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12,393 results for “Material”
Peridigm User Material Interface Dataset
<p>The dataset includes a Fortran user material routine which is used for a reference finite element and peridynamic model of a dogbone.</p> <p>The user material interface allows the simplified use of already existing material routines in the peridynamic framework Peridigm. The interface is based on the Abaqus UMAT definition and allows the integration of these Fortran routines directly into Peridigm. The integration of UMAT routines based on finite elements in Peridigm eliminates the need for parallel development of existing material models from classical continuum mechanics theory. Thus, all developer of material models can utilize both finite element frameworks and Peridynamics. This opens up new possibilities for analysis, verification and comparison. In order to be able to use the material routine, the UMAT file must be precompiled and copied to a specific folder. With this interface many material routines can be reused and applied to progressive failure analysis.</p>
Datasets for submitted paper "Color appearance in rotational material jetting"
<p>This folder contains data for the submitted paper "Color appearance in rotational material jetting"<br> For more information, please contact Ali Payami Golhin (payami.ag@gmail.com)</p>
Supplementary materials to the paper: Automatic Parameters Tuning of Late Reverberation Algorithms for Audio Augmented Reality
<p>Supplementary materials to the paper:</p> <blockquote> <p>Riccardo Bona, Davide Fantini, Giorgio Presti, Marco Tiraboschi, Isaac Engel and Federico Avanzini. 2022. Automatic Parameters Tuning of Late Reverberation Algorithms for Audio Augmented Reality. In <em>Proceedings of International Conference on Audio Mostly</em>.</p> </blockquote> <p>The supplementary materials include the reverberated audio stimuli employed in the MUSHRA listening test reported in the paper. For each type of audio stimuli (Drums, Sax and Speech) the version reverberated with each of the six target Room Impulse Responses (RIRs) is provided along with the versions reverberated using the reverb matching method proposed in the paper (two different artificial reverberators have been considered: FDN and Freeverb).</p> <p>Further, the reverberation times (<span class="math-tex">\(T_{20}\)</span>) per octave band for each considered RIR are provided.</p>
Training data for 'Upload data to ENA' (Galaxy Training Material)
<p>The data here is a subset of the data published in 10.5281/zenodo.3732359 to be used in GTN 'Upload data to ENA' tutorial.</p> <p>Human traces have been removed following <a href="https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/human-reads-removal/tutorial.html">https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/human-reads-removal/tutorial.html</a></p> <p>We produced consensus sequences (*.fasta) for the Illumina PE data following SARS-CoV-2-PE-Illumina-WGS-variant-calling (https://workflowhub.eu/workflows/113?version=4), SARS-CoV-2-variation-reporting (https://workflowhub.eu/workflows/109?version=5) and COVID-19-consensus-construction (https://workflowhub.eu/workflows/138?version=4) workflows.</p>
A phenomenological law for complex granular materials from Mohr-Coulomb theory
<p>The compressed directory contains the data in .csv format used for the PCA analysis for each dataset (1, 2 and 3). </p>
Zeolite Templated Carbon Materials - DFTB Structural Database
<p>Zeolite-templated carbon (ZTC) is a unique porous carbonaceous material in that its structure is ordered at the nanometre scale, enabling a representative periodic description at the atomistic level. A structural library for ZTC of varying compositions was created using density functional tight binding (DFTB) potentials parameterized for materials science applications (matsci-0-3). We provide here quantum chemical-refined structures of models with CH, CHO, CHON, CHOB, and CHOBN compositions with various degrees of heteroatom substitution. The "initial ZTC structure" files correspond to the initial model used in our work that was developed using molecular mechanics, empirical force fields. These structural models comprise the characteristic morphological features of highly porous carbon materials, such as open-blade surfaces, edges, saddles, and closed-strut formations, spanning a range of curvatures and characteristic sizes. The optimized structures in CIF and native DFTB file formats are organized in the "stationary structure" file based on the optimization pathways that lead to the stationary structures.