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.
67
datasets available to search
ShareScore release 0.9.0
Dataset results
67 results for “spatial information”
Using seabird and whale distribution models to estimate spatial consumption of krill to inform fishery management
<p>Ecosystem dynamics at the north-west Antarctic Peninsula are driven by interactions between physical and biological processes. For example, baleen whale populations are recovering from commercial harvesting against the backdrop of rapid climate change, including reduced sea-ice extent and changing ecosystem composition. Concurrently, the commercial harvesting of Antarctic krill is increasing, with the potential to increase the likelihood for competition with and between krill predators and the fishery. However, understanding the ecology, abundance, and spatial distribution of krill predators is often limited, outdated, or at spatial scales that do not match those desired for effective fisheries management. We update current knowledge of predator dependence on krill by integrating telemetry-based data, at-sea observational surveys, estimates of predator abundance, and physiological data to estimate the spatial distribution of krill consumption during the austral summer by three species of Pygoscelis penguin, 11 species of flying seabirds, one species of pinniped and two species of baleen whale. Our models show that the majority of important areas for krill-predator foraging are close to penguin breeding colonies in nearshore areas where humpback whales also regularly feed, and along the shelf-break, though we caution that not all known krill predators are included in these analyses. We show that krill consumption is highly variable across the region, and often concentrated at fine spatial scales, emphasising the need for management of the local krill fishery at relevant temporal and spatial scales. We also note that krill consumption by recovering populations of krill predators provides further evidence in support of the krill surplus hypothesis, and highlight that despite less than comprehensive data, cetaceans are likely to consume a significant proportion of the krill consumed by natural predators but are not currently considered directly in the management of the krill fishery. If management of the krill fishery is to be precautionary and operate in a way that minimises the risks to krill predator populations, it will be necessary in future analyses, to include up-to-date and precise abundance and consumption estimates for pack-ice seals, finfish, squid, and other baleen whale species not currently considered.</p>
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - EE dataset
<p><strong>In vivo two-photon imaging dataset for Frechou et al. "Adult neurogenesis improves spatial information encoding in the mouse hippocampus"</strong></p> <p>This dataset includes data from mice that were housed in an enriched environment (EE). </p> <p>For each recording we included raw imaging data consisting of:</p> <ul> <li>Individual frames (.tif files) from 3 consecutive 3 min Ca2+ imaging movies (which were concatenated for analysis)</li> <li>Microscope settings metadata (Experiment.xml)</li> <li>Mouse location data (Episode001.h5 in SyncData folder) containing rotary encoder and RFID data</li> </ul> <p>Some analyzed data is also included:</p> <ul> <li>Suite2p analysis data (<strong>Suite2p</strong> folder)</li> <li><strong>fluorescence.npy </strong>contains raw fluorescence data (the F output of Suite2p data extraction). Rows are individual cells and columns are frames (i.e. timepoints) acquired at 15.253 Hz.</li> <li><strong>positions.npy </strong>contains the position of the mouse on the treadmill belt indexed from 0 to 100.</li> </ul> <p>Both NumPy(.npy) files are the output of the Preprocessing.py code, part of the analysis pipeline used for data analysis in the original publication, which can be found at <a href="https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis">https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis</a></p> <p>All imaged mice were female. Refer to the original publication for additional information. </p>
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - Irr+EE
<p><strong>In vivo two-photon imaging dataset for Frechou et al. "Adult neurogenesis improves spatial information encoding in the mouse hippocampus"</strong></p> <p>This dataset includes data from mice that were irradiated to ablate adult neurogenesis and housed in an enriched environment (Irr+EE). </p> <p>For each recording we included raw imaging data consisting of:</p> <ul> <li>Individual frames (.tif files) from 3 consecutive 3 min Ca2+ imaging movies (which were concatenated for analysis)</li> <li>Microscope settings metadata (Experiment.xml)</li> <li>Mouse location data (Episode001.h5 in SyncData folder) containing rotary encoder and RFID data</li> </ul> <p>Some analyzed data is also included:</p> <ul> <li>Suite2p analysis data (<strong>Suite2p</strong> folder)</li> <li><strong>fluorescence.npy </strong>contains raw fluorescence data (the F output of Suite2p data extraction). Rows are individual cells and columns are frames (i.e. timepoints) acquired at 15.253 Hz.</li> <li><strong>positions.npy </strong>contains the position of the mouse on the treadmill belt indexed from 0 to 100.</li> </ul> <p>Both NumPy(.npy) files are the output of the Preprocessing.py code, part of the analysis pipeline used for data analysis in the original publication, which can be found at <a href="https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis">https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis</a></p> <p>All imaged mice were female. Refer to the original publication for additional information. </p>
