic Reference Dataset described above, all in a single fasta.</p> <p>- PER_PROTEIN is a folder containing one fasta file for each protein within the Hominid Palaeoproteomic Reference Dataset. Each protein fasta file has the sequences of all individuals for that particular protein.</p> <p>- PER_SAMPLE is a folder containing one fasta file for each sample/individual within the Hominid Palaeoproteomic Reference Dataset. Each sample fasta file has the sequences of all proteins for that particular sample.</p> <p>-Reference_Protein_List.txt is a txt file containing two columns. The first column is a list of all the proteins selected to be translated. The second column describes where each of these proteins was mentioned or identified. If a protein was identified in a publication, the title of the publication is given. If a protein was identified in one of the publications of our group (E.Cappellini group) the identifier 'our samples' is given. If multiple publications supported a protein they are all given and seperated by comma.</p> <p> </p> <p>~ NOTE (!) ~</p> <p>Depending on which samples you use we highly encourage you to cite the original publication(s) from which we got the DNA data and translate the proteins from:</p> <p> </p> <p>Modern Humans:</p> <p>M Byrska-Bishop et al. “High Coverage Whole Genome Sequencing of the Expanded 1000 Genomes Project Cohort Including 602 Trios. bioRxiv. 2021”.</p> <p>Non-human great apes:</p> <p>Javier Prado-Martinez et al. “Great ape genetic diversity and population history”. In: Nature 499.7459 (2013), pp. 471–475.</p> <p>Pongos:</p> <p>Alexander Nater et al. “Morphometric, behavioral, and genomic evidence for a new orangutan species”. In: Current Biology 27.22 (2017), pp. 3487–3498.</p> <p>Neanderthal, Denisovan and other ancient anatomically modern humans:</p> <p>Kay Pr¨ufer et al. “A high-coverage Neandertal genome from Vindija Cave in Croatia”. In: Science 358.6363 (2017), pp. 655–658.</p> <p>Fabrizio Mafessoni et al. “A high-coverage Neandertal genome from Chagyrskaya Cave”. In: Proceedings of the National Academy of Sciences 117.26 (2020), pp. 15132–15136.</p>
(Annotation and metric) Thalassiosirales reference transcriptomes
<p>Here are deposited the public data recompiled for the construction of a Thalassiosirales reference database as part of a Ph.D Thesis "TEMPERATURE ACCLIMATION CAPACITY AND COLD-ADAPTATION MECHANIMS IN THALASSIOSIRALES ANTARCTIC MEMBERS". Data correspond to the annotation of 53 transcriptomes from the MMETSP of Thalassiosirales members.</p>
Dataset from the study "Analysis of the accuracy of scientific literature references provided by ChatGPT"
<p>This dataset corresponds to the study carried out to analyse 10 bibliographic references of 10 Spanish authors in the field of Information Sciences requested to the ChatGPT chatbot.</p> <p>The file "Bibliographic_references_ analysis" contains the 10 references returned by ChatGPT for each of the 10 authors (a total of 100 references), together with the variables analysed to check their authenticity.</p> <p>The "Keywords_analysis" file contains the normalisation carried out on the words considered to be key words extracted from the titles of the works, according to which a word cloud showing the frequency of occurrence could be drawn up.</p>
Lepidoptera genomics based on 88 chromosomal reference sequences informs population genetic parameters for conservation
<p>This repository contains (1) germline mutations called by the DeepVariant (v1.1.0) pipeline in VCF format; (2) rejected substitution scores calculated by the Genomic Evolutionary Rate Profiling (GERP++) software on each species and chromosome; and (3) the phylogenetic tree used as guide tree in the Cactus alignment.</p>
Proboscidean Palaeoproteomic Reference Dataset
<p>This entry contains the 'Proboscidean Palaeoproteomic Reference Dataset'.</p> <p>We used PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline ) to generate a palaeoproteomic reference dataset of protein sequences from ancient and present-day Proboscidae. Using the first two modules of PaleoProPhyler, we translated more than 35 publicly available whole genomes from extant and extinct species. Details on the processing of the sequences can be found below.</p> <p> </p> <p>Which individuals / species are included?</p> <p>The full list of individuals, the original fastq repository location and the species included in the dataset are contained within the tab seperated file 'METATADATA.txt', that also contains headers. Most individuals of the dataset belong to one of these 3 species: <em>Loxodonta africana</em>,<em> Elephas maximus</em>, <em>Mammuthus primigenius. </em></p> <p> </p> <p>Which Proteins are included?</p> <p>We compiled a small initial list of 262 proteins that had been indentified in either teeth, bone or items made out of ivory. For each protein, both the canonical and all alternative protein coding isoforms (based on the Loxodonta africana reference proteome of Ensembl) were translated, leading to more than 350 unique protein sequences for each individual in the dataset. The protein list is available in the file 'proteins.txt'</p> <p> </p> <p>How were the proteins translated/generated?