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36,943 results for “HUMANE”
Data for: World's human migration patterns in 2000-2019 unveiled by high-resolution data
<p> </p> <p>This dataset provides a<strong> global gridded (5 arc-min resolution) detailed annual net-migration dataset for 2000-2019</strong>. We also provide global annual birth and death rate datasets – that were used to estimate the net-migration – for same years. The dataset is presented in details, with some further analyses, in the following publication. <strong><em>Please cite this paper when using data. </em></strong></p> <p>Niva et al. 2023. World's human migration patterns in 2000-2019 unveiled by high-resolution data. Nature Human Behaviour 7: 2023–2037. Doi: <a href="https://doi.org/10.1038/s41562-023-01689-4" target="_blank" rel="noopener">https://doi.org/10.1038/s41562-023-01689-4</a> </p> <p>You can explore the data in our online net-migration explorer: <a href="https://wdrg.aalto.fi/global-net-migration-explorer/" target="_blank" rel="noopener">https://wdrg.aalto.fi/global-net-migration-explorer/</a></p> <p> </p> <p><strong>Short introduction to the data</strong></p> <p>For the dataset, we collected, gap-filled, and harmonised: </p> <ol> <li>a comprehensive national level birth and death rate datasets for altogether 216 countries or sovereign states; and </li> <li>sub-national data for births (data covering 163 countries, divided altogether into 2555 admin units) and deaths (123 countries, 2067 admin units).</li> </ol> <p>These birth and death rates were downscaled with selected socio-economic indicators to 5 arc-min grid for each year 2000-2019. These allowed us to calculate the 'natural' population change and when this was compared with the reported changes in population, we were able to estimate the annual net-migration. See more about the methods and calculations at Niva et al (2023). </p> <p><strong><em>We recommend using the data either over multiple years (we provide 3, 5 and 20 year net-migration sums at gridded level) or then aggregated over larger area (we provide adm0, adm1 and adm2 level geospatial polygon files). This is due to some noise in the gridded annual data. </em></strong></p> <p>Due to copy-right issues we are not able to release all the original data collected, but those can be requested from the authors. </p> <p> </p> <p><strong>List of datasets</strong></p> <p><em>Birth and death rates: </em></p> <p>raster_birth_rate_2000_2019.tif: Gridded birth rate for 2000-2019 (5 arc-min; multiband tif) </p> <p>raster_death_rate_2000_2019.tif: Gridded death rate for 2000-2019 (5 arc-min; multiband tif) </p> <p>tabulated_adm1adm0_birth_rate.csv: Tabulated sub-national birth rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national) </p> <p>tabulated_ adm1adm0_death_rate.csv: Tabulated sub-national death rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national) </p> <p> </p> <p><em>Net-migration: </em></p> <p>raster_netMgr_2000_2019_annual.tif: Gridded annual net-migration 2000-2019 (5 arc-min; multiband tif) </p> <p>raster_netMgr_2000_2019_3yrSum.tif: Gridded 3-yr sum net-migration 2000-2019 (5 arc-min; multiband tif) </p> <p>raster_netMgr_2000_2019_5yrSum.tif: Gridded 5-yr sum net-migration 2000-2019 (5 arc-min; multiband tif) </p> <p>raster_netMgr_2000_2019_20yrSum.tif: Gridded 20-yr sum net-migration 2000-2019 (5 arc-min) </p> <p> </p> <p>polyg_adm0_dataNetMgr.gpkg: National (adm 0 level) net-migration geospatial file (gpkg) </p> <p>polyg_adm1_dataNetMgr.gpkg: Provincial (adm 1 level) net-migration geospatial file (gpkg) (if not adm 1 level division, adm 0 used) </p> <p>polyg_adm2_dataNetMgr.gpkg: Communal (adm 2 level) net-migration geospatial file (gpkg) (if not adm 2 level division, adm 1 used; and if not adm 1 level division either, adm 0 used) </p> <p> </p> <p><strong>Files to run online net migration explorer </strong></p> <p>masterData.rds and admGeoms.rds are related to our online ‘Net-migration explorer’ tool (<a href="https://wdrg.aalto.fi/global-net-migration-explorer/">https://wdrg.aalto.fi/global-net-migration-explorer/</a>). The source code of this application is available in <a href="https://github.com/vvirkki/net-migration-explorer">https://github.com/vvirkki/net-migration-explorer</a>. Running the application locally requires these two .rds files from this repository. </p> <p> </p> <p><strong>Metadata </strong></p> <p><em>Grids: </em></p> <p>Resolution: 5 arc-min (0.083333333 degrees) </p> <p>Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax) </p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: Multiband geotiff; each band for each year over 2000-2019 </p> <p>Units: </p> <ul> <li> <p>Birth and death rates: births/deaths per 1000 people per year </p> </li> <li> <p>Net-migration: persons per 1000 people per time period (year, 3yr, 5yr, 20yr, depending on the dataset) </p> </li> </ul> <p> </p> <p><em>Geospatial polygon (gpkg) files: </em></p> <p>Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax) </p> <p>Temporal extent: annual over 2000-2019 </p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: gkpk </p> <p>Units: </p> <ul> <li> <p>Net-migration: persons per 1000 people per year </p> </li> </ul>
Arts and humanities in the LTER Network: understanding extent, values, and challenges by assessing the relevance of empathy in the LTER Network, 2013-2014
The Long-term Ecological Research (LTER) Network is a collection of 25 National Science Foundation-funded sites committed to long-term, place-based investigation of the natural world. While activities primarily focus on ecological research, arts and humanities inquiry emerged in 2002 and since then a substantial body of creative work has been produced at LTER-affiliated sites. These art-humanities-science collaborations parallel a wider trend in universities and nonprofits. However, there is little empirical work on the value and effectiveness of this work. After launching a survey in 2013 to assess the values and challenges associated with arts and humanities in the LTER Network, which identified empathy as a meaningful potential outcome of this creative work, we conducted a follow-up analysis to understand: the relevance of empathy in the LTER Network; the role of empathy in bridging arts, humanities, and science collaborations; and the capacity of empathy to connect wider audiences both to LTER science and to the natural world. Our research included phone interviews with representatives from 15 LTER sites and an audience perception survey at an LTER-hosted art show. We found that arts-humanities-science collaborations have great potential to catalyze relationships between scholars, the public, and the natural world; cultivate inspiration and empathy for the natural world; and spark awareness shifts that can enable pro-environmental behavior. Our research demonstrates the potential for art-humanities-science collaborations to facilitate conservation attitudes and action in the Network and beyond.
MCR LTER: Coral Reef: Coupled Natural-Human Systems: Survey of fish being sold on the roadside 2020-2021
This dataset includes the results of a survey on fish sold by the roadside in Moorea, French Polynesia. During 2020-2022, more than 7000 fish were identified and sized from photographs taken during the market surveys. These data were collected as part of CNH-L: Multiscale Dynamics of Coral Reef Fisheries: Feedbacks Between Fishing Practices, Livelihood Strategies, and Shifting Dominance of Coral and Algae (BCS-1714704) with additional support from the Moorea Coral Reef LTER (OCE- 1637396). This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 22-24354 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2023). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
The human Voice Areas: spatial organisation and inter-individual variability in temporal and extra-temporal cortices
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DWI Traveling Human Phantom Study
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Human hippocampal replay during rest prioritizes weakly learned information and predicts memory performance
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7 Tesla MRI of the ex vivo human brain at 100 micron resolution
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Robust functional mapping of layer-selective responses in human lateral geniculate nucleus with high-resolution 7T fMRI
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Model-based aversive learning in humans is supported by preferential task state reactivation
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Tumor growth kinetics of human LM2-4LUC+ triple negative breast carcinoma cells
