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1,868 results for “Spatial Data”
Shoreline data at 30-m spatial resolution for regions of the USA, in geoJSON format. Region 1: Delaware/Virginia border to Maine/Canada border
<p>Data file: NE_USA_Delaware_Maine_ref_shoreline.geojson</p> <p>Region: Delaware/Virginia border to Maine/Canada border</p> <p>Data fields:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> <p>The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk</p> <p>The data are described in the following publication</p> <p>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></p> <p>ABSTRACT</p> <p>A new 30-m spatial resolution global shoreline vector (GSV) was developed from annual composites of 2014 Landsat satellite imagery. The semi-automated classification of the imagery was accomplished by manual selection of training points representing water and non-water classes along the entire global coastline. Polygon topology was applied to the GSV, resulting in a new characterisation of the number and size of global islands. Three size classes of islands were mapped: continental mainlands (5), islands greater than 1 km<sup>2</sup> (21,818), and islands smaller than 1 km<sup>2</sup> (318,868). The GSV represents the shore zone land and water interface boundary, and is a spatially explicit ecological domain separator between terrestrial and marine environments. The development and characteristics of the GSV are presented herein. An approach is also proposed for delineating standardised, high spatial resolution global ecological coastal units (ECUs). For this coastal ecosystem mapping effort, the GSV will be used to separate the nearshore coastal waters from the onshore coastal lands. The work to produce the GSV and the ECUs is commissioned by the Group on Earth Observations (GEO), and is associated with several GEO initiatives including GEO Ecosystems, GEO Marine Biodiversity Observation Network (MBON) and GEO Blue Planet.</p> <p>https://www.tandfonline.com/doi/full/10.1080/1755876X.2018.1529714</p> <p> </p> <p> </p> <p> </p> <p> </p>
Shoreline data at 30-m spatial resolution for regions of the USA, in geoJSON format. Region 6: Oregon and Washington
<p>Data file: W_USA_Oregon_Washington_ref_shoreline.geojson</p> <p>Region: California/Oregon border to Washington/Canada border</p> <p>Data fields:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> <p>The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk</p> <p>The data are described in the following publication</p> <p>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></p> <p>ABSTRACT</p> <p>A new 30-m spatial resolution global shoreline vector (GSV) was developed from annual composites of 2014 Landsat satellite imagery. The semi-automated classification of the imagery was accomplished by manual selection of training points representing water and non-water classes along the entire global coastline. Polygon topology was applied to the GSV, resulting in a new characterisation of the number and size of global islands. Three size classes of islands were mapped: continental mainlands (5), islands greater than 1 km<sup>2</sup> (21,818), and islands smaller than 1 km<sup>2</sup> (318,868). The GSV represents the shore zone land and water interface boundary, and is a spatially explicit ecological domain separator between terrestrial and marine environments. The development and characteristics of the GSV are presented herein. An approach is also proposed for delineating standardised, high spatial resolution global ecological coastal units (ECUs). For this coastal ecosystem mapping effort, the GSV will be used to separate the nearshore coastal waters from the onshore coastal lands. The work to produce the GSV and the ECUs is commissioned by the Group on Earth Observations (GEO), and is associated with several GEO initiatives including GEO Ecosystems, GEO Marine Biodiversity Observation Network (MBON) and GEO Blue Planet.</p> <p>https://www.tandfonline.com/doi/full/10.1080/1755876X.2018.1529714</p>
R Code for Count data, spatial data, environmental data - Dee Estuary Waders 1970-2020
<p>R Code for analysis of spatio temporal data of waders on the Dee Estuary</p>
Data for: Effects of biotic interactions on plant fecundity depend on spatial and functional structure of communities and time since disturbance