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - Irr+RC
<p><strong>In vivo two-photon imaging dataset for Frechou et al. "Adult neurogenesis improves spatial information encoding in the mouse hippocampus"</strong></p> <p>This dataset includes data from mice that werethat were irradiated to ablate adult neurogenesis and housed in a regular cage (Irr+RC). </p> <p>For each recording we included raw imaging data consisting of:</p> <ul> <li>Individual frames (.tif files) from 3 consecutive 3 min Ca2+ imaging movies (which were concatenated for analysis)</li> <li>Microscope settings metadata (Experiment.xml)</li> <li>Mouse location data (Episode001.h5 in SyncData folder) containing rotary encoder and RFID data</li> </ul> <p>Some analyzed data is also included:</p> <ul> <li>Suite2p analysis data (<strong>Suite2p</strong> folder)</li> <li><strong>fluorescence.npy </strong>contains raw fluorescence data (the F output of Suite2p data extraction). Rows are individual cells and columns are frames (i.e. timepoints) acquired at 15.253 Hz.</li> <li><strong>positions.npy </strong>contains the position of the mouse on the treadmill belt indexed from 0 to 100.</li> </ul> <p>Both NumPy(.npy) files are the output of the Preprocessing.py code, part of the analysis pipeline used for data analysis in the original publication, which can be found at <a href="https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis">https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis</a></p> <p>All imaged mice were female. Refer to the original publication for additional information. </p>
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - RetroAAV-EE
<p><strong>In vivo two-photon imaging dataset for Frechou et al. "Adult neurogenesis improves spatial information encoding in the mouse hippocampus"</strong></p> <p>This dataset includes data from retro-AAV injected mice that were housed in an enriched environment (EE). </p> <p>For each recording we included raw imaging data consisting of:</p> <ul> <li>Individual frames (.tif files) from 3 consecutive 3 min Ca2+ imaging movies (which were concatenated for analysis)</li> <li>Microscope settings metadata (Experiment.xml)</li> <li>Mouse location data (Episode001.h5 in SyncData folder) containing rotary encoder and RFID data</li> </ul> <p>Some analyzed data is also included:</p> <ul> <li>Suite2p analysis data (<strong>Suite2p</strong> folder)</li> <li><strong>fluorescence.npy </strong>contains raw fluorescence data (the F output of Suite2p data extraction). Rows are individual cells and columns are frames (i.e. timepoints) acquired at 15.253 Hz.</li> <li><strong>positions.npy </strong>contains the position of the mouse on the treadmill belt indexed from 0 to 100.</li> </ul> <p>Both NumPy(.npy) files are the output of the Preprocessing.py code, part of the analysis pipeline used for data analysis in the original publication, which can be found at <a href="https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis">https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis</a></p> <p>All imaged mice in this dataset were male. Refer to the original publication for additional information. </p>
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - RC
<p><strong>In vivo two-photon imaging dataset for Frechou et al. "Adult neurogenesis improves spatial information encoding in the mouse hippocampus"</strong></p> <p>This dataset includes data from mice that were housed in a regular cage (RC). </p> <p>For each recording we included raw imaging data consisting of:</p> <ul> <li>Individual frames (.tif files) from 3 consecutive 3 min Ca2+ imaging movies (which were concatenated for analysis)</li> <li>Microscope settings metadata (Experiment.xml)</li> <li>Mouse location data (Episode001.h5 in SyncData folder) containing rotary encoder and RFID data</li> </ul> <p>Some analyzed data is also included:</p> <ul> <li>Suite2p analysis data (<strong>Suite2p</strong> folder)</li> <li><strong>fluorescence.npy </strong>contains raw fluorescence data (the F output of Suite2p data extraction). Rows are individual cells and columns are frames (i.e. timepoints) acquired at 15.253 Hz.</li> <li><strong>positions.npy </strong>contains the position of the mouse on the treadmill belt indexed from 0 to 100.</li> </ul> <p>Both NumPy(.npy) files are the output of the Preprocessing.py code, part of the analysis pipeline used for data analysis in the original publication, which can be found at <a href="https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis">https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis</a></p> <p>Refer to the original publication for additional information. The sex of individual mice is as follows:</p> <table> <tbody> <tr> <td><strong>Group</strong></td> <td><strong>Mouse #</strong></td> <td><strong>Sex</strong></td> </tr> <tr> <td>RC</td> <td>M1</td> <td>M</td> </tr> <tr> <td> </td> <td>M2</td> <td>M</td> </tr> <tr> <td> </td> <td>M3</td> <td>M</td> </tr> <tr> <td> </td> <td>M4</td> <td>F</td> </tr> <tr> <td> </td> <td>M5</td> <td>F</td> </tr> </tbody> </table>
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - RetroAAV-RC
