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30,948 results for “Profile”
ARISA fungal community profiles for each resprouting Betula nana shrub sampled in an observational study of post-fire mycorrhizal communities across a fire-severity gradient in the Anaktuvuk River Fire burn scar, 2010
The dataset contains fungal community data from a sampling campaign in 2009, two growing seasons after the Anaktuvuk River Fire. We used molecular tools, including ARISA and fungal ITS sequencing, to characterize the mycorrhizal communities on resprouting Betula nana shrubs across a fire-severity gradient. ARISA profiles are provided for each shrub sampled in the study.
ARISA profiles for root-associated fungal communities associated with seedlings that established after fire and adjacent shrubs harvested at Finger Mountain and Nome Creek in 2009
This dataset contains the ARISA profiles of the root-associated fungal communities associated with seedlings that established after the 2004 fires and the closest resprouting shrub.
Vertical profiles of in-situ biogenic silica (bSi) from discrete rosette bottle samples from CCE-LTER starting with cruise P1706.
Samples are taken at discrete depths from rosette bottles in the California Current Ecosystem and measured for biogenic silica (bSi) concentration to create bSi depth profiles. Diatom community and physiology affect biogenic silica concentration. These data are being used to investigate the effects of Fe limitation on carbon and silica cycling in the CCE.
Cone Pond Watershed: Soil Profiles (Pedons), 1988-2023
Cone Pond Watershed: Soil Profiles (Pedons), 1988-2023 This dataset documents pedons (soil profiles) sampled at Cone Pond Watershed, Thornton, New Hampshire. Since the early 1980’s, Cone Pond, on the Pemigewasset District of the White Mountain National Forest, has been an active research satellite site to the nearby Hubbard Brook Experimental Forest. In 1988, intensive monitoring began as part of a comprehensive watershed scale ecosystem study. A weir was built on the main inlet stream, just above its mouth at Cone Pond, to monitor streamflow. Two rain gages were installed to monitor atmospheric deposition and multiple studies of major ecosystem components were initiated. Soil profiles documented in this dataset were sampled between 1988 and 2003. All descriptive profile and horizon data as well as chemical analyses of samples of genetic horizons are included in this dataset. Samples from 123 of these horizons have been accessioned into the Hubbard Brook physical sample archive; the archived mass of each is included in the horizon table. In addition to these pedons sampled in detail, a number of reconnaissance observations, made at a lower level of detail, and without sampling, were made in 2023 in order to validate a hydropedologic soil model created at Hubbard Brook. These data are included in a separate table. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Mirror Lake High-Frequency Dissolved Oxygen and Temperature Profiles
This lake metabolism data was collected by HOBO Temperature loggers and miniDOT loggers that were deployed at Mirror Lake Central Buoy over the deepest part of the lake (11m) on 2023-08-23. HOBO loggers were deployed vertically at depths 0.25m, 2m, and 4m, and miniDOT loggers were deployed vertically at depths 0.5m, 1m, and 6m. Sensors were tied to nylon climbing rope at these different depths and anchored to a buoy. Loggers were later removed on 2023-09-08. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest in the White Mountains of New Hampshire, which is operated and maintained by the USDA Forest Service, Northern Research Station.
MCR LTER: Coral Reef: Water Column: Offshore Water Profiles: CTD Transects
This dataset contains vertical water column profiles along transects heading offshore over the forereefs on the three sides of Moorea. These sides are designated north, southeast, and southwest. This process study intended to look for a hypothesized buoyant flow that would account for the counter-clockwise (CCW) circulation around the island. These transects were designed to look for cross-shore density gradients associated with the CCW flow. The SeaBird SBE19-Plus Profiler (CTD), outfitted with WetLabs FLNTURT-221 fluorometer, was cast at closely-spaced stations along transects oriented approximately normal to the three sides of the island. Standard CTD parameters (conductivity, temperature, and density) were used to calculate salinity, depth, and other physical quantities. No bottle samples were collected on these cruises.
MCR LTER: Coral Reef: Water Column: Bay Water Profiles: CTD Transects
This dataset contains vertical water column profiles along transects in Opunohu Bay and Cook’s Bay on the north side of Moorea. The SeaBird SBE19-Plus Profiler (CTD), outfitted with WetLabs FLNTURT-221 fluorometer, was cast at closely-spaced stations along transects down the length of the bays. Standard CTD parameters (conductivity, temperature, and density) were used to calculate salinity, depth, and other physical quantities. No bottle samples were collected on these cruises. Profiles in the bays were obtained when sea conditions offshore prohibited sampling over the forereef.
