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7,370 results for “supplement”
Multiple-benefit Conservation in Practice: Supplemental Spatial Data for Quantifying Multidimensional Impacts of Landscape Change in California's Sacramento–San Joaquin Delta
<p><strong>SUMMARY</strong><br> Spatial data representing climate, proximity to streams, and probability of flooding in the Sacramento-San Joaquin Delta.</p> <p><strong>DESCRIPTION</strong><br> These data were compiled as predictors of the distribution of riparian landbird species and groups of waterbird species, to facilitate projecting the probability of species or group presence across a given landscape. They were used to identify Priority Bird Conservation Areas and in analyses of the impacts of scenarios representing habitat restoration and perennial crop expansion on suitable habitat. These data are required for using the R package "DeltaMultipleBenefits", which provides the code and work flow for repeating the initial analyses or analyzing new scenarios.</p> <p>For additional details about the development and applications of these data, please see: </p> <ul> <li>Dybala KE, et al. (<em>In review</em>) Multiple-benefit Conservation in Practice: A Framework for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta</li> <li>Dybala KE, Sesser K, Reiter M, Shuford WD, Golet GH, Hickey C, Gardali T (<em>In review</em>) Priority Bird Conservation Areas in California’s Sacramento–San Joaquin Delta. </li> <li>Dybala KE (2023) <em>DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta</em>. R package version 1.0.0. doi: 10.5281/zenodo.7718620. Available from: https://pointblue.github.io/DeltaMultipleBenefits.</li> </ul> <p><strong>FUNDING STATEMENT</strong><br> These data were developed as part of the project "Trade-offs and Co-benefits of Landscape Change on Bird Communities and Ecosystem Services in the Sacramento–San Joaquin River Delta", funded by Proposition 1 Delta Water Quality and Ecosystem Restoration Program, Grant Agreement Number – Q1996022, administered by the California Department of Fish and Wildlife.</p> <p><strong>POINT OF CONTACT</strong><br> Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org</p> <p><strong>SUGGESTED CITATION</strong><br> Dybala KE. 2023. Multiple-benefit Conservation in Practice: Supplemental Spatial Data for Quantifying Multidimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta. doi:10.5281/zenodo.7672193.</p> <p><strong>DATA DISTRIBUTION</strong><br> Zenodo (https://doi.org/10.5281/zenodo.7672193)</p> <p><strong>PROGRESS</strong><br> Complete</p> <p><strong>UPDATE FREQUENCY</strong><br> None planned</p> <p><strong>DATE</strong><br> These data were compiled in 2022, based on data from WorldClim (representing 1970-2000), National Hydrography Dataset (published 2020), and Point Blue's Water Tracker (representing 2013-2019).</p> <p><strong>FIELD DEFINITIONS</strong></p> <ul> <li><strong>bio_1: </strong>annual mean temperature (C), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li><strong>bio_12:</strong> total annual precipitation (mm), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li><strong>streamdist: </strong>square root of the distance to the nearest stream (m) (National Hydrography Dataset; USGS 2020)</li> <li><strong>pwater_fall:</strong> mean probability of open surface water during the fall, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> <li><strong>pwater_win:</strong> mean probability of open surface water during the winter, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> </ul> <p><strong>Literature Cited</strong></p> <ul> <li>Fick SE, Hijmans RJ. 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. 37:4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a> </li> <li>Reiter ME, Elliott NK, Barbaree B, Moody D. 2018. An automated open surface water tracking system for California’s Central Valley. Report to the U.S. Fish and Wildlife Service. Petaluma, California: Point Blue Conservation Science. Available from: <a href="https://data.pointblue.org/apps/autowater/ ">https://data.pointblue.org/apps/autowater/ </a></li> <li>[USGS] United States Geological Survey. 2020. National Hydrography Dataset Best Resolution (NHD) for Hydrologic Units (HU) 4 - 1802, 1803, 1804, 1805. Reston (VA): U.S. Geological Survey. Available from: <a href="https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products ">https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products </a></li> </ul> <p><strong>ABBREVIATION DEFINITIONS</strong><br> N/A</p> <p><strong>COORDINATE REFERENCE SYSTEM</strong><br> WGS 84 / UTM zone 10N (EPSG:32610)</p> <p><strong>ACCESS & USE CONSTRAINTS</strong><br> CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>KEYWORDS</strong></p> <ul> <li><strong>Themes:</strong> climate, temperature, precipitation, hydrology, streams, water, flood, remote sensing </li> <li><strong>Place: </strong>Sacramento-San Joaquin River Delta, Central Valley, California</li> </ul>
Supplemental data files: Beyond the reference: gene expression variation and transcriptional response to RNAi in C. elegans
<p>This dataset holds all non-GEO-hosted supplemental data files for manuscript "Beyond the reference: gene expression variation and transcriptional response to RNAi in <em>C. elegans</em>". Please see the linked preprint/publication for full details.</p> <p>The PDF _guide_to_datafiles.pdf gives details on the format and content of each of the included files.</p>