</p> <p>All genetic data were downloaded from ENA (https://www.ebi.ac.uk/ena/browser/home) as fastq files and mapped onto LoxAfr3, which is the latest annotated African elephant genome in Ensembl. The scripts used for the mapping are available here: https://github.com/johnpatramanis/Mapping_Scripts . We used the resulting bam files as input for PaleoProPhyler's module 1 & 2 , using LoxAfr3 as the reference proteome.</p> <p>Other files included in the zip folder:</p> <p>- ALL_PROT_REFERENCE.fa contains all of the sequences generated as part of the Proboscidean Palaeoproteomic Reference Dataset described above</p> <p>- PER_PROTEIN is a folder containing one fasta file for each protein within the Proboscidean Palaeoproteomic Reference Dataset, each protein fasta file has the sequences of all individuals for that particular protein</p> <p>- PER_SAMPLE is a folder containing one fasta file for each sample/individual within the Proboscidean Palaeoproteomic Reference Dataset, each sample fasta file has the sequences of all proteins for that particular sample.</p>
Translation and references to commentary of Maximianus' Elegia Prima (6th c. AD)
<p>This dataset pertains to the article "Laat of te Laat? Ouderdom in de laat-Latijnse Elegieën van Maximianus (6de eeuw n.Chr.)" (related to <strong>FWO grant 1107123N</strong>) and contains the following:</p> <ol> <li>Latin text of Maximianus' Elegia Prima (edition by D'Amanti 2020)</li> <li>English translation (my own)</li> <li>Source reference</li> <li>Reference to commentary on Latin text (D'Amanti 2020)</li> </ol> <p>Physical copies supplementing this data are the following (ISBN in related identifiers):</p> <ul> <li>D'Amanti, E.R. (2020). Massimiano: Elegie. Fondazione Lorenzo Valla.</li> <li>Franzoi, A. (2014). Le Elegie di Massimiano. Adolf M. Hakkert. </li> </ul>
TauBench: A Dynamic Benchmark for Graphics Rendering (Dataset Reference Frames)
<p>TauBench is a dynamic graphics rendering benchmark dataset, targeted especially towards rendering methods relying on the reuse of temporal data. The dataset is available at <a href="https://doi.org/10.5281/zenodo.5729573">https://doi.org/10.5281/zenodo.5729573</a>, and this upload provides path traced reference frames for it in PNG format. The images are rendered with <a href="https://github.com/vga-group/tauray">Tauray</a> at 16384 samples per pixel (spp), at both 1080p and 2160p resolutions. Frame indices start from 0 and are <em>not</em> padded with leading zeroes.</p> <p>More information about TauBench is also available at <a href="https://webpages.tuni.fi/vga/taubench">https://webpages.tuni.fi/vga/taubench</a>.</p>
Standardized reference grids for spatial analyses at various grain sizes
<p><strong>Description:</strong><br> These Reference grids have been created for the <a href="https://naturaconnect.eu/">NaturaConnect project</a> and are based on an intersection of the<a href="https://www.eea.europa.eu/data-and-maps/data/eea-coastline-for-analysis-1/gis-data/europe-coastline-shapefile"> European Coastline delineation</a> and the <a href="https://gadm.org/">GADM database</a>.<br> Thee reference grids have been created in a way so that they are fully consistent with the EEA reference grid (https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2), meaning that for example two 5km gridded cells fully match a 10km grid cell in width.</p> <p><strong>Filestructure:</strong><br> ReferenceGrid_Europe_{format}_{grain}</p> <ul> <li> format is either "frac" for fractional data (which has been multiplied with 10000 to save in integer format) or binary (0,1).</li> <li> grain is provided as layers in 100m, 1000m, 5000m, 10000m, 50000m spatial resolution. Alternative aggregations can be provided on request.</li> </ul> <p><strong>File format:</strong><br> The layers are gridded geoTiff files and can be loaded in any conventional Graphical Information System (GIS) or specific analytical programming languages (e.g. R or python). In addition external pyramids (.tfw) have been precreated to enable faster rendering.</p> <p><strong>Geographic projection:</strong><br> We use the <a href="https://epsg.io/3035">Lamberts-Equal-Area Projection</a> by default for all layers in NaturaConnect. This is an equal-area (but distorted shape) projection and commonly used by European institution with a focus on the European continent. For global layers the <a href="https://epsg.io/54009">equal-area World Mollweide projection</a> is used.<br> <br> <strong>Sourcecode:</strong><br> The code to reproduce the layers has been made available in the "code" file.<br> </p>
Wikidata Subsetting: Reference-based Subsetting Experiment Datasets
<p>Files in this dataset have been produced during Flexibility experiments of Wikidata subsetting practical tools: Subsetting based on references using WDSub.</p>
