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242 results for “Spatial Dataset”
Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms
<h2>Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms</h2> <div>The dataset captures a Human-Robot Spatial Interaction (HRSI) scenario between a person and the TIAGo robot. It focuses specifically on human-goal and human-robot spatial interaction in an indoor environment, captured from the perspective of a 3D Velodyne VLP-16 LiDAR mounted on the TIAGo robot. It includes:</div> <ul> <li>rosbags containing: Velodyne LiDAR point clound, robot and human state (position, orientation and velocities);</li> <li>CSV files containing trajectories of the person and the robot generated by post-processing the rosbags;</li> <li>the map of the environment extracted from the TIAGo robot.</li> </ul> <p><strong>15 participants</strong> took part in the experiment, with the dataset capturing <strong>5 minutes of HRSI motion for each participant</strong>.</p> <h3>Experiment Description</h3> <p>The experiment and data collection occurred in a laboratory room of the University of Lincoln (UK), measuring 5 x 8.2m. <br>Fifteen participants (6 females, aged between 25 and 55) took part in the experiment. Seven of them were used to work with a robot. They were required to walk between four goal positions and avoid the robot if a cross occurs. A predefined rectangular path was set for the TIAGo robot to navigate along the room and generate frequent interactions with the participants.</p> <p>The experimental procedure can be described as follows. Each participant started from one of the four target positions. The next target position was randomly chosen by the participant, who then started moving towards it. Upon reaching the goal position, the participant stopped there and randomly chose the next goal, repeating the process for 5 minutes. In this experimental setting, the robot was considered by the participant as an obstacle to avoid while walking towards their target positions.</p> <h3>Directory Structure</h3> <p>Dataset<br>|<br>|____Map: folder containing the map of the environment extracted from the TIAGo robot<br>|<br>|____RosBags: forder containing the rosbag for each partipant<br>| |____A1.bag<br>| |____A2.bag<br>| |____A3.bag<br>| |____A4.bag<br>| |____A5.bag<br>| |____A6.bag<br>| |____A7.bag<br>| |____A8.bag<br>| |____A9.bag<br>| |____A10.bag<br>| |____A11.bag<br>| |____A12.bag<br>| |____A13.bag<br>| |____A14.bag<br>| |____A15.bag<br>|<br>|____Trajectories: postprocessed trajectories extracted for the rosbag files <br> |____A1_traj.csv<br> |____A2_traj.csv<br> |____A3_traj.csv<br> |____A4_traj.csv<br> |____A5_traj.csv<br> |____A6_traj.csv<br> |____A7_traj.csv<br> |____A8_traj.csv<br> |____A9_traj.csv<br> |____A10_traj.csv<br> |____A11_traj.csv<br> |____A12_traj.csv<br> |____A13_traj.csv<br> |____A14_traj.csv<br> |____A15_traj.csv</p>
Dataset on Spatial Analysis and Clustering of Deforestation in the Amazon Biome: Spatio-Temporal Patterns and Priority Areas
<p>The dataset was developed with the aim of facilitating the development of a methodology to identify and evaluate deforestation patterns and trends in the Amazon. This innovative method combines deforestation alerts from the Real-Time Deforestation Detection System (DETER) with detailed information on various land categories, including environmental protection areas, settlements, rural properties, undesignated public forests, indigenous lands, and conservation units. The integration of this robust data allowed for the precise identification of areas at risk of deforestation, significantly strengthening monitoring and control activities aimed at combating deforestation in the Amazon region.</p> <p> </p> <p><strong>Spatial resolution</strong></p> <p>The data are available with a spatial resolution of 25 x 25 km (625 km²) and cover the Amazon biome.</p> <p> </p> <p><strong>Temporal resolution </strong></p> <p>Period of observed data: 2017 and 2021</p> <p> </p> <p><strong>Coordinate reference system</strong> </p> <p>Geographic Coordinate System with Datum SIRGAS 2000 (EPSG:5880)</p> <p> </p> <p><strong>Data format</strong></p> <p>Data is provided as Shapefile.</p> <p> </p> <p><strong>Dataset usage</strong> </p> <p>It is free to use, but please make sure to cite the repository and our paper properly if you use this dataset.</p> <p> </p> <p><strong>Publication & further information</strong></p> <p>For additional scenario information, please contact Francisco Gilney Silva Bezerra (franciscogilney@gmail.com).</p>
Regulatory T cell therapy is associated with distinct immune regulatory lymphocytic infiltrates in kidney transplants: Spatial transcriptomic dataset and images