Dataset and supplemental codes for : "Referenceless characterisation of complex media using physics-informed neural networks"
<p>Dataset and associated supplemental codes for : "Referenceless characterisation of complex media using physics-informed neural networks".</p>
Data supplement to 'Vertical land motion reconstruction unveils non-linear effects on relative sea level changes from 1900-2150'
<p>This is a data supplement to <strong>'Vertical land motion reconstruction unveils non-linear effects on relative sea level changes from 1900-2150</strong>'. It presents a global-scale Vertical Land Motion (VLM) reconstruction that resolves height changes in the period 1995-2020. It is based on the joint probabilistic analysis of an extensive network of more than 11,000 GNSS stations, tide gauges, and satellite altimetry. The approach used to derive this reconstruction is described in the paper. The dataset variables are explained in the .pdf file.</p>
Data publication supplementing "Novel nanoindentation strain rate sweep method for continuously investigating the strain rate sensitivity of materials at the nanoscale"
<p>This data publication contains the results of nanoindentation tests on Fused silica, nanocrystalline nickel, a nanocrystalline FeCr alloy, a bulk metallic glass, the superplastic alloy Zn-22%Al and single crystalline aluminum as well as the method files developed for the G200 nanoindenter. It supplements the publication "Novel nanoindentation strain rate sweep method for continuously investigating the strain rate sensitivity of materials at the nanoscale". The materials are described in more detail in the respective publication. The data publication takes over the sample naming convention from the related publication. </p><p>Nanoindentation measurement were performed by H. Holz at the Max-Planck Institut für Eisenforschung GmbH, Max-Planck-Straße 1, 40237 Düsseldorf, Germany using a G200 nanoindenter (KLA, Milpitas, CA, USA), equipped with a modified Berkovich diamond indenter tip of the type 171-561-500 with a serial number of C-0040446 from Synton MDP (Nidau, Switzerland). Constant strain rate tests, strain rate jump tests, strain rate sweep tests and strain rate sweep reversal tests were performed on each material in Continuous Stiffness Measurement (CSM) mode. The maximum indentation depth was 2200 nm, the CSM amplitude 2 nm and the CSM frequency 45 Hz. Strain rates were varied within the range 0.001 – 0.1 s-1. Further information on the test protocol can be found in the corresponding publication.</p><p>The subfolder "Nanoindentation data" contains the raw data for all valid indents as output by the NanoSuite © software v 7.1.7 and converted to the semicolon-separated format. The naming convention for the folder in which the CSV files are located in gives first the used material, then the method used with additional information to the parameters inputted for the method such as strain rate and indentation depth all separated by an underscore. An example can be "FS_CSR_01s-1" for a constant strain rate tests performed on fused silica with a strain rate target of 0.1 s-1 or "Nc-Ni_SweepReversal_005s-1_0005s-1" for a sweep reversal test performed on the nanocrystalline nickel sample with a targeted initial and ending strain rate of 0.05 s-1 and a strain rate target at which the strain rate direction gets reversed of 0.005 s-1. The CSV files are named either "Results" giving the average results of each test, "Required Inputs" giving information about the parameters used for the experiments, "Inputs Editable Post Test" giving information about the analysis parameters to obtain the results from, and "Test XXX" which include the Raw data of the corresponding test number. In each file the first row gives the data description e.g., "Time", the second row the physical unit e.g., "s" for seconds and from the third row the measured values.</p><p>The Nano Suite method files to perform the experiments on KLA G200 instruments is provided in the folder "G200 methods". The method for the strain rate sweep experiments is called "Strain Rate Sweep.msm" and the method for the strain rate sweep reversal experiments "Strain Rate Sweep Reversal.msm". This method is provided as is and shall be used at your own risk. The authors explicitly decline responsibility for any physical or immaterial damage resulting from the use of this method. Should minor issues occur, some feedback to the authors would be greatly appreciated.</p>
Delta Smelt (Hypomesus transpacificus) biomarker and genetic data from supplemental release into the San Francisco Estuary, 2022
Delta Smelt (Hypomesus transpacificus) is an endangered fish that is endemic to the San Francisco Estuary. As a conservation strategy, hatchery-reared Delta Smelt have been released into the San Francisco Estuary to supplement the wild population. State and federal agencies surveyed the abundance of Delta Smelt, collected fish specimens, and recorded associated environmental data from sampling sites. Delta Smelt specimens were preserved and transported to the University of California, Davis where a variety of biomarkers were assessed on individual fish. This project, including the supplementation of hatchery-reared Delta Smelt and data collection, is ongoing.