</p> <p>Secondly, we carried out alternating compression and expansion of the CHO model unit cell to determine the lowest energy structure as well as to obtain the bulk modulus. The file "bulk modulus" contains two data sets that describe the deformational energy landscape of pure faujasite zeolite, Na-substituted zeolite, and the ZTC model structure.</p> <p>The file "analysis tools" is a representative compilation of utilities for file format conversion, fractional vs. Cartesian crystal coordinates, and structural analysis spreadsheets.</p> <p>The agreement between experimental measurements and the computational model is remarkable that demonstrates the power of approximate density functional theory as a cost-effective computational tool with chemical accuracy for the investigation of structure/property relationships in real-world carbon-based solids.</p>
Replication Materials for Disclosure Limitation and Confidentality Protection in Linked Data
<p>These are the data and derived figures as used in the chapter by Abowd, Schmutte, and Vilhuber, "Disclosure Limitation and Confidentiality Protection in Linked Data"</p>
Urban material ground truth data for the 2015 APEX hyperspectral image of Brussels
<p>This dataset entails a spectral library file (.sli file with matching .hdr text file) with 1350 georeferenced and labeled spectra derived from the 2m resolution airborne hyperspectral APEX image of Brussels (Belgium) that was acquired during the summer of 2015. The labeled spectra included in this dataset describe level 2A surface reflectance profiles ranging between 450 and 2431 nm. The original APEX image files can be downloaded via the <a href="https://belair.vito.be/en/belair-data" target="_blank" rel="noopener">Belair website</a>, and the preprocessing performed on this image data is explained in Sterckx et al. (2016) and Vreys et al. (2016). See the "Related works" section of this data publication.</p> <p>The main purpose of this dataset is to provide Ground Truth (GT) data for remote sensing-based mapping experiments with a generic urban spectral library, performed in the frame of the GENLIB research project. The content of this dataset hence focuses on the optical reflectance/absorption behaviour of urban surface materials and their variations.</p> <p>The spectra included in this dataset were manually sampled from the above mentioned APEX image and labeled using ancillary reference data (very high-resolution aerial imagery, Google Street View, LiDAR ...), already published urban spectral libraries, terrain knowledge and some field work. The header of the spectral library contains the various labels that were added to these spectra. These labels cover:</p> <ul> <li>EAGLE Land Cover Component (LCC) from the EAGLE matrix version 3.1. Visit the <a href="https://land.copernicus.eu/en/eagle" target="_blank" rel="noopener">website of the EAGLE framework</a> for more information.</li> <li>Material Groups (MG).</li> <li>Artificial Material Types (AMT).</li> <li>Artificial Material Coating or Fabrication (AMCF).</li> <li>Artificial Material Forms (AMF).</li> <li>Latitude (degrees, WGS84).</li> <li>Longitude (degrees, WGS84).</li> </ul> <p>The value domains of these spectrum attributes are described in the look-up table included as a CSV-file in this data publication.</p> <p>While considerable efforts have been made to safeguard the accuracy of these data, they are published as is, without any warranty or support. Use at your own discretion.</p>
Supplementary Material for Embodied Emotions in Ancient Neo-Assyrian Texts Revealed by Bodily Mapping of Emotional Semantics
<p>This dataset accompanies the article "Embodied Emotions in Ancient Neo-Assyrian Texts Revealed by Bodily Mapping of Emotional Semantics" (Lahnakoski & Bennett et al., submitted). </p> <p>It includes the Neo-Assyrian text corpus that is the basis for the word embeddings, a list of the Akkadian emotion and body words of interest for this study, and the scripts, toolboxes, and data used to generate the heat maps of the body.</p> <p>There is an additional folder containing the high resolution figures included in the article.</p> <p>A detailed ReadMe (README.txt) provides an overview of the folders.</p>
Supplemented material to "Mycobacteriosis in various pet and wild birds from Germany: pathological findings, coinfections, and characterisation of causative Mycobacteria."