<p>The outputs of the NanoString GeoMx DSP platform were concatenated into three xlsx files, each illustrating a separate experiment along with their sample annotations. This technique analyzes protein or RNA abundance within regions of interest (ROIs) or specific cell segments selected based on histological features and immunofluorescence. In this repository, the concatenated GeoMx output files are presented, along with PowerPoint presentations for each biopsy that show immunofluorescence images of the selected ROIs and/or cell segments.</p> <ul> <li><strong>Protein_Full ROI:</strong> This experiment measured the abundance of 41 proteins in discrete regions of interest (ROIs) within transplant kidney biopsies.</li> <li><strong>Protein_Rare cell:</strong> This experiment measured the abundance of 40 proteins in specific cell segments, such as CD4+FoxP3- cells vs. CD4+FoxP3+ cells, within transplant kidney biopsies.</li> <li><strong>RNA:</strong> This experiment measured the abundance of 90 genes in discrete ROIs within transplant kidney biopsies.</li> </ul>
Rome (ITALY) - Urban Agriculture spatial dataset (years 2007 and 2013)
<p><strong>Motivation</strong></p> <p>The data in this dataset is a spatial inventory of <strong>urban agriculture</strong> (UA) carried out in the city of Rome (Italy) (Grande Raccordo Anulare (GRA)). UA areas where identified with a multi-step and iterative procedure by using different web-mapping tools, especially multitemporal Google Earth images, and ancillary data such as Google Street View and Bing Maps.</p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p> <p><strong>Disclaimer</strong></p> <p>Despite our best efforts to validate the data, some information may be incorrect.</p> <p><strong>Description of the dataset</strong></p> <p><em><strong>Typologies of UA</strong></em></p> <ul> <li><strong>Residential garden: </strong>Private parcel near single houses (e.g. backyard), villas, buildings, industrial and commercial activities, generally managed by property owners. Cultivation is diversified ranging from leafy vegetables to herbs and fruit trees. Production is intended for self-consumption and/or for hobby purposes.</li> <li><strong>Community garden: </strong>A large area subdivided into multipleplots managed individually (i.e. allotment) or collectively by a group of people. Crop production is intended for self-consumption. Land is assigned by the Municipality; several cases of land cultivated without authorization are also common.</li> <li><strong>Urban farm: </strong>Parcel managed by professional farmers with an intensive and an advanced cropping system. The cultivation can be specialized or oriented to high diversity vegetables. The production is intended for market. The mapping procedure focus exclusively on horticulture, vineyard, olive groves and orchard.</li> <li><strong>Institutional garden: </strong>Parcel managed by institutions or organizations like schools, religious center, prisons and non-profit organizations. The production is generally intended for self-consumption and less frequently for trade. Several gardens in this category are intended for social purposes (e.g. recreation,education, etc.).</li> <li><strong>Illegal garden: </strong>Parcel isolated, cultivated without authorization organized and managed individually or by a few people. Localization occurs on unused or abandoned areas owned by public bodies or private subjects. The production is intended for self-consumption.</li> </ul> <p><em><strong>Land use typologies</strong></em></p> <ul> <li><strong>Horticulture: </strong>annual crops generally seed sown in spring or summer (tomatoes, lettuce, zucchini, cucumbers, peppers).</li> <li><strong>Vineyard: </strong>grape vines grown in order to produce wine or table grape.</li> <li><strong>Olive groves: </strong>olive trees grown in order to produce olive oil or table olives.</li> <li><strong>Orchards: </strong>mixed trees such as orange, stone fruit, pome fruit, olive trees.</li> <li><strong>Mixed crops: </strong>an area grown with a mix of horticulture crops and fruit trees, not divisible.</li> </ul> <p><strong>Credit</strong></p> <p>Pulighe G., Lupia F. (2016) <em>Mapping spatial patterns of urban agriculture in Rome (Italy) using Google Earth and web-mapping services. </em><strong>Land Use Policy</strong> 59(2016) 49-58.</p> <p><a href="http://www.sciencedirect.com/science/article/pii/S0264837716300059"><em>www.sciencedirect.com/science/article/pii/S0264837716300059</em></a></p>
Dataset for: An optimisation approach for designing wildlife corridors with ecological and spatial considerations
<p>The fragmentation of wildlife habitats caused by anthropogenic activities has reduced biodiversity and impaired key ecosystem functions. Wildlife corridors play an important role in linking detached habitats. The optimal design of such corridors considering spatial, ecological, and economic factors is addressed in this paper.</p> <p>We present a novel graph-theoretic optimisation approach and a mixed-integer linear programming model to determine an optimal wildlife corridor connecting two given habitat patches. The model maximises the total quality of the corridor and satisfies pre-specified corridor width and length requirements under a resource constraint.</p> <p>Compared to the corridor design models presented in the literature, our model is conceptually simpler, and it is computationally convenient. We applied the model to a real data set for Eldorado National Forest in California, USA, involving 1,363 irregular land parcels.</p> <p>The model can be extended to design multiple corridors that connect two or more existing habitat patches.</p>