Supplemental materials of the Castaño-Sánchez et. al. (2023) article (Agricultural Systems) containing the IFSM model input parameters not included in the main text, and the Criollo ranches survey form
CONTEXT: The southwestern United States is experiencing an increasingly warmer and drier climate that is affecting cattle production systems of the region. Adaptation strategies are needed that will not compromise environmental quality or profitability. Options include the use of desert-adapted beef cattle biotypes, such as Rarámuri Criollo cattle, and crossbreds of Criollo with more traditional British breeds. Currently, most calves raised in the Southwest are grain finished, often with irrigated crops produced in the hydrologically-threatened Ogallala Aquifer region. A viable alternative may be grass finishing with the rainfed forage of the arid and semi-arid rangeland of the Southwest or in the temperate grasslands of the Northern Plains. OBJECTIVE: Compare the environmental impacts and production costs of grain-finishing in Texas and grass-finishing in the Northern plains and the Southwest with traditional Angus cattle vs. Criollo and Criollo x Angus cattle. METHODS: Nine supply chain strategies were simulated using the Integrated Farm System Model to compare farm-gate life cycle intensities of greenhouse gas emissions (carbon footprint), fossil energy footprint, nitrogen footprint, blue water footprint and production costs using representative (appropriate soils, climate, and management) ranch and feedlot operations. RESULTS AND CONCLUSIONS: For both finishing options (grass, grain), Criollo x Angus cattle had the best environmental (3%-27% lower), and production cost (4-23% lower) outcomes followed by pure Criollo and then Angus cattle. Crossbred production combined the lower feed supplementation requirements of Criollo cows with heavier final carcasses of offspring from Angus genetics. Crossbred cattle with grass finishing in the Southwest or Northern Plains outperformed on most environmental variables as well as production costs, mostly due to reduced external input requirements (primarily feed). A downside for grass-finished crossbreds was greater carbon fo
Supplemental soil moisture and temperature data from the saddle catchment sensor network, 2019 - 2021.
Hand-held soil moisture measurements were taken at 8 of 16 soil moisture sensors within the sensor network at Niwot Ridge to supplement the continuous measurement system at these locations. The hand-held measurements occur much less frequently than the 10 min sensor data, but they are important in determining the spatial variability of soil moisture in the alpine.