Ecosystem-scale rainfall manipulation in a Pinon-Juniper Woodland: Volumetric Water Content (VWC) Profile Data (2009-2013 )
Climate models predict that water limited regions around the world will become drier and warmer in the near future, including southwestern North America. We developed a large-scale experimental system that allows testing of the ecosystem impacts of precipitation changes. Four treatments were applied to 1600 m2 plots (40 m × 40 m), each with three replicates in a piñon pine (Pinus edulis) and juniper (Juniper monosperma) ecosystem. These species have extensive root systems, requiring large-scale manipulation to effectively alter soil water availability. Treatments consisted of: 1) irrigation plots that receive supplemental water additions, 2) drought plots that receive 55% of ambient rainfall, 3) cover-control plots that receive ambient precipitation, but allow determination of treatment infrastructure artifacts, and 4) ambient control plots. Our drought structures effectively reduced soil water potential and volumetric water content compared to the ambient, cover-control, and water addition plots. Drought and cover control plots experienced an average increase in maximum soil and air temperature at ground level of 1-4° C during the growing season compared to ambient plots, and concurrent short-term diurnal increases in maximum air temperature were also observed directly above and below plastic structures. Our drought and irrigation treatments significantly influenced tree predawn water potential, sap-flow, and net photosynthesis, with drought treatment trees exhibiting significant decreases in physiological function compared to ambient and irrigated trees. Supplemental irrigation resulted in a significant increase in both plant water potential and xylem sap-flow compared to trees in the other treatments. This experimental design effectively allows manipulation of plant water stress at the ecosystem scale, permits a wide range of drought conditions, and provides prolonged drought conditions comparable to historical droughts in the past – drought events for which wide
Bulk RNA-Seq PBMC data of SLE patients and healthy volunteers/ profiling of 29 individual immune cell types as well as PBMCs of healthy donors
<p>This Zenodo project contains processed gene expression data from two publicly available data sets. It includes the gene expression data of peripheral blood mononuclear cells (PBMCs) of systemic lupus erythematosus (SLE) patients as well as healthy volunteers (GSE122459). The project also comprises the bulk RNA-Seq profiling of 29 immune cell types as well as PBMCs of healthy individuals (GSE107011). In both cases, the raw RNA-Seq data was downloaded, aligned and processed. The gene expression data is available in form of a count matrix (GSE107011) or count matrix and transcript-per-million (TPM) values (GSE122459). For the latter, an annotation file is attached. Further details are provided in the information file. </p>
TIPP 2.0.0 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>TIPP<br><strong>SoftwareVersion: </strong>2.0.0<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://github.com/smirarab/sepp<br><strong>DockerImage:</strong> stefanjanssen/docker_profiling_tools:tipp<br><strong>IsBiobox:</strong> True<br><strong>BioboxYAMLFile:</strong> https://zenodo.org/record/3629567/files/biobox.yaml?download=1<br><strong>ReferenceDatabase:</strong> 2015<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> docker run \<br>--volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \<br>--volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \<br>--volume="/path/to/output:/bbx/mnt/output:rw" \<br>--volume="/path/to/output/metadata:/bbx/metadata:rw" \<br>--volume="/path/to/output/cache:/cache:rw" \<br>stefanjanssen/docker_profiling_tools:tipp
Bracken 2.5 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>Bracken<br><strong>SoftwareVersion: </strong>2.5<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://github.com/jenniferlu717/Bracken<br><strong>DockerImage:</strong> cami/bracken:2.5<br><strong>IsBiobox:</strong> True<br><strong>BioboxYAMLFile:</strong> https://zenodo.org/record/3629567/files/biobox.yaml?download=1<br><strong>ReferenceDatabase:</strong> Kraken standard db built May 2019<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> docker run \<br>--volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \<br>--volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \<br>--volume="/path/to/output:/bbx/mnt/output:rw" \<br>--volume="/path/to/output/metadata:/bbx/metadata:rw" \<br>--volume="/path/to/output/cache:/cache:rw" \<br>cami/bracken:2.5