Datasets used for the spatial mode-based calibration (SMoC) paper
<p>Datasets for a journal paper "Spatial mode-based calibration (SMoC) of forecast precipitation fields from numerical weather prediction models".</p> <p>In the SMoC paper, the performance of SMoC is evaluated by applying it to forecasts of substantive precipitation events over the Brisbane Drainage Basin in eastern Australia. This repository provides datasets of precipitation forecasts (3 years) and observations (33 years) at a grid spacing of 0.05° x 0.05° and on a daily basis for this case study: (1) Forecasts: "3-year forecast data.nc"; (2) Corresponding observations: "3-year observation data.nc"; (3) Long-term observations: "30-year observation data.nc". These three files are compressed into "Forecast and observation data.zip".</p> <p>Author name: Pengcheng Zhao. Affiliation: Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne. Email: pengcheng@student.unimelb.edu.au.</p>
Auxiliary Euro-Calliope datasets: Spatial data to represent a European energy system model at several spatial resolutions
<p>Main output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow.</p> <p>This output provides similar data to <a href="https://doi.org/10.5281/zenodo.3246302">https://doi.org/10.5281/zenodo.3246302</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with two key differences:</p> <ol> <li>The spatial extent has been expanded to include Iceland.</li> <li>Two new spatial resolutions have been added: `ehighways` and `ehighways_disaggregated`.</li> </ol> <p>`ehighways` defines 98 regions based on the result of work undertaken in the European Commission Seventh Framework Programme project e-HIGHWAY 2050 [1]. The regions cover 35 European countries; 19 are described at a national resolution and the rest at a subnational resolution. Those at a subnational resolution are aggregated from NUTS3-2006 statistical units. `ehighways_disaggregated` provides the data at the resolution of statistical units in Europe, which is then aggregated to produce the data at the `ehighways` resolution. The mapping from statistical units to ehighways regions is defined in `./ehighways/statistical_units_to_ehighways_regions.csv`. `./ehighways/units.png` shows a map of the resulting 98 `ehighways` regions. The region colours are used to help differentiate regions and have no other meaning.</p> <p>This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.</p> <p>[1] Anderski, T., Surmann, Y., Stemmer, S., Grisey, N., Momot, E., Leger, A.-C., Betraoui, B., and van Roy, P. (2014). European cluster model of the Pan-European transmission grid (e-HIGHWAY 2050)</p>
Dataset for the study "Changes in audio-spatial working memory abilities during childhood: The role of spatial and phonological development"
<p>Working memory is a cognitive system devoted to storage and retrieval processing of information.<br> Numerous studies on the development of working memory have investigated the<br> processing of visuo-spatial and verbal non-spatialized information; however, little is known<br> regarding the refinement of acoustic spatial and memory abilities across development.<br> Here, we hypothesize that audio-spatial memory skills improve over development, due to<br> strengthening spatial and cognitive skills such as semantic elaboration. We asked children<br> aged 6 to 11 years old (n = 55) to pair spatialized animal calls with the corresponding animal<br> spoken name. Spatialized sounds were emitted from an audio-haptic device, haptically<br> explored by children with the dominant hand’s index finger. Children younger than 8<br> anchored their exploration strategy on previously discovered sounds instead of holding this<br> information in working memory and performed worse than older peers when asked to pair<br> the spoken word with the corresponding animal call. In line with our hypothesis, these findings<br> demonstrate that age-related improvements in spatial exploration and verbal coding<br> memorization strategies affect how children learn and memorize items belonging to a complex<br> acoustic spatial layout. Similar to vision, audio-spatial memory abilities strongly depend<br> on cognitive development in early years of life.</p> <p>Data in the file are divided into six sheets based on the age of the participants and the experimental condition, either call-call or call-name. Each sheet contains six columns: Participant ID, age and gender are the first three. The last three columns instead refer to the test parameters: the number of attempts, the audio-anchor and the score. In details, the number of attempts indicates the number of trials needed to pair the sounds. The audio-anchor provides a measurement of the exploration strategy. It accounts for how many consecutive attempts the child begins by touching the same speaker while the score takes into account the frequency of touches on the same speakers: the more the participant returns on the same stimulus location, the lower the score.</p>