Data supplement for "Bifurcations of front motion in passive and active Allen-Cahn-type equations"
<p>This dataset contains the data and source files for figures 5 and 7-10 in the following publication: </p> <p>F. Stegemerten, S.V. Gurevich, U. Thiele</p> <p><em>'Bifurcations of front motion in passive and active Allen–Cahn-type equations' </em></p> <p>published in 2020 in CHAOS.</p> <p>Please follow the instructions given in 'Readme.txt'.</p>
Supplemental artifacts of the paper: Efficient Binary-Level Coverage Analysis
<p>NOTE: the official repository of bcov is: <a href="https://github.com/abenkhadra/bcov">https://github.com/abenkhadra/bcov</a></p> <p>This repository contains the artifacts accompanying our paper: "Efficient Binary-Level Coverage Analysis", which appeared in ESEC/FSE'20. The artifacts consists of two packages, namely, bcov-benchmarks.tar.gz and bcov-artifacts.tar.gz. The former package contains the complete list of binaries described in our experiments. The artifacts of the latter package are organized as follows:</p> <p> - <strong>sample-binaries</strong>. Folder that contains sample binaries patched with bcov.</p> <p> - <strong>dataset.tar.gz</strong>. Package containing experimental data in csv format.</p> <p> - <strong>figures</strong>. Folder that contains the python script used to generate the figures<br> of our paper. It assumes that the dataset was first extracted to the folder `dataset`.</p> <p> - <strong>install.sh</strong>. This script builds and installs bcov together with its dependencies.</p> <p> - <strong>experiment-01.sh</strong>. This script patches our sample binaries and shows how coverage<br> data can be collected. It assumes that bcov was installed using the previous script.</p> <p> - <strong>bcov.tar.gz</strong>. Source code of the first public version of `bcov`. The tool is distributed under an MIT license.<br> </p>
Supplement 1: Full list of ICD10 codes and number of gene-disease links (tab-separated-value file); Supplement 2: Mapping (tab-separated-value file)
<p>Supplements to BioMedBridges deliverable 10.2 A prototype linking ICD10/SNOMED CT concepts to Ensembl gene identifiers:</p> <p><strong>Supplement 1</strong>: Full list of ICD10 codes and number of gene-disease links: table_icd10_gene_count_descr.tsv</p> <p><strong>Supplement 2</strong>: Mapping of disease terms: <em>ICD10_to_doid.tsv</em></p>
HaMMLET - Supplemental Material
<p>HaMMLET is a novel type of Bayesian Hidden Markov Model, using dynamically adaptive wavelet compression for improved speedup and convergence behaviour of Forward-Backward Gibbs sampling.</p> <p>This data set contains supplemental material for HaMMLET's initial publication.</p>
Supplement for "Using Phylogenetic Networks to Model Chinese Dialect History"
<p>This is the supplementary material accompanying the paper "Using Phylogenetic Networks to Model Chinese Dialect History", which appeared in 2014 in "Language Dynamics and Change" (volume 4, issue 2).</p>
Dataset supplementing the article Einhäuser, W., Methfessel, P., & Bendixen, A. (2017). Newly acquired audio-visual associations bias perception in binocular rivalry. Vision Research, 133, 121-129.
<p>This dataset supplements the publication<br> Einhäuser, W., Methfessel, P., & Bendixen, A. (2017). Newly acquired audio-visual associations bias perception in binocular rivalry. Vision Research, 133, 121-129. doi: 10.1016/j.visres.2017.02.001</p> <p>Use is free for scientific purposes, provided the aforementioned reference is appropriately cited.<br> Description of files<br> - conditionsByObserver.csv<br> contains for each of the 16 observers the color and grating direction that had been coupled to either the low-pitch or the high-pitch tone<br> column 1: observer number<br> column 2: color associated with low-pitch tone<br> column 3: color associated with high-pitch tone<br> column 4: drift direction associated with low-pitch tone<br> column 5: drift direction associated with high-pitch tone</p> <p>- conditionsByObserver.mat contains the same information as matlab variables (as four vectors/cell arrays with one entry per observer)</p> <p>- toneByBlockAndTrial.csv<br> contains the conditions for all 18 rivalry trials (6 rivalry blocks with 3 trials each) for each observer<br> column 1: observer number<br> column 2: block number<br> column 3: trial number<br> column 4: tone (low [pitch], high [pitch], none) played in this trial<br> Note that due to a technical error for observer #16, block 6 was presented first, followed by 1,2,3,4,5; for all other observers blocks were used in the order given (1,2,3,4,5,6).