Data of the characterisation of conventional 87Sr/86Sr isotope ratios in cement, limestone and slate reference materials based on an interlaboratory comparison study
<p>This dataset represents the electronic supplementary material (ESM) of the publication entitled "Characterisation of conventional <sup>87</sup>Sr/<sup>86</sup>Sr isotope ratios in cement, limestone and slate reference materials based on an interlaboratory comparison study", which is published in Geostandards and Geoanalytical Research under the DOI: 10.1111/GGR.12517. It consists of four files. 'ESM_Data.xlsx' contains all reported data of the participants, a description of the applied analytical procedures, basic calculations, the consensus values, and part of the uncertainty assessment. 'ESM_Figure-S1' displays a schematic on how measurements, sequences and replicates are treated for the uncertainty calculation carried out by PTB. 'ESM_Technical-protocol.pdf' is the technical protocol of the interlaboratory comparison, which has been provided to all participants together with the samples and which contains bedside others the definition of the measurand and guidelines for data assessment and calculations. 'ESM_Reporting-template.xlsx' is the Excel template which has been submitted to all participants for reporting their results within the interlaboratory comparison. Excel files with names of the the structure 'GeoReM_Material_Sr8786_Date.xlsx' represent the <em>R</em><sub>con</sub>(<sup>87</sup>Sr/<sup>86</sup>Sr) data for a specific reference material downloaded from GeoReM at the specified date, e.g. 'GeoReM_IAPSO_Sr8786_20221115.xlsx' contains all <em>R</em><sub>con</sub>(<sup>87</sup>Sr/<sup>86</sup>Sr) data for the IAPSO seawater standard listed in GeoReM until 15 November 2022.</p>
16S V4-V5 metabarcoding reference databases and weighted naive-bayes classifiers, dereplicated
<p>16S metabarcoding databases and naive-bayes classifiers specific to the V4-V5 region. Built from the <a href="https://www.arb-silva.de/documentation/release-138/">Silva 138.1 SSU Ref NR 99</a> database using Qiime2 (version 2023.2) and the <a href="https://github.com/BenKaehler/q2-clawback">q2-clawback plugin.</a> Includes weighted classifiers for two Earth Microbiome Project Ontology (EMPO) 3 habitat types: "sediment (saline)" and "water (saline)" , with data downloaded from <a href="https://qiita.ucsd.edu/">Qiita</a>. Sequences were dereplicated with Rescript --p-mode 'uniq' , retaining identical sequence records that have differing taxonomies.</p> <p>Primers used:</p> <p>EMP 16S 515f: GTGYCAGCMGCCGCGGTAA</p> <p>EMP 16S 926r: CCGYCAATTYMTTTRAGTTT</p> <p><strong>Stats</strong></p> <p>286,948 unique sequences</p> <p>309,567 total sequences</p> <p>46,254 unique taxa (Level 7)</p> <table> <caption>File description</caption> <thead> <tr> <th scope="col"> <table> <thead> <tr> <th>File</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>make new 16S silva V4-V5 database.md</td> <td>Markdown with code used to generate databases</td> </tr> <tr> <td>silva-138-99-seqs.qza</td> <td>Full length Silva 138.1 SSU 99 sequences</td> </tr> <tr> <td>silva-138-99-tax.qza</td> <td>Taxa for full length Silva 138.1 SSU 99 database</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-seqs.qza</td> <td>Sequences for 16S V4-V5 (primers 515f, 926r), extracted from Silva 138.1 SSU 99, generated by qiime2-2023.2 (forward compatible), dereplicated</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-taxa.qza</td> <td>Taxa for silva-138_1-99-515f_926r-seqs.qza database, dereplicated</td> </tr> <tr> <td>uniform-silva-138_1-99-515f_926r-uniq-classifier.qza</td> <td>Unweighted (uniform) naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-sediment-saline-classifier.qza</td> <td>Weighted naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, weighted for sediment-saline, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-q2_2023_2-uniq-sediment-saline-weights.qza</td> <td>Weights used to generate silva-138_1-99-515f_926r-q2_2023_2-sediment-saline-classifier.qza</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-water-saline-classifier.qza</td> <td>Weighted naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, weighted for water-saline, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-water-saline-weights.qza</td> <td>Weights used to generate silva-138_1-99-515f_926r-water-saline-classifier.qza</td> </tr> </tbody> </table> </th> <th scope="col"> </th> </tr> </thead> <tbody> </tbody> </table> <p> </p>
Wallaby Reference Simulation
<p>Data for the Wallaby Reference Simulation presented in "WALLABY – an SKA Pathfinder HI survey" by Koribalski et al., Astrophysics and Space Science volume 365, Article number: 118 (2020). This simulation relies on the SURFS simulations of cosmic structure (Elahi et al., 2018), the SHARK semi-analytic model of galaxy evolution (version 1.2.1, Lagos et al., 2018) and the STINGRAY mock sky builder (version 0.38, Obreschkow et al.).</p>
The Object Detection for Olfactory References (ODOR) Dataset.