GPR-BMP-SPI: High spatial resolution multi-scale SPI datasets over China from January 1984 to December 2020
<p>The datasets include standard precipitation index (SPI) at 1-month, 3-month, 6-month, 9-month and 12-month scales over the main terrestrial lands of China from January 1984 to December 2020. The SPI datasets were produced by blending the information from meteorological stations, and precipitation products, as well as topographical and geographical variables based on Gaussian process regression (GPR) models.</p> <p>The meteorological station data are from the China Meteorological Data Service Centre. Five precipitation products are used: (1) CHIRPS Daily: Climate Hazards Group InfraRed Precipitation With Station Data (Version 2.0 Final); (3) ERA5-Land Monthly Averaged by Hour of Day - ECMWF Climate Reanalysis; (3) FLDAS: Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System; (4) PERSIANN-CDR: Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record; (5) TerraClimate: Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces.</p> <p>The maps of the difference of the confidence intervals (the upper prediction limit minus the lower prediction limit) at a significance level of 95% are also provided to show the spatial uncertainty of every single SPI map.</p> <p>The drought events were counted during 1984-2020 at annual and seasonal scales. The variables related to the drought events are presented in “Drought_Event.zip”.</p> <p>Reference: He, Q., Wang, M., Liu, K., Li, B., & Jiang, Z. (2023). Spatiotemporal analysis of meteorological drought across China based on the high-spatial-resolution multiscale SPI generated by machine learning. <em>Weather and Climate Extremes</em>, <em>40</em>, 100567.</p> <p> </p>
Daily satellite and gauge observed rainfall dataset (1980-2019) for Oman at 1km spatial resolution in GeoTIFFs
<p>This is a re-gridded daily TRMM datasets. It has been resampled to 1km spatial resolution. The datasets have also been projected to UTM 40N</p>
Dataset for Spatial Variations in the Osteocyte Lacuno-canalicular Network Density and Analysis of the Connectomic Parameters
<p>This dataset is a representative case of the loaded tibia of a C57BL/6 mouse at the mid-shaft. The image pixel size is 0.303 by 0.303 um, and the z-depth is 0.296 um. </p> <p>To generate, analyse, and quantify the osteocyte lacuno-canalicular network, it requires 'Tool for Image and Network Analysis (TINA)' which can be acqruied from https://gitlab.mpikg.mpg.de/rummler/TINA.git. A demonstration has been included on using TINA.</p>
HDCA fetal lung spatial proteomics example datasets
<p>The data provided in this repository is published alongside <a href="https://doi.org/10.1101/2024.01.25.577163" target="_blank" rel="noopener noreferrer">this preprint</a> titled as 'High-parametric protein maps reveal the spatial organization in early-developing human lung' and <a href="https://github.com/CellProfiling/HDCA-FetalLung-SpatialProteomics" target="_blank" rel="noopener noreferrer">this code repository</a>. The preprint and GitHub repository provide further metadata and analysis information. When using the data in this repository, please cite the preprint under DOI: <a href="https://doi.org/10.1101/2024.01.25.577163" target="_blank" rel="noopener noreferrer">https://doi.org/10.1101/2024.01.25.577163</a>.</p>
Partitioning of water and CO2 fluxes at NEON sites into soil and plant components: a five-year dataset for spatial and temporal analysis
<p>This dataset includes estimates of transpiration, evaporation, soil respiration, and plant net photosynthesis obtained using five partitioning approaches. Flux components are available at 47 NEON sites over a period of five years. Additional meteorological inputs and water-use efficiency data are also included.</p>
Processed CODEX Datasets from - Discovery and Generalization of Tissue Structures from Spatial Omics Data
