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635 results for “Attributes”
Analyzing App Store Comments and Quality Attributes for Defining an Inspection Checklist for Mobile Educational Games
<p>To evaluate educational games, several techniques have been proposed considering different quality attributes. However, there are still several educational games for the mobile context that have low scores in the app stores. These stores allow users to make comments to evaluate the applications, as this data can be useful for the development team that aims to meet users' expectations. The analysis of comments made by users can help identify which attributes impact the use of mobile educational games. In this paper, an inspection checklist is proposed to evaluate mobile educational games. To complement the attributes identified in the analysis of comments, attributes from existing techniques for evaluating mobile educational games were also considered. The final evaluation form contains a total of 82 attributes distributed in evaluation categories, such as: user interface, mobility, pedagogy, gameplay, among others. To evaluate the proposed technique, an evaluation was carried out with the checklist in two mobile educational games available in the Google Play Store, different from those used to define the technique. The initial results indicate that the proposed checklist allows the identification of problems pointed out by users in the comments left in the app store.</p> <p> </p> <p><a href="https://zenodo.org/api/files/8ca1af4d-6f7c-440f-a272-9366de2ad3ef/SBES%202020%20-%20Ideias%20Inovadoras%20e%20Resultados%20Emergentes%20-%20Analisando%20atributos%20de%20qualidade%20e%20coment%C3%A1rios%20de%20lojas%20de%20aplicativos%20para%20a%20defini%C3%A7%C3%A3o%20de%20uma%20t%C3%A9cnica%20de%20inspe%C3%A7%C3%A3o%20de%20jogos%20educacionais%20m%C3%B3veis.mp4">SBES 2020 - Ideias Inovadoras e Resultados Emergentes</a></p>
Model output for: Attributing causes of future climate change in the California Current System with multi-model downscaling
<p>Regional Ocean Modeling System outputs from dynamic downscaling of Coupled Model Intercomparison Project climate forcings in the California Current system, including projections with full climate forcings, as well as attribution experiments with only changes in wind, heat fluxes and other properties changing stratification, and boundary biogeochemical forcings. Output variables include euphotic zone integrated net primary productivity, and incident photosytnehtically available radiation, and ocean temperature, salinity, vertical velocity, and dissolved oxygen and nitrate concentrations at select depths.</p>
Data and code for: "Large reductions in tropical bird abundance attributable to heat extreme intensification "
<p>Data and code necessary to reproduce results of paper:</p> <p>Large reductions in tropical bird abundance attributable to heat extreme intensification</p> <p>By Maximilan Kotz, Tatsuya Amanon and James Watson.</p> <p>Please contact maximilian.kotz@bsc.es / maxkotz@pik-potsdam.de for questions.</p> <p> </p>
FishPass Sortable Attribute Database: Phenological, morphological, physiological, and behavioural characteristics related to passage and movement of Great Lakes fishes
<p>In-stream barriers pose threats to fishes, including habitat loss, constraints on migration, and reduced connectivity between populations. Despite many negative consequences, barriers can serve to protect native species by limiting the spread of invasive species. For example, in the Laurentian Great Lakes, physical barriers have long been used to control invasive Sea Lamprey (<em>Petromyzon marinus</em>) populations by limiting access to potential upstream spawning and rearing habitat. Selective fish passage systems could solve this connectivity conundrum but must efficiently pass multiple native or desirable species while blocking invasive species. Designing such fish passage systems requires an understanding of the phenology, morphology, physiology, and behaviour (attribute dimensions) of fishes in the community. Here, we describe the first comprehensive collection of sortable attributes associated with fish passage. The integrated database consists of 21 biological attributes that influence the movement and passage of 220 species in the Great Lakes. Data coverage varies with species, taxonomic orders, and attribute dimensions. Behavioural attributes were typically underrepresented in the literature and the ecology of potential invaders was not well understood. The synthesis described herein is a critical step towards a holistic approach to fish passage design and may help to inform management actions related to population connectivity.</p>
Fig. 8. Misgolas wayorum n in Trapdoor Spiders of the Genus Misgolas (Mygalomorphae: Idiopidae) in the Sydney Region, Australia, With Notes on Synonymies Attributed to M. rapax
Fig. 8. Misgolas wayorum n.sp. (A–D) Ƌ, holotype AM KS50047.