<p>Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. Existing datasets provide instance-level<br> annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. The proposed ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories. Conducting a statistical analysis, we showcase challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. Furthermore, we provide an extensive baseline analysis for object detection models and highlight the challenging properties of the dataset through a set of secondary studies. Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception.</p> <p><strong>How to use</strong></p> <p>The annotations are provided in COCO JSON format. To represent the two-level hierarchy of the object classes, we make use of the supercategory field in the categories array as defined by COCO. In addition to the object-level annotations, we provide an additional CSV file with image-level metadata, which includes content-related fields, such as Iconclass codes [72 , 73]) or image<br> descriptions, as well as formal annotations, such as artist, license, or creation year. For the sake of license compliance, we do not publish the images directly (although most of the images are public domain). Instead, we provide links to their source collections in the metadata file (meta.csv) and a python script to download the artwork images (download_images.py).</p> <p> </p>
Soil extracellular enzyme activities in plots dominated by trees that associate with arbuscular mycorrhizal or ectomycorrhizal fungi in the N fertilized and reference watershed at the Fernow Experimental Forest, WV.
Our objective was to detect possible differences in N fertilization responses of soil extracellular enzymes in plots dominated by trees that associate with arbuscular mycorrhizal fungi (AM) or ectomycorrhizal fungi (ECM). To do this, we established a plot network of 6 AM and 6 ECM dominated 10 x 10 m plots in both the reference and N fertilized watersheds (N=24 plots) at the Fernow Experimental Forest, Parsons, WV. We assayed the potential activity of hydrolytic enzymes that release N (N-acetylglucosaminidase; NAG), phosphorus (acid phosphatase; AP), and simple carbon (ß-glucosidase; BG). In addition, we measured microbial allocation to complex C degrading oxidative enzymes phenol oxidase and peroxidase. The activities of these enzymes were measured separately in bulk mineral, rhizosphere, and organic horizon soils during the growing season in 2017.
Virgin Islands National Park: Coral Reef: Reference: Taxonomy
In early years photos were analyzed with less refined taxonomic classsification. This reference table may be used to aggregate taxa to align with coarse analysis. Use this table to define sub-categories and super-categories such as the M. annularis complex. This is a reference dataset containing the taxonomic classifications, groupings of taxonomic classifications, and functional groups. It has been updated in accordance with Budd, et al., 2012.
Extracellular enzyme activities in plots dominated by trees that associate with arbuscular mycorrhizal or ectomycorrhizal fungi in the nitrogen fertilized and reference watershed at the Bear Brook Watershed in Maine, USA during the final year of N fertilization (2016) and during the year after N fertilization ceased (2017).
Our objective was to detect possible differences in N fertilization responses of extracellular enzymes in plots dominated by trees that associate with arbuscular mycorrhizal fungi (AM) or ectomycorrhizal fungi (ECM). To do this, we established a plot network of 6 AM and 6 ECM dominated (>65% diameter at breast height) 10 x 10 m plots in the lower elevation hardwood zone of both the reference and N fertilized watersheds (N=24 plots) at Bear Brook Watershed, in Maine USA. We assayed the potential activity of hydrolytic enzymes that release N (N-acetylglucosaminidase; NAG), phosphorus (acid phosphatase; AP), and simple carbon (ß-glucosidase; BG). The activities of these enzymes were measured separately in bulk mineral, rhizosphere, and organic horizon soils in the final year of N fertilization at Bear Brook in 2016 and during the year after N fertilization ceased in 2017.
Coarse woody debris volume and mass from line transect inventory from reference stands and inventory plots of the Pacific Northwest, 1997 to 2005
These data can be used to provide an inventory of the volume of coarse woody debris stored in forests.
Mass of forest floor litter from cores in reference stands and inventory plots in the Pacific Northwest, 1992 to 2003
These data provide an inventory of the mass of forest floor organic matter stored within various forest types. This data is used to determine total organic matter, carbon, and nutrient stores in forests.
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.