<p>This entry provides access to processed CODEX data files of four studies analyzed in the article "Discovery and Generalization of Tissue Structures from Spatial Omics Data". Details of datasets can be found in the STAR Methods section of the article.</p> <p>For each dataset, a zip file containing multiple comma-separated values (CSV) files is included.</p> <p>Each region is assigned an unique identifier (e.g., DKD_kidney_001), and its related data files are:</p> <ul> <li>`{region_id}.cell_data.csv`, a table containing three columns: "CELL_ID", "X", and "Y". This table provides centroid locations for all cells segmented in this region.</li> <li>`{region_id}.expression.csv`, a table containing multiple columns: "CELL_ID", "DAPI", "CD45", etc. This table provides detailed protein biomarker expression quantified for all cells in this region.</li> <li>`{region_id}.scgp_annotations.csv`, a table containing two columns: "CELL_ID" and "SCGP". This table provides SCGP/SCGP-Extension annotations for all cells in this region.</li> </ul> <p>Code base for SCGP is also included in this entry. Please refer to <a href="https://gitlab.com/enable-medicine-public/scgp">https://gitlab.com/enable-medicine-public/scgp</a> for the latest codes, questions, and/or issues. Raw CODEX data and images will be accessible through links posted at the code base. Raw data will also be available from lead contact (A.E.T.) upon request.</p>
Spatial dataset of breeding bird territories in East-Estonian forested landscapes, 2020-2022
<p>Please cite this dataset as: <strong>Lõhmus, A. 2024. A high-precision dataset of breeding bird distributions in forested landscapes in Estonia. Data in Brief, 57, 111012. https://doi.org/10.1016/j.dib.2024.111012</strong></p> <p>The dataset depicts the distribution of all breeding bird pairs across a 14.3 km2 area in East Estonia, along River Ahja. The area comprises three adjacent, mostly forested landscape plots (forest land 81%), of which one plot (A) was mapped in three years (2020-2022) and the others once (plot B in 2021; plot C in 2022). The bird data includes the most likely centroids of each nesting territory of each species (ideally, nest location) as interpreted from multiple records; all the field data have been collected and interpreted by the author. The fieldwork included standard multi-visit mapping of nesting territories (on average, 7–8 visits from April to July), and each bird data point (5398 in total) includes a spatial accuracy assessment. In total, 98 bird species were detected. The bird data are accompanied with map layers depicting the study area borders and forest stand descriptions to facilitate habitat and landscape analyses; the available formats are MapInfo 10.5, ESRI Shape File, and csv; the co-ordinates are WGS84. Detailed descriptions of the data are included in a separate uploaded text file. The data have been used for several publications as indicated in the Reference list, notably for habitat analyses of woodland birds (Certhia familiaris; Cuculus canorus; Lophophanes cristatus; Turdus viscivorus) and for assessing forest management impacts on bird assemblages. </p> <p>NOTE 19.07.2024: The following corrections are to be made (v2 coming soon; thus far please consider). 1) Birddata – one TETURO record missing in Plot C. 2) Birddata – one BONBON record (Pair ID 4379) under the species code TETBON. 3) Birddata_explanations – codes for field Type missing; should read as follows: Pinus = Pinus sylvestris dominated; Picea = Picea abies dominated; Con = Mixed conifer forest (≥80% in total); Mix = Conifer-deciduous mixture (neither ≥80%); Bet = Betula sp. dominated; Ainc = Alnus incana dominated; Aglu = A. glutinosa dominated; Ptre = Populus tremula dominated; Dec = Other deciduous forest (≥80% in total).</p>
TAU Spatial Sound Events 2019 - Ambisonic and Microphone Array, Evaluation Datasets
<p>This package consists of two evaluation datasets, <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> and <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong>. These datasets contain recordings from an identical scene, with <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> providing four-channel First-Order Ambisonic (FOA) recordings while <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> provides four-channel directional microphone recordings from a tetrahedral array configuration. Both formats are extracted from the same microphone array. The recordings in the two datasets consist of stationary point sources from multiple sound classes each associated with a temporal onset and offset time, and DOA coordinate represented using azimuth and elevation angle. These evaluation datasets are part of the <a href="https://github.com/sharathadavanne/seld-dcase2019">DCASE 2019 Sound Event Localization and Detection Task</a>. The corresponding development datasets can be downloaded <a href="https://doi.org/10.5281/zenodo.2599196">here</a>.</p> <p>The IRs were collected in Finland by Tampere University between 12/2017 - 06/2018. The data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND.</p> <ul> <li>The <strong>foa_eval.zip</strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> evaluation dataset.</li> <li>The <strong>mic_eval.zip</strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> evaluation dataset.</li> </ul> <p>-- Version 2 updates --</p> <p>The<a href="http://dcase.community/challenge2019/task-sound-event-localization-and-detection-results"> DCASE 2019 sound event localization and detection task has now ended</a>. Hence we are releasing the reference labels for the evaluation dataset in this version.