<p><span>Biotic interactions in plant communities affect individual fitness and community dynamics. Interactions between plants vary in space, over time and with organisational complexity. Yet it is challenging to quantify temporal, spatial and functional determinants of different types of interactions between long-lived perennial plant species and their effect on lifetime fecundity. We studied how plant-plant, pollinator- and seed predator-mediated interactions affect year-to-year variation in three fecundity components (cone production, seed set and seed survival) during post-fire recovery. Age-stratified data on the three fecundity components were collected in 19 even-aged communities comprising 20 serotinous <em>Protea </em>shrub species in the South African Fynbos. We analyse data on these fecundity components with neighbourhood models to infer the sign and strength of interactions throughout post-disturbance recovery, the neighbour plant traits that shape them and the spatial scale at which interactions take place. For each fecundity component, these models describe how neighbourhood effects change over time and with spatial distance between plants. For each focal plant, we then predicted neighbourhood effects on individual fecundity components and cumulative reproductive output at different post-fire stand ages. Competitive effects on cone production and seed set increased with post-fire stand age as biomass and floral resources for pollinators build up. In contrast, neighbourhood effects on seed survival were weak throughout post-disturbance recovery. Plant-plant interactions were shaped by neighbour traits related to resource acquisition, whereas animal-mediated interactions depended on neighbour traits related to resource availability for pollinators and seed predators. The spatial scale of the interactions increased from plant-plant over predator-mediated to pollinator-mediated interactions. The joint effect of these interactions on cumulative reproductive output caused the proportion of focal plants experiencing competition to increase with time since fire. We show that temporal changes in biotic interactions throughout post-disturbance recovery of perennial plant communities depend on functional traits and can be integrated to neighbourhood effects on lifetime fecundity. Studying the temporal, spatial and functional determinants of neighbourhood effects on lifetime fecundity is important for predicting not only individual plant fitness, but also population and community dynamics in changing environments. </span></p>
Spatial transcriptomic data
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dataset for Spatial Distribution of Wildlife on University Campuses and Its Correlations with Environmental Factors: A Multi-Source Data Analysis
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Spatial data of Moroccan crop pollinators
<p><span>The data was collected in the framework of the IKI-FAP project. </span></p> <p><span>This data was explored in the research of Sentil et al., 2024.</span></p> <p><span>The objective of this research was to assess the impact of the Farming with Alternative Pollinators (FAP) approach on pollinator diversity and abundance.</span></p> <p><span>FAP field is 300 m² (30m*10m). 25 % of the FAP field area (the surrounding area) is occupied by Marketable Habitat Enhancement Plants (MHEP) and 75 % of the field area (the central zone) is occupied by the main crop. To test the impact of the FAP appraoch on pollinators, we compared FAP fields with control fields (the main crop occupies 100 % of the field area). </span></p> <p><span>The impact of the FAP approach on pollinator abundance and richness was assessed in four Moroccan agro-ecosystems (Settat, Kenitra, Errachidia and Sefrou), during two consecutive years (2018 and 2019) and using six main crops (faba bean, eggplant, pumpkin, zucchini, tomato and apple), resulting in 27 crop trials. Each crop trial represents one main crop (faba bean, eggplant, zucchini, pumpkin, apple or tomato) planted in one year (2018 or 2019) and in one region (Settat, Kenitra, Errachidia or Sefrou). </span></p> <p><span>For each crop trial 8 fields were selected when possible : 5 FAP fields (FAP1, FAP2, FAP3, FAP4 and FAP5) and 3 control fields (C1, C2 and C3).</span></p> <p><span>The sampling in the main crop consisted of walking alongside two 28 m transects (T1 and T2) and the sampling in the 25 zone ( the main crop in control field and MHEP in FAP field) consisted of walking alongside an 80 m transect (T3).</span></p> <p><span>Four insect samplings were conducted in each FAP and control field: one before the blooming of the main crop (S1), two during the blooming of the main crop (S2 and S3) and one after the blooming of the main crop (S4). </span></p> <p><span>For further details please see Sentil et al., 2024.</span></p> <p><strong><span>References:</span></strong></p> <p><span>Sentil, A, Lhomme, P, Reverte, S, El Abdouni, I, Hamroud, L, Ihsane, O, Bencharki, Y, Rollin, O, Rasmont, P, Chrif, M, Michez, D, Ssymank, A, Christmann, S. (2024). The pollinator conservation approach “ Farming with Alternative Pollinators ” : Success and drivers. </span>Agriculture , Ecosystems and Environment 369. https://doi.org/10.1016/j.agee.2024.109029</p> <p> </p> <p> </p>
spatial_data for workshop session 2
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Data to accompany "Evaluation of Spatial Audio Reproduction Methods (Part 2): Analysis of Listener Preference", J. AES, 2016