<p>This is the supplemented material to the publication "Mycobacteriosis in Various Pet and Wild Birds from Germany: Pathological Findings, Coinfections, and Characterization of Causative Mycobacteria". <br>The causative agents and confounding factors of mycobacteriosis in a set of pet (n=45) and some wild birds (n=5) from Germany were examined in this study. Not only Mycobacterium genavense (Mg), but also M. avium subsp. avium (Maa) and M. avium subsp. hominissuis (Mah), contributed to mycobacteriosis in these birds. The isolates were characterized by a combination of different typing methods. The genetic diversity of isolates belonging to Mg, Maa and Mah differed. Various coinfections by viruses, endoparasites, fungi and other bacterial species did not affect the manifestation of mycobacteriosis. Cross pathological fidings were more often seen in mycobacteriosis caused by Ma compared to Mg suggesting a different pathogenicity of the two species. New genotypes of Mah were identified in these birds that is important for epidemiological studies and for understanding the zoonotic role of this pathogen, as the subsp. hominissuis represents an increasing public health concern. The study provides some evidence of correlation between individual Maa genotypes and virulence which will have to be confirmed by broader studies.</p>
Supporting Material for "Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review"
<p>This dataset contains all supporting material for the paper "Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review", published in the journal Swiss Psychology Open:</p> <p><em>Mack, M., Scarampi, C., Joly-Burra, E., Zuber, S., de Freitas, C., Teixeira, R. and Kliegel, M. (2025) ‘Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review’, Swiss Psychology Open, 5(1), p. 2. Available at: <a href="https://doi.org/10.5334/spo.81.">https://doi.org/10.5334/spo.81</a>.</em></p> <p>It includes the following documents and files:</p> <p><strong>S1. Protocol:</strong> ADVANCE Protocol for desk reviews</p> <p><strong>S2. Search strategy</strong></p> <p><strong>S3. Guidelines for title and abstract screening:</strong> Guidelines for the selection of articles included in the desk review</p> <p><strong>S4. Guidelines full-text screening:</strong> Guidelines for the selection of articles included in the desk review</p> <p><strong>S5. Guidelines data extraction:</strong> ADVANCE Guidelines/codebook data extraction</p> <p><strong>data extraction_desk review_switzerland.xlsx</strong></p> <p>This desk review was conducted as part of the ADVANCE project, which aims to enhance our understanding of mental health promotion and prevention. This desk review evaluates the current state of interventions for mental health and cognitive functioning among older adults in Switzerland focusing on the features of these interventions as well as on Swiss-specific contextual factors that contribute to vulnerability and stigma. This results of the desk review has been submitted for publication to 'LIVES Working Papers' and 'Swiss Psychology Open' . The two versions of the desk review differ slightly. The version for LIVES Working Papers, included the results of the Delphi survey and the resulting intervention scenarios. The version for Swiss Psychology Open, did not include the Delphi survey results and the resulting intervention scenarios, but included a more detailed discussion of the review results.</p>
Introduction to bulk RNAseq analysis: supplementary material
<p><strong>Vampirium setup</strong></p><p>This archive contains materials (datasets, exercises and slides, etc) used for the Introduction to bulk RNAseq analysis workshop taught at the University of Copenhagen by the Center for Health Data Science (HeaDS). The course repo can be found on <a href="https://github.com/hds-sandbox/bulk_RNAseq_course">Github</a>:</p><p>Assignments.zip contains exercises for the preprocessing part of the course, like fastqc and multiqc examples of bulk RNAseq experiments</p><p>Data.zip contains count matrices (both traditional counts and salmon pseudocounts), as well as sample metadata (samplesheet.csv) and backup results from the preprocessing pipeline.</p><p>Notes.zip contains supplementary materials such as extra pdfs for more information on bulk RNAseq technology.</p><p>Slides.zip contains all the slides used in the workshop.</p><p>raw_reads.zip contains the raw reads from the bulk RNAseq experiment (<a href="https://doi.org/10.1016/j.celrep.2014.10.054">10.1016/j.celrep.2014.10.054</a>) used in this course.</p>
Experimental data generated on the stability of hydrophobic porous materials