</p> <p>- toneByBlockAndTrial.mat contains the same information as a 16x6x3 matrix named toneByBlockAndTrial ; tones are coded numerically (1-low pitch,2-high pitch,3-none)</p> <p>- eyeTraces.mat contains three cell arrays of dimensions 16x6x3 (observer x rivalry block x rivalry trial) called xEye, oknGain, and timeSinceTrialStart;</p> <p>o each entry of xEye contains the horizontal eye position for<br> the respective trial in eye-tracker coordinates (which correspond to screen pixels, except that (1/1) is the upper right rather than the upper left and values increase from right to left due to the setup configuration)</p> <p>o oknGain contains the gain computed from these eye positions.</p> <p>o timeSinceTrialStart contains the time in seconds since onset of the trial</p> <p><br> For all variables, the sampling rate is 500 Hz, in eye-tracker coordinates the speed of the grating is 240 units/ms. Blinks were removed from both eye-data variables, fast-phases were removed from the gain data. Removed data were set to NaN in eye-data variables.</p> <p>- Matlab functions figure1d.m, figure 2.m, figure3.m and figure4.m compute raw versions of the aforementioned paper's figures from the datafiles to exemplify their usage.</p> <p>[Note: In the originally published version of the article, the first two means and their standard errors of section 3.3 were stated incorrectly. All figures and statistical analyses are based on the correct data].</p>
CLDF dataset with data and supplements for Barlow "Loss of colexification of 'hand' and 'five' in Austronesian languages"
CLDF dataset with data and supplements for Barlow "Loss of colexification of 'hand' and 'five' in Austronesian languages"
Updated Files from the revision 1 (986 cMAGs and prokka annotations, all supplemental files)
<p>Included are the following files<br><br>1) Assembly files (47) in the form of .fasta for each of the 47 different samples (8 participants x 5 or 6 time points). This is the results of the assembly from hybrid long read data (all Pacbio Revio + ONT Promethion > Q20). Assemblies performed using metaMDBG. This file includes all contigs from the pipeline described in the manuscript and will include both high-quality and complete MAGs. <br>"hybrid_assemblies.tar.gz"<br><br>2) Assembly files (40) in the form of .fasta for the short read metaspades and tell-seq assembly methods<br>"SR_tellseq_assemblies.tar.gz"<br>"SR_metaspades_assemblies.tar.gz"</p> <p>3) Assembly files from sub-sampling experiment: 3 samples (A6, D5, and H6) were deep sequenced with both PB Revio and ONT Promethion lsk114 R10.4.1 SUP400. Data was randomly subsampled to various depths (1, 5, 10, 20, 30, 40 Gbp, and all) and then assembled with both metaMDBG and metaFlye. <br>-A6: total of 26 files (PB was less than 40 Gbp thus no 40 Gbp subsampling depth)<br>-D5: total of 28 files<br>-H6: total of 28 files<br>-total: 82 files<br>"LR_ONTPB_sub_assemblies.tar.gz"</p> <p>4) Tar file of all circular contigs from assemblies. This will include 47 separate fasta files (1 for each participant and time point). This data was used for the viral and plasmid analyses. <br>"Hsap_circ_contigs_47_assemblies.tar.gz"<br><br>5) File containing all of the 985 cMAGs with annotations generated using prokka</p> <p>"985cMAGs_prokkaannotation.tar.gz</p> <p>6) Deep taxonomic profiling feature table: Pacbio samples (45) with features classified using the GTDB and 985 cMAGs custom database<br>"PB_985cMAG-sourmash_45s_2162f" #feature table<br>"mapping-file_PB45s_2162f_deID.txt" #mapping file, deidentified<br><br>7) tar file containing all genomes in the updated quality 'c986 cMAGs' <br>(>90% completeness, <5% contamination, contig=1<br><br>8) tar file containing all prokka annotation files associated with the 986 cMAGs<br><br>9) All supplemental tables or data files used in the revision</p>
Video, image, and supplemental files linked in Burge et al. (2023) "Depredation by Bottlenose Dolphins Tursiops truncatus from Antillean Z-traps at Discovery Bay, Jamaica"
<p>Video, image, and supplementary text files linked in Burge et al. (2023), Caribbean Naturalist, 95: 1–25.</p><p><strong>Depredation by Bottlenose Dolphins </strong><i><strong>Tursiops truncatus</strong></i><strong> from Antillean Z-traps at Discovery Bay, Jamaica</strong></p><p>All video and image files referred to in the main text, figures, and tables are available from this repository. See Table 1 and Table S1 for additional details.</p><p> </p>