<p>This work was supported by the EPSRC Programme Grant S3A: Future Spatial Audio for an Immersive Listener Experience at Home (EP/L000539/1). Details about the data underlying this work, along with the terms for data access, are available from http://dx.doi.org/10.15126/surreydata.00809533</p> <p>If you use the data, please cite the following paper:</p> <p>J. Francombe, T. Brookes, and R. Mason, 2016: Evaluation of Spatial Audio Reproduction Methods (Part 2): Analysis of Listener Preference. Journal of the Audio Engineering Society</p>
Supporting data for SpatialOne: End-to-End Analysis of Spatial Transcriptomics at Scale
<p>Supplementary data supporting the <em>SpatialOne: End-to-End Analysis of Spatial </em><em>Transcriptomics at Scale</em> publication</p> <p> </p> <blockquote> <p>To showcase the capabilities of SpatialOne, two human lung cancer formalin-fixed, paraffin-embedded (FFPE) samples are analyzed. These samples are prepared following the CG000495 protocol (Figure 1b), sequenced with the 10x Visium CytAssist, and processed using the 10x SpaceRanger version 2. We also present analysis of two adult mouse samples sequenced using 10x Visium samples (one fresh frozen brain tissue section processed using SpaceRanger v2 and one FFPE kidney sample processed using the SpaceRanger v1), and 75 internal samples. </p> <p> For the human lung cancer samples, single-cell data from the the Lung Cancer Atlas (Salcher et al., 2022) is used as reference. This dataset is filtered to include only Chromium-generated data. For the mice samples, the GSE107585 single-cell dataset serves as reference. In the human lung cancer datasets, a pathologist annotated regions of interest corresponding to tumors, blood vessels, and alveolar regions.</p> </blockquote> <p> </p> <p>Changelog:</p> <ul> <li>Added a README file describing the zip content.</li> </ul>
Spatial patterns and effects of invasive plants on soil microbial activity and diversity along river corridors - raw data
<p>environmental data, plant community data, CLPP profiles, microbial activity data</p>
Data for "Spatial scales of rising-tone chorus in a dipole magnetic field: two-dimensional particle-in-cell simulation"
<p>The GCPIC simulation output data and the corresponding data analysis code.</p>
Figure 1 from: Underwood E, Taylor K, Tucker G (2018) The use of biodiversity data in spatial planning and impact assessment in Europe. Research Ideas and Outcomes 4: e28045. https://doi.org/10.3897/rio.4.e28045
Figure 1 Data use within the EIA process Own compilation based on information in (King et al. 2012).
Spatial Transcriptomics data (GeoMx) of midbrain dopamine cells in control and PD subjects
<p>The repository includes Spatial Transcriptomic datasets generated by Nanostring GeoMx (Hu WTA) analysis of midbrain TH+ cells from Controls (n=10), Incidental Lewy Body Disease (n=10), early Parkinsons Disease (ePD,n=5) and late Parkinsons Disease (lPD,n=5). A total 348 Regions of Interest were analysed. The raw and processed counts and metadata are provided as an R Seurat object (geomx_edwards_thmask.rds). The scripts used for low level data processing are described in https://github.com/zchatt/ASAP-SpatialTranscriptomics/blob/main/geomx/lowlevel/README.md</p> <p>Tissue samples from pathologically confirmed asymptomatic stage I-II Lewy body disease, stage IV Lewy body PD (early-PD), stage VI Lewy body PD (late-PD)(Braak, Del Tredici et al. 2003) and controls without the neurological or neuropathological disease were obtained from the Sydney Brain Bank. The study was approved by the University of Sydney Human Research Ethics Committee (2021/845). All cases with PD were levodopa-responsive and fulfilled the UK Brain Bank Clinical Criteria for a diagnosis of clinical PD (Hughes, Ben-Shlomo et al. 1992) with no other neurodegenerative conditions. </p> <p>Cells were not extracted. Tissue sections were cut from FFPE blocks of post-mortem human midbrains at 6µm on a rotary microtome (HistoCore MULTICUT, Leica Biosystems) and mounted on Series 2 adhesive microscope slides (Trajan Scientific Medical, AU) for processing for spatial trranscriptomics. To remove the paraffin, slides were incubated in the oven at 60°C for 1hr and then submerged in HistoChoice Clearing Agent (Sigma-Aldrich, H2779) for 2x7mins, followed by rehydration in decreasing ethanol concentrations (100% ethanol for 2x3mins, 95% ethanol for 3mins, 70% ethanol for 3mins) and distilled H2O for 3mins. </p> <p>Tissue sections were immunohistochemically stained for tyrosine hydroxylase and Regions of Interest (ROIs) processed following Nanostring GeoMx® Digital Spatial Profiler using the manufacturer’s instructions. Libraries were sequenced on Illumina Novaseq 6000 platform using NovaSeq SP 100 cycle kit (XP workflow, 27-8-8-27).</p> <p>This research was funded in whole or in part by Aligning Science Across Parkinson’s (ASAP-020529) through the Michael J. Fox Foundation for Parkinson’s Research (MJFF). For the purpose of open access, the author has applied a CC BY 4.0 public copyright license to all Author Accepted Manuscripts arising from this submission.</p>