<p><strong>Cell culture and data set</strong></p> <p>Tumor growth data used in this study were obtained from experiments involving the use of a LM2-4<sup>LUC+</sup> cells (or LM2-4), a metastatic variant of the human triple-negative breast carcinoma MDA-MB-231 cells. Animal studies were performed as described previously under Roswell Park Comprehensive Cancer Center (RPCCC) Institutional Animal Care and Use Committee (IACUC) protocol number 1227M [1-7]. Tumor growth data were pooled from eight separate experiments conducted with a total of 581 observations, and represent control (vehicle-treated) animals from published studies [1-7]. Vehicle formulation was carboxymethylcellulose sodium (USP, 0.5% w/v), NaCl (USP, 1.8% w/v), Tween-80 (NF, 0.4% w/v), benzyl alcohol (NF, 0.9% w/v), and reverse osmosis deionized water (added to final volume) and adjusted to pH 6 (see [3]) and was given at 10ml/kg/day for 7-14 days prior after tumor implantation and before tumor resection [1-7].</p> <ul> </ul> <p><strong>Tumor injections</strong></p> <p>LM2-4<sup>LUC+</sup> cells were orthotopically implanted (10<sup>6</sup> cells per injection) into the right inguinal mammary fat pads of 6- to 8-week-old female severe combined immunodeficient (SCID) mice.</p> <p><strong>Tumor measurements</strong></p> <p>Tumor size was measured regularly with calipers to a maximum volume of 2 cm<sup>3</sup>, calculated by the formula </p> <p><span class="math-tex">\(V = \frac{\pi}{6} w^2 L\)</span></p> <p>(ellipsoid) where <em>L</em> is the largest and <em>w</em> is the smallest tumor diameter.</p> <p><strong>Please cite: </strong>Vaghi C, Rodallec A, Fanciullino R, Ciccolini J, Mochel JP, et al. (2020) Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors, PLoS Comput Biol, 16, p. e1007178. <a href="https://doi.org/10.1371/journal.pcbi.1007178">https://doi.org/10.1371/journal.pcbi.1007178</a></p> <p> </p> <p>In the file, the columns correspond to:</p> <ul> <li>ID: identifier of the animal</li> <li>Time: day of the tumor measurement after implantation</li> <li>Observation: tumor measurement (in mm<sup>3</sup>)</li> </ul> <p> </p> <p><strong>References</strong></p> <p>[1] Benzekry, S., Lamont, C., Beheshti, A., Tracz, A., Ebos, J. M. L., Hlatky, L., & Hahnfeldt, P. (2014). Classical mathematical models for description and prediction of experimental tumor growth. PLoS Comput Biol, <em>10</em>(8), e1003800. http://doi.org/10.1371/journal.pcbi.1003800</p> <p>[2] Benzekry S, Tracz A, Mastri M, Corbelli R, Barbolosi D, Ebos JML. (2016) Modeling Spontaneous Metastasis Following Surgery: An In Vivo-In Silico Approach. Cancer Res.;76(3):535–547. doi:10.1158/0008-5472.CAN-15-1389.</p> <p>[3] Ebos JML, Lee CR, Bogdanovic E, Alami J, Van Slyke P, Francia G, et al. (2008) Vascular Endothelial Growth Factor-Mediated Decrease in Plasma Soluble Vascular Endothelial Growth Factor Receptor-2 Levels as a Surrogate Biomarker for Tumor Growth. Cancer Res.;68(2):521–529. doi:10.1158/0008-5472.CAN-07-3217.</p> <p>[4] Ebos JML, Mastri M, Lee CR, Tracz A, Hudson JM, Attwood K, et al. (2014) Neoadjuvant antiangiogenic therapy reveals contrasts in primary and metastatic tumor efficacy. EMBO Mol Med;6:1561–76. https://doi.org/10.15252/emmm.201403989</p> <p>[5] Ebos JML, Lee CR, Cruz-Munoz W, Bjarnason GA, Christensen JG, Kerbel RS. (2009) Accelerated metastasis after short-term treatment with a potent inhibitor of tumor angiogenesis. Cancer Cell;15:232–9. https://doi.org/10.1016/j.ccr.2009.01.021</p> <p>[6] Mastri M, Tracz A, Lee CR, Dolan M, Attwood K, Christensen JG, et al. (2018) A Transient Pseudosenescent Secretome Promotes Tumor Growth after Antiangiogenic Therapy Withdrawal. Cell Rep.; 25 (13):3706–20 e8. Epub 2018/12/28. https://doi.org/10.1016/j.celrep.2018.12.017</p> <p>[7] Vaghi C, Rodallec A, Fanciullino R, Ciccolini J, Mochel JP, et al. (2020) Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors, PLoS Comput Biol, 16, p. e1007178. <a href="https://doi.org/10.1371/journal.pcbi.1007178">https://doi.org/10.1371/journal.pcbi.1007178</a></p>
A Wi-Fi Channel State Information (CSI) and Received Signal Strength (RSS) data-set for human presence and movement detection
<p>This data-set consists of antenna-wise received signal strength (RSS) and channel state information (CSI) data. Both types of data have been captured using the <a href="https://dhalperi.github.io/linux-80211n-csitool/">Intel CSI Tools</a>. The RSS data have been used in our paper "Detecting Human Movement from Ambient Wi-Fi Signal Strength".</p> <p>This release extends the README with a data dictionary for the annotations. We hope to add more information about the data acquisition process (e.g., data acquisition protocols).</p>