Fig. 7 in Trapdoor Spiders of the Genus Misgolas (Mygalomorphae: Idiopidae) in the Sydney Region, Australia, With Notes on Synonymies Attributed to M. rapax
Fig. 7. Misgolas maculosus. (A–C) ♀, AM KS69957. (A), tarsus
Replicating Attribute Amnesia effect in the single-stimulus design
<p>Attribute Amnesia refers to the phenomenon that subjects fail to report some features (e.g., colour) of a stimulus after they have been asked to repetitively report some other features (e.g., numeric parity) of the same stimulus. This effect has shown strong replicability in the multi-stimuli design, in which there is normally one target among three distractor stimuli. Recently, Wang et al. (2021) furtherly showed the robustness of this effect when there was only one stimulus on the screen. As the main experiment of the current project will be conducted online, we aim to pre-register a replication attempt of Experiment 1 of Wang et al. (2021) in the online-experiment setting. This study shows that one can indeed replicate previous in-lab experiments on attribute amnesia in the online setting, demonstrating convincing online data quality for serious investigations. The functions of Webcam-based eyetracking from Labvanced also make rigorous environmental control and recording saccadic data possible.</p>
AttackER: NER Attack Attribution
<p>The folder contains 8 files. The files with .spacy extension are the files that can be used to train models using spaCy and the .JSON files can be used to train NER models using Huggingface transformers. The new_model.zip contains the fine-tuned transformer model using the .spacy files that can be used for NER tasks on Cyber attack attribution. The spacy_run_script.ipynb file can be used to view the contents of the .spacy files as well as run the model inside the .zip file. The script contains the necessary guidelines for the same. Since NER tasks in Huggingface transformers requires a JSON format, this folder contains the necessary train, test and dev files in the .json format.</p>
Interrelationship between Microplastics, soil and soil attributes
<p>Database that was used for the investigation of the interrelationship between Microplastics, terrain and soil attributes in a sub-basin in the state of Minas Gerais, Brazil.</p>
Fig. 5. – Sheet number 39 in Study of a pre-Linnaean herbarium attributed to Francesco Cupani (1657-1710)
Fig. 5. – Sheet number 39 of the Cupani Herbarium containing one specimen of Rosa L. (Rosaceae).
Fig. 6. – Sheet number 17 in Study of a pre-Linnaean herbarium attributed to Francesco Cupani (1657-1710)
Fig. 6. – Sheet number 17 of the Cupani Herbarium.
Fig. 1 in Study of a pre-Linnaean herbarium attributed to Francesco Cupani (1657-1710)
Fig. 1. – Spine and inventory label of the Cupani Herbarium.
Fig. 3. – Sheet number 6 in Study of a pre-Linnaean herbarium attributed to Francesco Cupani (1657-1710)
Fig. 3. – Sheet number 6 of the Cupani Herbarium.
Fig. 4 in Study of a pre-Linnaean herbarium attributed to Francesco Cupani (1657-1710)
Fig. 4. – First page of the index of the Cupani Herbarium.
Fig. 7. – Sheet number 1 in Study of a pre-Linnaean herbarium attributed to Francesco Cupani (1657-1710)
Fig. 7. – Sheet number 1 of the Cupani Herbarium.
Fig. 2 in Study of a pre-Linnaean herbarium attributed to Francesco Cupani (1657-1710)
Fig. 2. – Title page of the Cupani Herbarium.