Spatial Transcriptomics data (GeoMx) of locus coeruleus dopamine cells in control and PD subjects
<p>The repository includes Spatial Transcriptomic datasets generated by Nanostring GeoMx (Hu WTA) analysis of locus coeruleus TH+ cells from Controls (n=9), early Parkinsons Disease (ePD, n=8) and late Parkinsons Disease (lPD, n=2). A total 39 Regions of Interest were analysed. The raw and processed counts and metadata are provided as an R Seurat object (geomx_vila_thmask.rds). The scripts used for low level data processing are described in https://github.com/zchatt/ASAP-SpatialTranscriptomics/blob/main/geomx/lowlevel/README.md.</p> <p>Tissue samples from pathologically confirmed stage IV Lewy body PD (early-PD), stage VI Lewy body PD (late-PD)(Braak, Del Tredici et al. 2003) and controls without the neurological or neuropathological disease were obtained from the Sydney Brain Bank. The study was approved by the University of Sydney Human Research Ethics Committee (2021/845). All cases with PD were levodopa-responsive and fulfilled the UK Brain Bank Clinical Criteria for a diagnosis of clinical PD (Hughes, Ben-Shlomo et al. 1992) with no other neurodegenerative conditions. </p> <p>Cells were not extracted. Tissue sections were cut from FFPE blocks of post-mortem human midbrains at 6µm on a rotary microtome (HistoCore MULTICUT, Leica Biosystems) and mounted on Series 2 adhesive microscope slides (Trajan Scientific Medical, AU) for processing for spatial trranscriptomics. To remove the paraffin, slides were incubated in the oven at 60°C for 1hr and then submerged in HistoChoice Clearing Agent (Sigma-Aldrich, H2779) for 2x7mins, followed by rehydration in decreasing ethanol concentrations (100% ethanol for 2x3mins, 95% ethanol for 3mins, 70% ethanol for 3mins) and distilled H2O for 3mins. </p> <p>Tissue sections were immunohistochemically stained for tyrosine hydroxylase and Regions of Interest (ROIs) processed following Nanostring GeoMx® Digital Spatial Profiler using the manufacturer’s instructions. Libraries were sequenced on Illumina Novaseq 6000 platform using NovaSeq SP 100 cycle kit (XP workflow, 27-8-8-27).</p> <p>This research was funded in whole or in part by Aligning Science Across Parkinson’s (ASAP-020505) through the Michael J. Fox Foundation for Parkinson’s Research (MJFF). For the purpose of open access, the author has applied a CC BY 4.0 public copyright license to all Author Accepted Manuscripts arising from this submission.</p>
Spatial Transcriptomics data (GeoMx) of midbrain tissue in control and PD subjects
<p>he repository includes Spatial Transcriptomic datasets generated by Nanostring GeoMx (Hu WTA) analysis of midbrain unmasked (whole tissue) Regions of Interest from Controls (n=10), Incidental Lewy Body Disease (n=11), early Parkinsons Disease (ePD, n=5) and late Parkinsons Disease (lPD, n=6). A total 515 Regions of Interest were analysed. The raw and processed counts and metadata are provided as an R Seurat object (geomx_vila_unmask.rds). The scripts used for low level data processing are described in https://github.com/zchatt/ASAP-SpatialTranscriptomics/blob/main/geomx/lowlevel/README.md </p> <p>Tissue samples from pathologically confirmed asymptomatic stage I-II Lewy body disease, stage IV Lewy body PD (early-PD), stage VI Lewy body PD (late-PD)(Braak, Del Tredici et al. 2003) and controls without the neurological or neuropathological disease were obtained from the Sydney Brain Bank. The study was approved by the University of Sydney Human Research Ethics Committee (2021/845). All cases with PD were levodopa-responsive and fulfilled the UK Brain Bank Clinical Criteria for a diagnosis of clinical PD (Hughes, Ben-Shlomo et al. 1992) with no other neurodegenerative conditions. </p> <p>Cells were not extracted. Tissue sections were cut from FFPE blocks of post-mortem human midbrains at 6µm on a rotary microtome (HistoCore MULTICUT, Leica Biosystems) and mounted on Series 2 adhesive microscope slides (Trajan Scientific Medical, AU) for processing for spatial trranscriptomics. To remove the paraffin, slides were incubated in the oven at 60°C for 1hr and then submerged in HistoChoice Clearing Agent (Sigma-Aldrich, H2779) for 2x7mins, followed by rehydration in decreasing ethanol concentrations (100% ethanol for 2x3mins, 95% ethanol for 3mins, 70% ethanol for 3mins) and distilled H2O for 3mins. </p> <p>Tissue sections were immunohistochemically stained for tyrosine hydroxylase and Regions of Interest (ROIs) processed following Nanostring GeoMx® Digital Spatial Profiler using the manufacturer’s instructions. Libraries were sequenced on Illumina Novaseq 6000 platform using NovaSeq SP 100 cycle kit (XP workflow, 27-8-8-27).</p> <p>This research was funded in whole or in part by Aligning Science Across Parkinson’s (ASAP-020505) through the Michael J. Fox Foundation for Parkinson’s Research (MJFF). For the purpose of open access, the author has applied a CC BY 4.0 public copyright license to all Author Accepted Manuscripts arising from this submission.</p>