<div>/* **********</div> <div>/* This work is licensed under a Creative Commons Attribution 4.0 International License.</div> <div>/* **********</div> <div> </div> <div>Open access to experimental data generated by the project Electro-Intrusion (101017858, Horizon 2020, European Union, https://www.electro-intrusion.eu/en) along with the research to be used in intrusion-extrusion applications. Research pertaining to Task 2.1 (WP2). </div> <div>Underlying data for the publication Amayuelas, E. et al. Bimetallic Zeolitic Imidazole Frameworks for Improved Stability and Performance of Intrusion-Extrusion Energy Applications. The Journal of Physical Chemistry 2023, 127, 18310-18315. https://doi.org/10.1021/acs.jpcc.3c04368. Data related to Figures 2, 3 and 4 in the article.</div> <div> </div> <div>Dataset Identifier: 10.5281/zenodo.11273904</div> <div> </div> <div>Contact person: Eder Amayuelas (CIC energiGUNE). ORCID: </div> <div> </div> <div> </div> <div>The archive 'JPCC_3c04368.zip' contains 25 files:</div> <p> </p>
Supplemental Material to "Tenacity of Animal Disease Viruses on Wood Surfaces Relevant to Animal Husbandry"
<p>Data set for individual titre reduction of viruses over a period of time in multiple experiments.</p>
Supplementary material for 'Revealing patterns of nocturnal migration using the European weather radar network'
<p>This package contains data, filters and visualizations from <a href="https://doi.org/10.1111/ecog.04003">Nilsson and Dokter et al. (2019)</a>.</p> <p><strong>Files</strong></p> <p><strong>radar_metadata.csv</strong>: Metadata for the 84 European radars considered for this study. Includes radar code (<code>odim_code</code> = <code>country</code> + <code>odim_code_3char</code> and alternative radar code <code>vp_radar</code>), radar site location (<code>location</code>, <code>latitude</code>, <code>longitude</code>), radar site elevation (<code>site_altitude_asl</code> in meters above sea level) and radar altitude range used in this study (<code>min_height_cut_asl</code> and <code>max_height_cut_asl</code> in meters above sea level).</p> <p><strong>vp.zip</strong>: Vertical profiles of birds (vp) data, processed from the radar volume data following procedures described by Dokter et al. (2011), using the vol2bird algorithm in the R package bioRad. Zip file includes vp data for the 84 European radars considered for this study from September 19 to October 9, 2016 (21 days). This time period is characterized by strong passerine migration throughout Europe. Files are organized in radar (= <code>odim_code</code>), date and hour directories and follow the <a href="https://github.com/adokter/vol2bird/wiki/ODIM-bird-profile-format-specification">ODIM bird profile format specification</a>. Data can be read with the <a href="https://github.com/adokter/bioRad/">R package bioRad</a>.</p> <p><strong>vp_processing_settings.yaml</strong>: Data selection setting for this study, based on data quality criteria. File lists for each radar the altitudes to include (<code>include_heights</code>), time periods to exclude (<code>exclude_datetimes</code>) and reasons for exclusion (comments). 70 of the 84 radars were retained after filtering.</p> <p><strong>vp_processed_70_radars_20160919_20161009.csv</strong>: Processed vp data for 70 radars. Is the result of processing <code>vp.zip</code> with <code>vp_processing_settings.yaml</code> and <code>radar_metadata.csv</code> using <a href="https://doi.org/10.5281/zenodo.1173544">vp-processing</a> (Desmet & Nilsson 2018). Note: includes all timestamps: day and night & those marked for exclusion (marked in <code>exclusion_reason</code>). This data file forms the basis for analysis in the study.</p> <p>Headers are:</p> <ul> <li><code>radar_id</code>: odim_code of the radar</li> <li><code>datetime</code>: timestamp</li> <li><code>HGHT</code>: lower altitude of altitude bin (m above sea level)</li> <li><code>u</code>: bird ground speed towards east (m/s)</li> <li><code>v</code>: bird ground speed towards north (m/s)</li> <li><code>dens</code>: bird density (birds/km3)</li> <li><code>dd</code>: bird flight direction (degrees from north)</li> <li><code>ff</code>: bird ground speed (m/s)</li> <li><code>DBZH</code>: reflectivity factor (dBZ) in horizontal polarisation</li> <li><code>mtr</code>: migration traffic rate (birds/km/h)</li> <li><code>day_night</code>: timestamp occurs during <code>day</code> or <code>night</code> (based on sunrise/sunset)</li> <li><code>date_of_sunset</code>: date at sunset, with night timestamps between midnight and sunrise belonging to the previous date</li> <li><code>exclusion_reason</code>: reason timestamp is excluded in vp_processing_settings.yaml (if applicable). Excluded timestamps have <code>NA</code> values for <code>u</code>, <code>v</code>, <code>dens</code>, <code>dd</code>, <code>ff</code>, <code>DBZH</code>, and <code>mtr</code>.