</p> <ul> <li>The <strong><em>metadata_eval.zip</em></strong> is the common metadata for both <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> and <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> evaluation datasets. </li> <li>The <strong>short2longnames.txt</strong> file consists of the corresponding names for each recording in the dataset in the <a href="http://dcase.community/challenge2019/task-sound-event-localization-and-detection#development-dataset">development-set format</a>, i.e., including the information of the impulse response location and the maximum number of overlapping sound events in the recording.</li> </ul> <p>Download the zip files corresponding to the dataset of interest and use your favorite compression tool to unzip these split zip files.<br> </p> <p> </p> <p> </p>
Dataset from: Transport and water age dynamics in soils: a comparative study of spatially-integrated and spatially-explicit models
<p>This dataset contains high-resolution vegetated lysimeter experimental dataset carried out in EPFL, Lausanne, Switzerland in March-August 2016. The lysimeter is 100 cm long with a diameter of 120 cm. During this experiment, a simultaneous spike injection of five different solutes (2,5-DFBA, 2-TFMBA, 3,4-DFBA, 2,6-DFBA, 3-TFMBA) took place on the 3rd of March 2016 at 14:00 in an hour. The solutes' mass recovery at the bottom of lysimeter was observed for these solutes.</p> <p>This dataset contains three files which are described in the following:</p> <ul> <li>"hydrologic_data.dat" contains the hourly fluxes (precipitation, irrigation, evapotranspiration measured from load cells, and the water draining at the bottom of lysimeter ) in mm/hr between the 19th of Feb-the 1st of Sep 2016.</li> <li>"tracer_data.dat" contains the tracer concentration observed at the bottom of lysimeter in mg/L. These samples are collected at variable frequencies with an approximate average of 1.5 samples per day.</li> <li>"additional_data.dat" informs you on dry mass and soil volume in the lysimeter, vegetation type, and volume of injection per solute.</li> </ul> <p> </p>
Processed CODEX Datasets from - Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens
<p>This entry provides access to processed CODEX data files of three studies analyzed in the article "Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens". Details of datasets can be found in the Methods section of the article.</p> <p>For each dataset:</p> <ul> <li>A comma-separated values (CSV) file containing metadata of regions is included</li> <li>A zip file containing multiple CSV files is included: <ul> <li>`{region_id}.cell_data.csv`, a table containing three columns: "CELL_ID", "X", and "Y". This table provides centroid locations for all cells segmented in this region.</li> <li>`{region_id}.expression.csv`, a table containing multiple columns: "CELL_ID", "DAPI", "CD45", etc. This table provides detailed protein biomarker expression quantified and normalized for all cells in this region.</li> <li>`{region_id}.cell_types.csv`, a table containing two columns: "CELL_ID" and "CELL_TYPE". This table provides cell type annotations for all cells in this region.</li> <li>`{region_id}.cell_features.csv`, a table containing two columns: "CELL_ID" and "SIZE". This table provides morphology descriptors (only containing cell size for these studies) for all cells in this region.</li> </ul> </li> </ul> <p>These data files are also available through the Enable Medicine Public Study page: <a href="https://app.enablemedicine.com/portal/atlas-library/studies/92394a9f-6b48-4897-87de-999614952d94?sid=1168">https://app.enablemedicine.com/portal/atlas-library/studies/92394a9f-6b48-4897-87de-999614952d94?sid=1168</a>. Raw multiplexed immunofluorescence images will be accessible through the visualizer app of Enable Medicine Portal.</p> <p>Codes for this study are stored in <a href="https://gitlab.com/enable-medicine-public/space-gm">https://gitlab.com/enable-medicine-public/space-gm</a>. Please direct all further questions and/or issues to the gitlab repository or lead contact (A.E.T.).</p>
Analysis and visualization of the Fasciola hepatica spatial transcriptomics dataset
<p>This repository contains various files related to the analysis of the paper: Spatial transcriptomics of a parasitic flatworm provides a molecular map of drug targets and drug-resistance genes.</p>
Dataset associated with A. Hallou, R. He, et al. A computational pipeline for spatial mechano-transcriptomics. bioRxiv 2023.08.03.551894
<p>Dataset associated with:</p> <p>Adrien Hallou, Ruiyang He, Benjamin David Simons and Bianca Dumitrascu. A computational pipeline for spatial mechano-transcriptomics. bioRxiv 2023.08.03.551894; doi: <a href="https://doi.org/10.1101/2023.08.03.551894">https://doi.org/10.1101/2023.08.03.551894</a></p> <p>Licence</p> <p>This dataset is licensed under the <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</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.