Collection of global datasets for the study of floods, droughts and their interactions with human societies
<p>This is a collection of 134 global and free datasets allowing for spatial (and temporal) analyses of floods, droughts and their interactions with human societies. We have structured the datasets into seven categories: hydrographic baseline, hydrological dynamics, hydrological extremes, land cover & agriculture, human presence, water management, and vulnerability. Please refer to <a href="https://doi.org/10.1002/wat2.1424">Lindersson et al. (2020)</a> for further information about review methodology.</p> <p>The collection is a descriptive list, holding the following information for each dataset: </p> <ul> <li>Category<em> - as structured in Lindersson et al. (2020).</em></li> <li>Sub-category<em>- as structured in Lindersson et al. (2020).</em></li> <li>Abbreviation - <em>official or as specified in Lindersson et al. (2020).</em></li> <li>Title <em>- full title of dataset.</em></li> <li>Product(s)<em> - type of product(s) offered by the dataset.</em></li> <li>Period<em> - time period covered by the dataset, not defined for all datasets.</em></li> <li>Temporal resolution<em> - not defined for static datasets.</em></li> <li>Angular spatial resolution<em> - only defined for gridded datasets.</em></li> <li>Metric spatial resolution <em>- only defined for gridded datasets.</em></li> <li>Map scale</li> <li>Extent<em> - geographic coverage of dataset given in latitude limits.</em></li> <li>Description</li> <li>Creating institute(s)</li> <li>Data type<em> - raster, vector or tabular.</em></li> <li>File format</li> <li>Primary EO type<em> - specifies if the product primarily is based on remote sensing, ground-based data, or a hybrid between remote sensing and ground-based data.</em></li> <li>Data sources<em> - lists the data sources behind the dataset, to the extent this is feasible.</em></li> <li>Data sources also in this table<em> - data sources that are also included as datasets in this collection.</em></li> <li>Intentionally compatible with<em> - defines other datasets in this collection that the dataset is intentinoally compatible with.</em></li> <li>Citation<em> - dataset reference or credit.</em></li> <li>Documentation <em>- dataset documentation.</em></li> <li>Web address<em> - dataset access link.</em></li> </ul> <p>NOTE: Carefully consult the data usage licenses as given by the data providers, to assure that the exact permissions and restrictions are followed.</p>
Eigen scores for human genome assembly GRCh38 Part 4 (Chr1 - Chr2)
<p>Eigen is a spectral approach to the functional annotation of genetic variants in coding and noncoding regions. Eigen makes use of a variety of functional annotations in both coding and noncoding regions (such as protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics projects), and combines them into one single measure of functional importance. Eigen is an unsupervised approach, and, unlike many existing methods, is not based on any labelled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations.</p>
Eigen scores for human genome assembly GRCh38 Part 3 (Chr3 - Chr5)
<p>Eigen is a spectral approach to the functional annotation of genetic variants in coding and noncoding regions. Eigen makes use of a variety of functional annotations in both coding and noncoding regions (such as protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics projects), and combines them into one single measure of functional importance. Eigen is an unsupervised approach, and, unlike many existing methods, is not based on any labelled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations.</p>
Highly multiplexed histology reveals phenotypic and spatial characteristics of human Innate Lymphoid Cells in chronic inflammation - MELC tonsil data-set
<p><strong>53 marker MELC Run in human tonsil</strong>. Each image depicts the same field of view, sequentially stained with the depicted fluorescence-labelled antibodies. Images contain 2048 x 2048 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have not been normalized and intensities have not been adjusted.</p> <p> </p>
Review of the evidence for Oceans and Human Health relationships in Europe: A systematic map.