MetaPalette 1.0.0 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>MetaPalette<br><strong>SoftwareVersion: </strong>1.0.0<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://doi.org/10.5281/zenodo.1730624<br><strong>DockerImage:</strong> stefanjanssen/docker_profiling_tools:commonkmers<br><strong>IsBiobox:</strong> True<br><strong>BioboxYAMLFile:</strong> https://zenodo.org/record/3629567/files/biobox.yaml?download=1<br><strong>ReferenceDatabase:</strong> https://zenodo.org/record/1749272<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> docker run \<br>--volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \<br>--volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \<br>--volume="/path/to/output:/bbx/mnt/output:rw" \<br>--volume="/path/to/output/metadata:/bbx/metadata:rw" \<br>--volume="/path/to/output/cache:/cache:rw" \<br>--volume="/path/to/reference_database:/exchange/db:rw" \<br>stefanjanssen/docker_profiling_tools:commonkmers
ESSENTIA analysis of audio snippets from the Million Song Dataset Taste Profile subset
<p>This upload includes the ESSENTIA analysis output of (a subset of) song snippets from the Million Song Dataset, namely those included in the Taste Profile subset. The audio snippets were collected from 7digital.com and were subsequently analyzed with ESSENTIA 2.1-beta3. Pre-trained SVM models provided by the ESSENTIA authors on their website were applied.</p> <p>The file <strong>msd_song_jsons.rar </strong>contains the ESSENTIA analysis output after applying the SVM models for highlevel feature extraction. Please note that these are 204317 files.</p> <p>The file <strong>msd_played_songs_essentia.csv.gz </strong>contains all one-dimensional real-valued fields of the jsons merged into one csv file with 204317 rows.</p> <p>The full procedure and subsequent analysis is described in</p> <p>Fricke, K. R., Greenberg, D. M., Rentfrow, P. J., & Herzberg, P. Y. (2019). Measuring musical preferences from listening behavior: Data from one million people and 200,000 songs. <em>Psychology of Music</em>, 0305735619868280.</p>
Structural Profiling of Web Sites in the Wild
<p>The dataset contains and processes results of a large-scale survey of 708 websites, made in December 2019, in order to measure various features related to their size and structure: DOM tree size, maximum degree, depth, diversity of element types and CSS classes, among others. The goal of this research is to serve as a reference point for studies that include an empirical evaluation on samples of web pages.</p> <p>See the Readme.md file inside the archive for more details about its contents.</p>
Top-down and bottom-up vegetal and animal product metabolic profiles for 29 european countries (EU27 + United Kingdom + Norway).
<p>This repository contains the data needed to reproduce the results in:</p> <p>Cadillo-Benalcazar, J.; Renner, A. Giampietro, M. (2020). A multiscale integrated analysis of the factors characterizing the sustainability of food systems in Europe. Journal of Environmental Management. Volume 271, 1 October 2020, 110944. <a href="https://doi.org/10.1016/j.jenvman.2020.110944">https://doi.org/10.1016/j.jenvman.2020.110944</a></p> <p>Renner, A.; Cadillo-Benalcazar, J.; Benini, L., Giampietro, M. (2020). Environmental pressure of the European agricultural system: Anticipating the biophysical consequences of internalization. Ecosystem services. Special Issue: Agro-futures. Volume 46, December 2020, 101195. <a href="https://doi.org/10.1016/j.ecoser.2020.101195">https://doi.org/10.1016/j.ecoser.2020.101195</a></p>
Seawater temperature profiles from Expendable Bathythermograph (XBT) probe deployments during the Antarctic Circumnavigation Expedition (ACE)
<p><strong>Dataset abstract</strong></p> <p>This data set contains vertical seawater temperature profiles measured by Expendable Bathythermograph (XBT) probes that were deployed in the Southern Ocean during the Antarctic Circumnavigation Expedition (ACE) on board the R/V Akademik Tryoshnikov. 40 XBT probes were deployed during legs 2 and 3 of the expedition in the period 25th January, 2017 to 17th March, 2017. The XBT probes are manufactured and distributed by T.S.K./Sippican Tsurumi-Seiki Co. Ltd., Yokohama, Japan (http://www.tsk-jp.com) and are of the type T-07, which is rated at a ship speed of up to 15 knots. These probes have a measuring time of 123 seconds and maximum measurement depth of about 789 m. Probes were launched from a handheld device from the stern of the ship either on the port or starboard side while the ship was moving. The deck unit recorded the temperature and the time since the probe was launched. This time was then converted to depth using the known fall rate of the probe in seawater and the coefficients provided by the