MetSys Node Attributes
<p>Cytoscape node attribute file for visualizing GO pathway maps (sif format) from online tool - metsys.idsl.site </p>
GeoDAR: Georeferenced global Dams And Reservoirs dataset for bridging attributes and geolocations
<p>Documented March 19, 2023</p> <p><strong>!!NEW!!!</strong></p> <p><strong>GeoDAR reservoirs were registered to the drainage network! </strong>Please see the auxiliary data "<a href="../records/7750736">GeoDAR-TopoCat</a>" at <a href="../records/7750736">https://zenodo.org/records/7750736</a>. "GeoDAR-TopoCat" contains the <strong>drainage topology</strong> (reaches and upstream/downstream relationships) and catchment boundary for each reservoir in GeoDAR, based on the algorithm used for Lake-TopoCat (doi:10.5194/essd-15-3483-2023).</p> <p> </p> <p>Documented April 1, 2022</p> <p><strong>Citation</strong></p> <p>Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, M. S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: georeferenced global dams and reservoirs database for bridging attributes and geolocations. Earth System Science Data, 14, 1869–1899, 2022, https://doi.org/10.5194/essd-14-1869-2022.</p> <p>Please cite the reference above (which was fully peer-reviewed), NOT the preprint version. Thank you.</p> <p> </p> <p><strong>Contact</strong></p> <p>Dr. Jida Wang, jidawang@ksu.edu, gdbruins@ucla.edu</p> <p> </p> <p><strong>Data description and components</strong></p> <p>Data folder “<strong>GeoDAR_v10_v11</strong>” (.zip) contains two consecutive, peer-reviewed versions (<strong>v1.0</strong> and <strong>v1.1</strong>) of the Georeferenced global Dams And Reservoirs (GeoDAR) dataset:</p> <ul> <li><strong>GeoDAR_v10_dams</strong> (in both shapefile format and the comma-separated values (csv) format): GeoDAR version 1.0, including 22,560 dam points georeferenced based on the World Register of Dams (WRD), the International Commission on Large Dams (ICOLD; <a href="https://www.icold-cigb.org">https://www.icold-cigb.org</a>; last access on March 13th, 2019).</li> <li><strong>GeoDAR_v11_dams</strong> (in both shapefile and csv): GeoDAR version 1.1 dam points, including 24,783 dams which harmonized GeoDAR_v10_dams and the Global Reservoir and Dam Database (GRanD) v1.3 (Lehner et al., 2011).</li> <li><strong>GeoDAR_v11_reservoirs</strong> (in shapefile): GeoDAR version 1.1 reservoirs, including 21,515 reservoir polygons retrieved by associating GeoDAR_v11_dams with GRanD v1.3 reservoirs, HydroLAKES v1.0 (Messager et al., 2016), and the UCLA Circa 2015 Lake Inventory (Sheng et al., 2016). The reservoir retrieval follows a one-to-one relationship between dams and reservoirs.</li> </ul> <p>As by-products of GeoDAR harmonization, folder “GeoDAR_v10_v11” also contains:</p> <ul> <li><strong>GRanD_v13_issues.csv</strong>: This file contains the original records of all 7,320 dam points in GRanD v1.3, with 94 of them marked by our identified issues and suggested corrections. These 94 records are placed at the beginning of this table. They include 89 records showing possible georeferencing and/or attribute errors, and another 5 records documented as subsumed or replaced. Our added fields start from column BG and include: <ul> <li>“Issue”: main issue(s) of this record</li> <li>“Description”: more detailed explanation of the issue</li> <li>“Lat_corrected”: suggested correction for latitude (if any) in decimal degree</li> <li>“Lon_corrected”: suggested correction for longitude (if any) in decimal degree</li> <li>“Correction_source”: correction source(s)</li> <li>“Harmonized”: whether this GRanD dam was harmonized in GeoDAR v1.1 and the reason.</li> </ul> </li> <li><strong>Wada_et_al_2017_harmonized.csv</strong>: This csv file contains the original records of all 139 georeferenced large dams/reservoirs in Wada et al. (2017; doi:10.1007/s10712-016-9399-6), with our revised storage capacities and spatial coordinates for data harmonization. Our added fields start from column E and include: <ul> <li>Revised_capacity_km3: Our revised reservoir storage capacity in cubic kilometers used for harmonization</li> <li>Revised_lat: Revised latitude in decimal degree</li> <li>Revised_lon: Revised longitude in decimal degree</li> <li>Verification_notes: Description of the issues, verification sources, and other information used for harmonization.