<p>This database details the results of a systematic mapping exercise linking marine exposures to measured human health outcomes for the Seas, Oceans and Public Health in Europe Project.</p>
SIRAH-CoV2 initiative: S1 Receptor Binding Domain in complex with human antibody CR3022 (PDBid: 6W41)
<p>This dataset contains the trajectory of a 12 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV-2 receptor binding domain in complex with a human antibody CR3022 (PDB id: 6W41). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Glycans have been removed from the structures.</p> <p>The file 6W41_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6W41_SIRAHcg_12us_prot.tar contains only the protein coordinates, while 6W41_SIRAHcg_12us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6W41_SIRAHcg_12us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6w41_SIRAHcg_prot.prmtop 6w41_SIRAHcg_prot.ncrst 6w41_SIRAHcg_prot_12us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Martín Soñora (msonora@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
Experimental data for PanDDA analysis of the bromodomain of human FALZ
<p>The repository contains processed data from the entire crystallographic fragment screen of the bromodomain of human nucleosome-remodeling factor subunit BPTF (FALZ). Crystals of FALZ were screened against the DSPL and 3D-Fragment Consortium Libraries by X-ray Crystallography at the XChem facility of Diamond Light Source beamline I04-1 (FALZ_XChem_screen.tar.bz2). Additionally, metadata about the experiment can be found in the <em>mainTable</em> of the corresponding SQLite database file (FALZ_XChem_screen.sqlite). All identified ligand-bound structures were deposited in the Protein Data Bank under Group ID <strong><a href="https://www.rcsb.org/search/structure?q=pdbx_deposit_group.group_id:G_1002123">G_1002123</a></strong>. The individual PDB codes are:</p> <ul> <li>FALZA-x0079 5R4G</li> <li>FALZA-x0085 5R4H</li> <li>FALZA-x0172 5R4I</li> <li>FALZA-x0177 5R4J</li> <li>FALZA-x0271 5R4K</li> <li>FALZA-x0309 5R4L</li> <li>FALZA-x0402 5R4M</li> <li>FALZA-x0438 5R4N</li> </ul> <p>All structures necessary to reproduce the deposited PanDDA event maps which were used for ligand identification were deposited in the Protein Data Bank under PDB ID <a href="https://www.rcsb.org/structure/5R4O">5R4O</a> (group ID <strong><a href="https://www.rcsb.org/search/structure?q=pdbx_deposit_group.group_id:G_1002124">G_1002124</a></strong>).</p> <p> </p> <p><strong><em>Usage:</em></strong></p> <p>download <em>FALZ_XChem_screen.tar.bz2</em> and save into the desired project directory, e.g.</p> <pre><strong>/home/me/FALZ</strong></pre> <p>unpack the tar archive:</p> <pre><strong>tar –xvjf FALZ_XChem_screen.tar.bz2</strong></pre> <p>run pandda, e.g.</p> <pre><strong>pandda.analyse data_dirs="/home/me/FALZ/*" out_dir="/home/me/FALZ_pandda" pdb_style=dimple.pdb mtz_style=dimple.mtz</strong></pre> <p>For more information about PanDDA, please check the <a href="http://www.ccp4.ac.uk/html/pandda.html">PanDDA CCP4 program documentation</a>.</p> <p> </p> <p><em><strong>Reference:</strong></em></p> <p>Pearce, N. M. <em>et al.</em> A multi-crystal method for extracting obscured crystallographic states from conventionally uninterpretable electron density. <em>Nature Communications</em> <strong>8</strong>, ncomms15123 (2017).</p> <p> </p>
Experimental data for PanDDA analysis of human JMJD1B