manufacturer (WMO standards; Hanawa et al., 1995). The profiles were corrected for known surface biases (Kizu and Hanawa, 2002; Uehara et al., 2008). We provide the raw data, the data produced by using the coefficients provided by the manufacturer, and a corrected version in which we apply an empirical correction based on a comparison with CTD data (Henry et al., 2019), where XBT profiles were launched alongside the CTD deployment. The data has been quality controlled by comparing it to a number of CTD profiles. Data is provided at full vertical resolution and a 1-m averaged resolution. In addition, we provide derived variables such as surface mixed layer depth (temperature threshold) estimates. We are grateful to the crew of the R/V Akademik Tryoshnikov and AARI for donating these probes to our project. Their use-by date had expired, however this was not seen as an issue. This data set provides insights into the hydrography of the Southern Ocean during one austral summer season and complements the CTD temperature profiles measured during ACE by filling in the gaps between CTD stations.</p> <p><strong>Dataset contents</strong></p> <p>Data:</p> <ul> <li>ace_xbt_raw/ace_xbt_YYYYMMDD_xxxx_uuuuuuuuuuuu.RAW, data file, comma-separated values</li> <li>ace_xbt_wmo_hanawa95_fullres/ace_xbt_YYYYMMDD_xxxx_uuuuuuuuuuuu.XBT, data file, comma-separated values</li> <li>ace_xbt_wmo_hanawa95_1m/ace_xbt_YYYYMMDD_xxxx_uuuuuuuuuuuu_1m.XBT, data file, comma-separated values</li> <li>ace_xbt_corrected_fullres/ace_xbt_YYYYMMDD_xxxx_uuuuuuuuuuuu.XBT, data file, comma-separated values</li> <li>ace_xbt_corrected_1m/ace_xbt_YYYYMMDD_xxxx_uuuuuuuuuuuu_1m.XBT, data file, comma-separated values</li> </ul> <p>Auxiliary data:</p> <ul> <li>ace_xbt_mld_tavg.csv, data file, comma-separated values</li> <li>ace_merged_ctd_xbt_mld_tsavg.csv, data file, comma-separated values</li> </ul> <p>Figures:</p> <ul> <li>figure1.pdf, metadata, portable document format</li> <li>ace_xbt_figures/ace_xbt_YYYYMMDD_xxxx_1m.pdf, metadata, portable document format</li> </ul> <p>Metadata:</p> <ul> <li>ace_xbt_deployment_summary.csv, metadata, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This seawater temperature profile dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
CO2 absorption coefficients (ABSCO) tables for the 4700 to 5100 cm-1 region using Voigt profile accounting for the line-mixing
<p>The attached files include the calculated absorption coefficient (in cm-1) for the 4700 to 5100 wavenumber region when using the Voigt profile accounting for the line-mixing parameter. The structure of the tables is as follows:</p> <p>Col 1- Wavenumber (in cm-1)</p> <p>Col 2- Absorption coefficient using the Voigt profile</p> <p>Col 3- Absorption coefficient using the Voigt profile including the first-order line-mixing</p> <p>Col 4- Absorption coefficient using the Voigt profile including the full line-mixing</p> <p>The JSON files contain the calculation conditions such as pressure, temperature, and mixing ratios of CO2 with a unique ID referring to the specific table.</p> <p>We note that for the Q branch lines and denser regions, the use of the first-order line-mixing is not effective in obtaining the absorption coefficient and the use of full line-mixing is recommended.</p> <p>For more information on how the calculation is performed, see ref. [JQSRT 151, 88 (2015)].</p>
Examples of ALD saturation profiles in rectangular channel LHAR structures simulated with a diffusion-reaction model
<p>Examples of atomic layer deposition ALD saturation profiles in rectangular channel LHAR structures simulated with a diffusion-reaction model by Ylilammi et al. (Journal of Applied Physics <strong>123</strong>, 205301 (2018); <a href="https://doi.org/10.1063/1.5028178">https://doi.org/10.1063/1.5028178</a>) as function of (a) Reactant A pulse time <em>t</em>, (b) Reactant A partial pressure <em>p</em>, and (c) sticking coefficient <em>c</em>. Parameters used in the simulation, if not otherwise stated: channel height <em>H</em> 500 nm, temperature 250°C, 250 cycles, inert carrier gas partial pressure 500 Pa, Reactant A molar mass 100 g/mol, inert carrier gas molar mass 28 g/mol, Reactant A diameter 0.600 nm, inert gas diameter 0.374 nm, adsorption capacity 4 metal atoms per nm<sup>2</sup>, density of the material grown 3.5 g/cm<sup>3</sup>, pulse time 0.1 s, Reactant A partial pressure 100 Pa, sticking coefficient 0.01. </p> <p>Abbreviations: ALD = atomic layer deposition, LHAR = lateral high aspect ratio</p>
Protein elution profiles accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"