Supplemental Files for Schlegel et al., Nature (2024)
<p>This repository contains supplemental files for the paper "<strong>Whole-brain annotation and multi-connectome cell typing of Drosophila</strong>" - Schlegel <em>et al.</em>, Nature (2024).</p> <ul> <li><em>nblast_flywire_all_right_aba_comp.feather</em> contains all-by-all NBLAST score for all FlyWire neurons where neurons from the left hemisphere have been mirrored to the right</li> <li><em>nblast_flywire_hemibrain_min_comp.feather</em> <em> </em>contains NBLAST scores for FlyWire versus "hemibrain" neurons</li> <li><em>nblast_flywirre_mirrored_hemibrain_comp.feather</em> contains NBLAST scores for FlyWire versus "hemibrain" neurons where all FlyWire neurons have been mirrored</li> <li><em>sk_lod_783_healed_ds2.parquet</em> contains skeletons in SWC format for all FlyWire neurons (generated from lod 1 meshes and 2X downsampled, coordinates are in nanometres); the raw data can be read with e.g. the Python <a href="https://pypi.org/project/pyarrow/"><em>pyarrow</em></a> package (see <a href="https://arrow.apache.org/docs/python/parquet.html">documentation</a> for examples). Alternatively, you can use the <a href="https://github.com/navis-org/navis"><em>navis</em></a> Python package to read the contents into neuron objects (see <a href="https://navis-org.github.io/navis/reference/navis/#navis.read_parquet">navis.read_parquet).</a></li> </ul> <p>Additional notes:</p> <ul> <li>all root IDs refer to the 783 release of FlyWire</li> <li>for NBLAST files: <ul> <li>columns/indices for FlyWire neurons are given as "{root_id},{supervoxel_id}", where the supervoxel ID represents an anchor that can be used to map this neuron to different materialization versions</li> <li>scores were compressed by rounding to the 4th decimal and clipping values below 0</li> </ul> </li> </ul> <p>For neuron annotations and further details please see <a href="https://github.com/flyconnectome/flywire_annotations">https://github.com/flyconnectome/flywire_annotations</a>. </p> <p>The proofreading and FlyWire resource are described in our companion paper (Dorkenwald <em>et al.</em>, Nature, 2024).</p>
Additional Artifacts - Supplements to: A Resilient Workflow to Control a Biomedical HPC Simulation in an Urgent Computing Setting
<p>In this dataset, we have collected supplementary artifacts to support an understanding of the workflow presented in the submission cited (see related identifiers).</p> <p>These artifacts are (cf. README.md in the main folder of the tar.gz archive):</p> <p>A1: modified HemoFlow code (cf. https://github.com/gzavo/hemoflow) for our workflow experiments (subfolder "hemoflowcfd");<br>A2: workflow descriptions in python for Apache Airflow (subfolder "workflow");<br>A3: inputs (.xml/.npz) and output (.txt) for the example (subfolder "case").</p> <p> </p>
Data supplement: Spatial autocorrelation in machine learning for modelling soil organic carbon
<p>Spatial autocorrelation in machine learning for modelling soil organic carbon: Data supplement</p> <p><br>Alexander Kmoch, Clay Taylor Harrison, Jeonghwan Choi, Evelyn Uuemaa</p> <p>Spatial autocorrelation, the relationship between nearby samples of a spatial<br>random variable, is often overlooked in machine learning models, leading to<br>biased results. This study investigates various methods to account for spa-<br>tial autocorrelation when predicting soil organic carbon (SOC) using random<br>forest models. Five models incorporating spatial structure were compared<br>against baseline models that did not have any added spatial components.<br>Cross-validation showed slight improvements in accuracy for models consid-<br>ering spatial autocorrelation, while Shapley Additive Explanations confirmed<br>the importance of spatial variables. However, no decrease in spatial autocor-<br>relation of residuals was observed. Raster-based models exhibited enhanced<br>prediction detail, but high-resolution validation data availability limited thor-<br>ough validation. The findings emphasize the value of incorporating spatial<br>autocorrelation for improved SOC prediction in machine learning models.<br>Considerations such as the distribution of predictions and computational<br>complexity should help guide the selection of suitable approaches for specific<br>spatial modelling tasks.</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.