</li> </ul> <p><strong>vp_flowviz.csv:</strong> Input data for visualizations. Is the result of processing <code>vp_processed_70_radars_20160919_20161009.csv</code> using <code>vp-to-flowviz.Rmd</code> in <a href="https://doi.org/10.5281/zenodo.1173544">vp-processing</a> (Desmet & Nilsson 2018). Aggregates data in hourly bins for 200-2000m (<code>altitude_band</code> = 1) and above (<code>altitude_band</code> = 2). Only altitude band 1 is used in visualizations.</p> <p><strong>flowviz.mov:</strong> Screencast of <code>vp_flowviz.csv</code> visualized with <a href="https://doi.org/10.5281/zenodo.57472">Bird migration flow visualization v2</a> (Desmet et al. 2016, Shamoun-Baranes et al. 2016). The visualization extrapolates the migration over the entire sampling range (cropped in the screencast due to technical limitations and thus excluding the Bulgarian radar), not taking topography or water bodies into account, and shows the ground speed (length of arrows) and direction of migration over time. Note that density is not shown: low density movements can therefore appear as strong as high density movements when ground speeds are similar.</p> <p><strong>cartoviz.mov:</strong> Screencast of <code>vp_flowviz.csv</code> visualized as an interactive map with <a href="https://carto.com">CARTO</a>. Visualization shows migration density (size of circles) and mean direction (colour) over time. The interactive map is available at <a href="https://inbo.carto.com/u/lifewatch/builder/8685140f-8d8c-4d06-9e1e-25d051d43748/embed">https://inbo.carto.com/u/lifewatch/builder/8685140f-8d8c-4d06-9e1e-25d051d43748/embed</a>.</p>
Materials for 2d representation of the HathiTrust Library
<p>Materials to create the LargeVis visualization online at http://creatingdata.us/datasets/hathi-features/, and described in <em>Benjamin Schmidt, "Stable random projection: lightweight, general-purpose dimensionality reduction for digitized libraries," Journal of Cultural Analytics. October 3, 2018.</em></p> <p>Two items. First, `hathi_pca.bin`: a binary file with 100-dimensional representations of the complete Hathi Trust Extended Features set. These began as 1280-dimensional SRP features, and were reduced to 100 dimensions using a PCA transformation matrix derived using a random sample of the full 13 million book set. Vectors were reduced to unit length before PCA, but not afterwords; this means that in general, their length gives some sense of much information was lost in the PCA representation. This can be read using the code at https://github.com/bmschmidt/pySRP, or anything that reads word2vec formatted vectors. Includes HathiTrust identifiers.</p> <p>Second, `hathi.tsv.gz`: a row oriented set containing a variety of metadata fields for each set, including (as 'x' and 'y') the coordinates of a 2-d LargeVis visualization. This is the immediate input to the visualization at ttp://creatingdata.us/datasets/hathi-features/. Columns should be relatively straightforward; they are derived from the HathiTrust MARC records, which can be accessed through Hathi's public API. Classification codes ('lc1') are using the Library of Congress classification; they represent the subclass (generally two characters, though it can be one or three). The first character alone represents the LC class and can be useful for coloring high-level overviews.</p> <p>These two files can be merged through the Hathi Trust identifier present in both.</p> <p> </p>
Aversive imagery causes de novo fear conditioning (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Mueller, E. M., Sperl, M. F. J., & Panitz, C. (2019). Aversive imagery causes de novo fear conditioning. <em>Psychological Science</em>, <em>30</em>(7), 1001–1015.</strong></p> <p>In classical fear conditioning, neutral conditioned stimuli (CS) that have been paired with aversive physical unconditioned stimuli eventually trigger fear responses. Here, we test whether aversive mental images systematically paired with a CS may also cause de novo fear learning in the absence of any external aversive stimulation. In two experiments, <em>N</em>=45 and <em>N</em>=41 participants were first trained to produce aversive, neutral, or no imagery in response to one of three different visual imagery cues. In a subsequent imagery-based differential conditioning paradigm, each of the three cues systematically co-terminated with one of three different neutral faces. Although the face that was paired with the aversive imagery cue was never paired with aversive external stimuli or threat-related instructions, participants rated it as more arousing, unpleasant, and threatening and displayed relative fear bradycardia and fear-potentiated startle. These results could be relevant for the development of fear and related disorders without trauma.</p>
Accompanying material to the Inventory of opportunities and bottlenecks in policy to facilitate the adoption of soil-improving techniques