</li> </ul> </li> </ul> <p> </p> <p><strong>Attribute description</strong></p> <table> <tbody> <tr> <td> <p><strong>Attribute</strong></p> </td> <td> <p><strong>Description and values</strong></p> </td> </tr> <tr> <td> <p>v1.0 dams (file name: GeoDAR_v10_dams; format: comma-separated values (csv) and point shapefile)</p> </td> </tr> <tr> <td> <p><em>id_v10</em></p> </td> <td> <p>Dam ID for GeoDAR version 1.0 (type: integer). Note this is not the same as the International Code in ICOLD WRD but is linked to the International Code via encryption.</p> </td> </tr> <tr> <td> <p><em>lat</em></p> </td> <td> <p>Latitude of the dam point in decimal degree (type: float) based on datum World Geodetic System (WGS) 1984.</p> </td> </tr> <tr> <td> <p><em>lon </em></p> </td> <td> <p>Longitude of the dam point in decimal degree (type: float) on WGS 1984.</p> </td> </tr> <tr> <td> <p><em>geo_mtd</em></p> </td> <td> <p>Georeferencing method (type: text). Unique values include “geo-matching CanVec”, “geo-matching LRD”, “geo-matching MARS”, “geo-matching NID”, “geo-matching ODC”, “geo-matching ODM”, “geo-matching RSB”, “geocoding (Google Maps)”, and “Wada et al. (2017)”. Refer to Table 2 in Wang et al. (2022) for abbreviations.</p> </td> </tr> <tr> <td> <p><em>qa_rank</em></p> </td> <td> <p>Quality assurance (QA) ranking (type: text). Unique values include “M1”, “M2”, “M3”, “C1”, “C2”, “C3”, “C4”, and “C5”. The QA ranking provides a general measure for our georeferencing quality. Refer to Supplementary Tables S1 and S3 in Wang et al. (2022) for more explanation.</p> </td> </tr> <tr> <td> <p><em>rv_mcm</em></p> </td> <td> <p>Reservoir storage capacity in million cubic meters (type: float). Values are only available for large dams in Wada et al. (2017). Capacity values of other WRD records are not released due to ICOLD’s proprietary restriction. Also see Table S4 in Wang et al. (2022).</p> </td> </tr> <tr> <td> <p><em>val_scn</em></p> </td> <td> <p>Validation result (type: text). Unique values include “correct”, “register”, “mismatch”, “misplacement”, and “Google Maps”. Refer to Table 4 in Wang et al. (2022) for explanation.</p> </td> </tr> <tr> <td> <p><em>val_src</em></p> </td> <td> <p>Primary validation source (type: text). Values include “CanVec”, “Google Maps”, “JDF”, “LRD”, “MARS”, “NID”, “NPCGIS”, “NRLD”, “ODC”, “ODM”, “RSB”, and “Wada et al. (2017)”. Refer to Table 2 in Wang et al. (2022) for abbreviations.</p> </td> </tr> <tr> <td> <p><em>qc</em></p> </td> <td> <p>Roles and name initials of co-authors/participants during data quality control (QC) and validation. Name initials are given to each assigned dam or region and are listed generally in chronological order for each role. Collation and harmonization of large dams in Wada et al. (2017) (see Table S4 in Wang et al. (2022)) were performed by JW, and this information is not repeated in the <em>qc</em> attribute for a reduced file size. Although we tried to track the name initials thoroughly, the lists may not be always exhaustive, and other undocumented adjustments and corrections were most likely performed by JW.</p> </td> </tr> <tr> <td> <p>v1.1 dams (file name: GeoDAR_v11_dams; format: comma-separated values (csv) and point shapefile)</p> </td> </tr> <tr> <td> <p><em>id_v11</em></p> </td> <td> <p>Dam ID for GeoDAR version 1.1 (type: integer). Note this is not the same as the International Code in ICOLD WRD but is linked to the International Code via encryption.</p> </td> </tr> <tr> <td> <p><em>id_v10</em></p> </td> <td> <p>v1.0 ID of this dam/reservoir (as in <em>id_v10</em>) if it is also included in v1.0 (type: integer).</p> </td> </tr> <tr> <td> <p><em>id_grd_v13</em></p> </td> <td> <p>GRanD ID of this dam if also included in GRanD v1.3 (type: integer).</p> </td> </tr> <tr> <td> <p><em>lat</em></p> </td> <td> <p>Latitude of the dam point in decimal degree (type: float) on WGS 1984. Value may be different from that in v1.0.