<p>The repository contains processed data from the entire crystallographic fragment screen of human JMJD1B at the XChem facility of Diamond Light Source beamline I04-1 (JMJD1BA_XChem_screen.tar.bz2). Additionally, metadata about the experiment can be found in the <em>mainTable</em> of the corresponding SQLite database file (JMJD1BA_XChem_screen.sqlite). All identified ligand-bound structures were deposited in the Protein Data Bank under Group ID <strong><a href="https://www.rcsb.org/search/structure?q=pdbx_deposit_group.group_id:G_1002146">G_1002146</a>. </strong>All structures necessary to reproduce the deposited PanDDA event maps which were used for ligand identification were deposited in the Protein Data Bank under PDB ID <a href="https://www.rcsb.org/structure/5R7X">5R7X</a> (group ID <strong><a href="https://www.rcsb.org/search/structure?q=pdbx_deposit_group.group_id:G_1002141">G_1002141</a></strong>).</p> <p> </p> <p><strong><em>Usage:</em></strong></p> <p>download <em>JMJD1BA_XChem_screen.tar.bz2</em> and save into the desired project directory, e.g.</p> <p><strong>/home/me/JMJD1B</strong></p> <p>unpack the tar archive:</p> <p><strong>tar –xvjf JMJD1BA_XChem_screen.tar.bz2</strong></p> <p>run pandda, e.g.</p> <p><strong>pandda.analyse data_dirs="/home/me/JMJD1B/*" out_dir="/home/me/JMJD1B_pandda" pdb_style=dimple.pdb mtz_style=dimple.mtz</strong></p> <p>For more information about PanDDA, please check the <a href="http://www.ccp4.ac.uk/html/pandda.html">PanDDA CCP4 program documentation</a>.</p> <p> </p> <p><strong><em>Reference:</em></strong></p> <p>Pearce, N. M. <em>et al.</em> A multi-crystal method for extracting obscured crystallographic states from conventionally uninterpretable electron density. <em>Nature Communications</em> <strong>8</strong>, ncomms15123 (2017).</p> <p> </p>
Anatomically Detailed Human Atrial FE Meshes
<p>The left atrium (LA) has a complex anatomy with heterogeneous wall thickness and curvature. We include 3 patient-specific anatomical FE meshes with rule-based myofiber directions of each of the anatomies included in our study ("The impact of wall thickness and curvature on wall stress in patient-specific electromechanical models of the left atrium", BMMB, 2020, <a href="https://pubmed.ncbi.nlm.nih.gov/31802292/">https://pubmed.ncbi.nlm.nih.gov/31802292/</a>).<br> Additionally we include<br> - a model with Gaussian noise added (mean 0 um , standard deviation 100 um) to the initial geometry of patient case 3 and subsequently smoothed using ParaView; and <br> - a mesh with a constant wall thickness of 0.5 mm generated based on the endocardial surface of patient case 3.</p> <p>The meshes are given in VTK file format (.vtu) and in the binary format used for the Cardiac Arrhythmia Research Package simulator, see <a href="https://carpentry.medunigraz.at/carputils/index.html">https://carpentry.medunigraz.at/carputils/index.html</a> and <a href="https://opencarp.org/">https://opencarp.org</a>. Here, for each of the geometries, we include a list of nodal coordinates (.bpts file), a list of triangular elements (.belem file), fiber fields (.blon file), surface files (*.surf files), and surface points (*.surf.vtx files).<br> Surface files include the endocardium (laendo.surf), the epicardium (laepi.surf), the mitral valve ring (mitralvv.surf), the pulmonary outlet rings (pulvring.surf) and lids (lid*.vtx) to close the five in- and outlets of the LA.</p> <p>Using the open source mesh utiliy "MeshTool" (<a href="https://bitbucket.org/aneic/meshtool/src/master/README.md">https://bitbucket.org/aneic/meshtool/src/master/README.md</a>)<br> meshes can be manipulated or converted to VTK or EnSight file formats.</p>
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.