<p>Key to files</p> <p><strong>Experiment_Order.csv</strong></p> <ul> <li>Description: Meta details of each experiment.</li> <li>Format: experiment_name,ExperimentID_order,tissue,experiment_type,spec,ExperimentID</li> </ul> <p><strong>Fraction_Details.csv</strong></p> <ul> <li>Description: Meta details of each fraction</li> <li>Format:FractionID,frac_order,ExperimentID</li> </ul> <p><strong>plant_virNOG_orthology.csv.gz</strong></p> <ul> <li>Description: Conversion between orthogroup and protein IDs.</li> <li>Format:ID,ProteinID,spec</li> </ul> <p><strong>orthogroup_annotation.csv.gz</strong></p> <ul> <li>Description: Orthogroup annotations</li> <li>Format:ID,Annotation,arath_genenames,arath_Entries,arath_Entry_names,arath_Protein_names,disruptions,tair_disruptions,lloyd2012_LOFs,arath_functions,arath_misc,pathway,unipathway,BioCyc,Reactome,BRENDA,kegg_pws,ec,arath_masses,arath_protein_names,arath_GO,devstages,tissues,tair,araport,orysj_genenames,orysj_Entries,orysj_Entry_names,orysj_Protein_names,orysj_disruptions,orysj_functions,orysj_misc</li> </ul> <p><strong>panplant_tidy_elution_virNOG.csv.gz</strong></p> <ul> <li>Description: Tidy (long format) table of counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups</strong> for all experiments. Includes parts per million in each fraction. </li> <li>Format: ExperimentID,FractionID,ID,Total_PeptideCount,spec,ExperimentID_order,FractionID_order,abundance_ppm</li> </ul> <p><strong>panplant_tidy_elution_protcount.csv.gz</strong></p> <ul> <li>Description: Tidy (long format) table of counts of peptide spectral matches (PSMs) for all observed <strong>proteins</strong> for all experiments. </li> <li>Format: ExperimentID,FractionID,ProteinID,ProteinCount,spec,ExperimentID_order,FractionID_order</li> </ul> <p><strong>panplant_wide_elution_virNOG.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups</strong></li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <p><strong>panplant_wide_elution_virNOG_annot.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups</strong>, includes annotation columns.</li> <li>Format: OrthogroupID,[Annotations],[Fractions]</li> </ul> <p><strong>panplant_wide_elution_expnorm.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles reporting per-fractionation experiment-normalized peptide spectral matches (PSMs) for all observed<strong> orthogroups</strong></li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <p><strong>panplant_wide_elution_expnorm_annot.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles reporting per-fractionation experiment-normalized peptide spectral matches (PSMs) for all observed<strong> orthogroups</strong>, including columns with annotations</li> <li>Format: OrthogroupID,[Annotations],[Fractions]</li> </ul> <p><strong>[experiment_name].virNOG.wide.gz</strong></p> <ul> <li>Description: Elution profile of raw counts of peptide spectral matches (PSMs) for all observed<strong> orthogroups</strong> in one experiment</li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <ul> </ul> <p><strong>[experiment_name].protcount.wide.gz</strong></p> <ul> <li>Description: Elution profile of raw counts of peptide spectral matches (PSMs) counts for all observed <strong>proteins </strong>in one experiment</li> <li>Format: ProteinID,[Fractions]</li> </ul> <p><strong>[species]_specconcat.virNOG.wide.gz</strong></p> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups </strong>from a particular species. Only present for species with more than one experiment. </li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <p><strong>[species]_specconcat.protcount.wide.gz</strong></p> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>proteins</strong> from a particular species. Only present for species with more than one experiment. </li> <li>Format: ProteinID,[Fractions]</li> </ul> <p> </p> <ul> </ul> <p>Species codes</p> <p>|Code | Species | Common name | Use |<br> |---|---|---|<br> | arath | Arabidopsis Thaliana | Arabidopsis | <br> | braol | Brassica oleracea | Broccoli |<br> | cansa | Cannabis sativa | hemp | <br> | cerri | Ceratopteris richardii | C-fern | <br> | chlre | Chlamydomonas reinhardtii | Chlamydomonas |<br> | chqui | Chenopodium quinoa | Quinoa | <br> | orysj | Oryza sativa var. japonica | Rice |<br> | selml | Selaginella moellendorffii | Selaginella | <br> | sollc | Solanum lycopersicum | Tomato | <br> | wheat | Triticum Aestivum | Wheat | <br> | soybn | Glycine max | Soybean | <br> | cocnu | Cocos nucifera | Coconut | </p> <p>| maize | MAIZE | maize | </p> <p> </p>
Profile Data for Stylus Device Calibration using Ball Standard
<p>X and Z profile data for the simulation of Stylus device calibration using Ball standard</p> <p>M1000: Number of profile points -20 means: 20 points at the left of the peak</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.