Raw Image Data Repository: Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics
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Data from: The importance of landscape and spatial structure for hymenopteran-based food webs in an agro-ecosystem
1. Understanding the environmental factors that structure biodiversity and food webs among communities is central to assess and mitigate the impact of landscape changes. 2. Wildflower strips are ecological compensation areas established in farmland to increase pollination services and biological control of crop pests, and to conserve insect diversity. They are arranged in networks in order to favour high species richness and abundance of the fauna. 3. We describe results from experimental wildflower strips in a fragmented agricultural landscape, comparing the importance of landscape, of spatial arrangement, and of vegetation on the diversity and abundance of trap-nesting bees, wasps and their enemies, and the structure of their food webs. 4. The proportion of forest cover close to the wildflower strips and the landscape heterogeneity stood out as the most influential landscape elements, resulting in a more complex trap nest community with higher abundance and richness of hosts, and with more links between species in the food webs and a higher diversity of interactions. We disentangled the underlying mechanisms for variation in these quantitative food-web metrics. 5. We conclude that in order to increase the diversity and abundance of pollinators and biological control agents and to favour a potentially stable community of cavity nesting hymenoptera in wildflower strips, more investment is needed in the conservation and establishment of forest habitats within agro-ecosystems, as a reservoir of beneficial insect populations.
Data from: Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross-validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross-validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also provides ample opportunity for overfitting with non-causal predictors. This problem can persist even if remedies such as autoregressive models, generalized least squares, or mixed models are used. Block cross-validation, where data are split strategically rather than randomly, can address these issues. However, the blocking strategy must be carefully considered. Blocking in space, time, random effects or phylogenetic distance, while accounting for dependencies in the data, may also unwittingly induce extrapolations by restricting the ranges or combinations of predictor variables available for model training, thus overestimating interpolation errors. On the other hand, deliberate blocking in predictor space may also improve error estimates when extrapolation is the modelling goal. Here, we review the ecological literature on non-random and blocked cross-validation approaches. We also provide a series of simulations and case studies, in which we show that, for all instances tested, block cross-validation is nearly universally more appropriate than random cross-validation if the goal is predicting to new data or predictor space, or for selecting causal predictors. We recommend that block cross-validation be used wherever dependence structures exist in a dataset, even if no correlation structure is visible in the fitted model residuals, or if the fitted models account for such correlations.
Data from: Monocular blur alters the tuning characteristics of stereopsis for spatial frequency and size
Our sense of depth perception is mediated by spatial filters at different scales in the visual brain; low spatial frequency channels provide the basis for coarse stereopsis, whereas high spatial frequency channels provide for fine stereopsis. It is well established that monocular blurring of vision results in decreased stereoacuity. However, previous studies have used tests that are broadband in their spatial frequency content. It is not yet entirely clear how the processing of stereopsis in different spatial frequency channels is altered in response to binocular input imbalance. Here, we applied a new stereoacuity test based on narrow-band Gabor stimuli. By manipulating the carrier spatial frequency, we were able to reveal the spatial frequency tuning of stereopsis, spanning from coarse to fine, under blurred conditions. Our findings show that increasing monocular blur elevates stereoacuity thresholds 'selectively' at high spatial frequencies, gradually shifting the optimum frequency to lower spatial frequencies. Surprisingly, stereopsis for low frequency targets was only mildly affected even with an acuity difference of eight lines on a standard letter chart. Furthermore, we examined the effect of monocular blur on the size tuning function of stereopsis. The clinical implications of these findings are discussed.
ScienceDex guides
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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.