<p><strong>In vivo two-photon imaging dataset for Frechou et al. "Adult neurogenesis improves spatial information encoding in the mouse hippocampus"</strong></p> <p>This dataset includes data from retro-AAV injected mice that were housed in a regular cage (RC). </p> <p>For each recording we included raw imaging data consisting of:</p> <ul> <li>Individual frames (.tif files) from 3 consecutive 3 min Ca2+ imaging movies (which were concatenated for analysis)</li> <li>Microscope settings metadata (Experiment.xml)</li> <li>Mouse location data (Episode001.h5 in SyncData folder) containing rotary encoder and RFID data</li> </ul> <p>Some analyzed data is also included:</p> <ul> <li>Suite2p analysis data (<strong>Suite2p</strong> folder)</li> <li><strong>fluorescence.npy </strong>contains raw fluorescence data (the F output of Suite2p data extraction). Rows are individual cells and columns are frames (i.e. timepoints) acquired at 15.253 Hz.</li> <li><strong>positions.npy </strong>contains the position of the mouse on the treadmill belt indexed from 0 to 100.</li> </ul> <p>Both NumPy(.npy) files are the output of the Preprocessing.py code, part of the analysis pipeline used for data analysis in the original publication, which can be found at <a href="https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis">https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis</a></p> <p>All imaged mice were male. Refer to the original publication for additional information. </p>
Multi-region sequencing with spatial information enables accurate heterogeneity estimation and risk stratification in liver cancer
<p>We performed multi-region sampling and sequencing on 14 patients with HCC, collecting a total of 75 tumor samples with spatial information and molecular data. In addition, 21 matched adjacent liver samples were also collected. All samples were subjected to RNA sequencing (RNA-seq). RNA sequencing was performed on Illumina NovaSeq 6000 platform with 40M pair-end 150bp reads per sample. Whole-exome sequencing (WES) was performed on 36 samples from patients T10, T13 and T18 (n = 26) as well as adjacent non-tumor tissues (n = 10). Paired end, 150bp read-length sequencing was then performed on Illumina NovaSeq 6000 platform with a mean sequencing coverage of 100X. Raw sequencing data has been deposited at the National Omics Data Encyclopedia (NODE) under the accession code OEP002956 ( <a href="http://www.biosino.org/node/project/detail/OEP002956">http://www.biosino.org/node/project/detail/OEP002956</a>).</p>
Designing a Spatial Navigation Intervention Protocol Informed by Region-specific Brain Activation for Mild Cognitive Impairment
ClinicalTrials.gov study NCT07225400. IPD Sharing: YES. Countries: 1. Publications: 46.
Data from: A spatial genetics approach to inform vector control of tsetse flies (Glossina fuscipes fuscipes) in Northern Uganda
Open the record for dataset details and reuse information.
Data from: Differences in spatial synchrony and interspecific concordance inform guild-level population trends for aerial insectivorous birds
Open the record for dataset details and reuse information.
Using seabird and whale distribution models to estimate spatial consumption of krill to inform fishery management
Open the record for dataset details and reuse information.
Data from: Molecular genetics to inform spatial management in benthic invertebrate fisheries: a case study using the Australian greenlip abalone
Open the record for dataset details and reuse information.
Lack of spatial coordinate information for an important insect order (Lepidoptera) in a Central European country
Open the record for dataset details and reuse information.
Behavioural and neurophysiological data from experiments on the impact of spatial frequency information to action comprehension
Open the record for dataset details and reuse information.
Data from: Spatial dynamics of habitat use informs reintroduction efforts in the presence of an invasive predator
Open the record for dataset details and reuse information.
Data from: The spatial dynamics of predators and the benefits and costs of sharing information
Open the record for dataset details and reuse information.
Data from: Phylogenetically informed spatial planning is required to conserve the mammalian tree of life
Open the record for dataset details and reuse information.
Data from: Revised time scales of RNA virus evolution based on spatial information
The time scales of pathogen evolution are of major concern in the context of public and veterinary health, epidemiology and evolutionary biology. Dating the emergence of a pathogen often relies on estimates of evolutionary rates derived from nucleotide sequence data. For many viruses, this has yielded estimates of evolutionary origins only a few hundred years in the past. Here we demonstrate through the incorporation of geographic information from virus sampling that evolutionary age estimates of two European hantaviruses are severely underestimated because of pervasive mutational saturation of nucleotide sequences. We detected very strong relationships between spatial distance and genetic divergence for both Puumala and Tula hantavirus – irrespective whether nucleotide or derived amino acid sequences were analyzed. Extrapolations from these relationships dated the emergence of these viruses most conservatively to at least 3,700 and 2,500 years ago, respectively. Our minimum estimates for the age of these hantaviruses are ten to a hundred times older than results from current non-spatial methods and in much better accordance with the biogeography of these viruses and their respective hosts. Spatial information can thus provide valuable insights on the deeper time scales of pathogen evolution and improve our understanding of disease emergence.
Improving accuracy of breeding values by incorporating genomic information in spatial-competition mixed models
<p>Supplementary information of a <em>Eucalyptus grandis</em> population, genomic and pedigree data including identity information of trees, family, and provenance of the paper entitled: <strong>Improving accuracy of breeding values by incorporating genomic information in spatial-competition mixed models</strong>.</p> <p> </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.