<p>Inventory of policies at EU and country level for the inventory and analysis of bottlenecks and opportunities in sectoral and environmental policies to facilitate the adoption of Soil-Improving Cropping Systems (SICS).</p>
Global restoration opportunities in tropical rainforest landscapes - Supplementary Materials - Spatial Data Layers
<p><strong>Global restoration opportunities in tropical rainforest landscapes</strong></p> <p><strong>Sci Adv 5 (7), eaav3223</strong></p> <p><strong>DOI: 10.1126/sciadv.aav3223</strong></p> <p><strong><a href="https://advances.sciencemag.org/content/5/7/eaav3223">https://advances.sciencemag.org/content/5/7/eaav3223</a></strong></p> <p><strong>Supplementary Materials</strong></p> <p><strong><a href="https://advances.sciencemag.org/content/suppl/2019/07/01/5.7.eaav3223.DC1">https://advances.sciencemag.org/content/suppl/2019/07/01/5.7.eaav3223.DC1</a></strong></p> <p><strong>Spatial Data layers:</strong></p> <p><strong><a href="https://doi.org/10.5281/zenodo.3233495">https://doi.org/10.5281/zenodo.3233495</a></strong></p> <p><strong>_OutR10:</strong></p> <p><strong>r_10.img → Global restoration opportunity score (ROS)</strong></p> <p><strong>r_10_sc.img → Global restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_nt_sc.img → Neo Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_aa_sc.img → Australiasia restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_at_sc.img → Afro Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_im_sc.img → Indo Malay restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_nt_sc.img → Neo Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p> </p> <p><strong>_OutBasics:</strong></p> <p><strong>r_1.img → Study Area</strong></p> <p><strong>r_2.img → Restorable Area</strong></p> <p><strong>r_3.img → Restoration Benefits</strong></p> <p><strong>r_4.img → Restoration feasibility</strong></p> <p><br> <strong>_OutCountry:</strong></p> <p><strong>r_10_XXX_sc.tif → restoration opportunity score (ROS) for country XXX – rescaled 0-1</strong></p> <p><br> <strong>_OutHotspots:</strong></p> <p><strong>r_10_hotspot_XXX_hotspot_area_sc.tif → restoration opportunity score (ROS) for conservation hotspot area XXX – rescaled 0-1</strong></p> <p><strong>r_10_hotspots_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in conservation hotspots</strong></p> <p><br> <strong>_OutKBA:</strong></p> <p><strong>r_10_XXX_sc.tif → restoration opportunity score (ROS) for Key Biodiversity Area XXX – rescaled 0-1</strong></p> <p><strong>r_10_kba_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in Key Biodiversity Areas</strong></p> <p><br> <strong>_OutAichi:</strong></p> <p><strong>r_10_aichi_XXX.tif → Top 15% area of with highest restoration opportunity score (ROS) in country XXX</strong></p> <p><strong>r_10_aichi.img → Top 15% area of with highest restoration opportunity score (ROS) global</strong></p> <p><br> <strong>_OutBonn:</strong></p> <p><strong>r_10_XXX_Bonn.img → Area with highest restoration opportunity score (ROS) in country XXX according to their Bonn Challenge commitments</strong></p> <p> </p> <p><strong>_OutParis:</strong></p> <p><strong>r_10_at_paris.img → Area with highest restoration opportunity score (ROS) in Afro Tropic Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><strong>r_10_im_paris.img → Area with highest restoration opportunity score (ROS) in Indo Malay Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><strong>r_10_nt_paris.img → Area with highest restoration opportunity score (ROS) in Neo Tropic Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><br> <strong>_OutTEOW:</strong></p> <p><strong>r_10_ECOREGION_XXX_sc.tif → restoration opportunity score (ROS) for Ecoregion XXX – rescaled 0-1</strong></p> <p><strong>r_10_ECOREGION_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in Ecoregions</strong></p> <p> </p> <p><strong>_OutAll</strong></p> <p><strong>alltargets.img → Area with highest restoration opportunity score (ROS) according to all targets (excluded from the paper)</strong></p> <p> </p>
Supplementary material for the paper "DS Andromedae, A Detached Eclipsing Double-Lined Spectroscopic Binary in the Galactic Cluster NGC 752
<p>Supplementary material supporting the paper "DS Andromedae: A Detached Eclipsing Double-Lined Spectroscopic</p> <p>Binary in the Galactic Cluster NGC 752" by E. F. Milone, S. J. Schiller, Th. Mellergaard Amby, and S. Frandsen.</p> <p>It includes:</p> <p>A Read-me file in three formats (docx, rtf, pdf); Unabridged Section 3 with extended modeling details (pdf);</p> <p>Extended spreadsheet version of Table 3 of adjusted parameters (pdf); Extended spreadsheet version of Table 8 of absolute</p> <p>parameters (pdf); and Complete Table 15 of photometric data (txt): and a sample DC input file (for Model 41, used in the</p> <p>DS And modeling) in dat format.</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.