</p> </td> </tr> <tr> <td> <p><em>lon </em></p> </td> <td> <p>Longitude of the dam point in decimal degree (type: float) on WGS 1984. Value may be different from that in v1.0.</p> </td> </tr> <tr> <td> <p><em>geo_mtd</em></p> </td> <td> <p>Same as the value of <em>geo_mtd</em> in v1.0 if this dam is included in v1.0.</p> </td> </tr> <tr> <td> <p><em>qa_rank</em></p> </td> <td> <p>Same as the value of <em>qa_rank</em> in v1.0 if this dam is included in v1.0.</p> </td> </tr> <tr> <td> <p><em>val_scn</em></p> </td> <td> <p>Same as the value of <em>val_scn</em> in v1.0 if this dam is included in v1.0.</p> </td> </tr> <tr> <td> <p><em>val_src</em></p> </td> <td> <p>Same as the value of <em>val_src</em> in v1.0 if this dam is included in v1.0.</p> </td> </tr> <tr> <td> <p><em>rv_mcm_v10</em></p> </td> <td> <p>Same as the value of <em>rv_mcm </em>in v1.0 if this dam is included in v1.0.</p> </td> </tr> <tr> <td> <p><em>rv_mcm_v11</em></p> </td> <td> <p>Reservoir storage capacity in million cubic meters (type: float). Due to ICOLD’s proprietary restriction, provided values are limited to dams in Wada et al. (2017) and GRanD v1.3. If a dam is in both Wada et al. (2017) and GRanD v1.3, the value from the latter (if valid) takes precedence.</p> </td> </tr> <tr> <td> <p><em>har_src</em></p> </td> <td> <p>Source(s) to harmonize the dam points. Unique values include “GeoDAR v1.0 alone”, “GRanD v1.3 and GeoDAR 1.0”, “GRanD v1.3 and other ICOLD”, and “GRanD v1.3 alone”. Refer to Table 1 in Wang et al. (2022) for more details.</p> </td> </tr> <tr> <td> <p><em>pnt_src</em></p> </td> <td> <p>Source(s) of the dam point spatial coordinates. Unique values include “GeoDAR v1.0”, “original GRanD”, “adjusted GRanD” (meaning the original dam point location in GRanD has been adjusted to improve the accuracy), and “corrected GRanD” (meaning the original point in GRanD was misplaced and has been corrected). Also see Table S5 in Wang et al. (2022).</p> </td> </tr> <tr> <td> <p><em>qc</em></p> </td> <td> <p>Roles and name initials of co-authors/participants during data QC, validation, and other manual operations. Name initials are given to each assigned dam or region and are listed generally in chronological order for each role. Correction of GRanD (see Table S5 in Wang et al. (2022)) and reservoir polygon QC were performed by JW, and this information is not repeated in the <em>qc</em> attribute to reduce the file size. Although we tried to track the name initials thoroughly, the lists may not be always exhaustive, and other undocumented adjustments and corrections were most likely performed by JW.</p> </td> </tr> <tr> <td> <p>v1.1 reservoirs (file name: GeoDAR_v11_reservoirs; format: polygon shapefile)</p> </td> </tr> <tr> <td> <p><em>plg_src</em></p> </td> <td> <p>Source of the retrieved reservoir polygon (type: text). Unique values include “GRanD v1.3”, “HydroLAKES v1.0”, and “UCLA Circa 2015”. Refer to Table 1 in Wang et al. (2022) for more details.</p> </td> </tr> <tr> <td> <p><em>plg_a_km2</em></p> </td> <td> <p>Area of the retrieved reservoir polygon in square kilometres (calculated based on the cylindrical equal area projection on datum WGS 1984).</p> </td> </tr> <tr> <td> <p><em>All other attributes in v1.1 dams.</em></p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Data and code availability</strong></p> <p>GeoDAR v1.0 (dam points) and v1.1 (both dam points and reservoir polygons) are available under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license (<a href="https://creativecommons.org/licenses/by/4.0">https://creativecommons.org/licenses/by/4.0</a>). </p> <p>Any user who would like to link GeoDAR features to the proprietary WRD attributes the user has purchased in advance from ICOLD should contact the corresponding author JW.</p> <p>Python scripts for geo-matching, geocoding, and reservoir assignment are available at <a href="https://github.com/surf-hydro/georeferencing-ICOLD-dams-and-reservoirs">https://github.com/surf-hydro/georeferencing-ICOLD-dams-and-reservoirs</a>. We request users who adapt or use the scripts to cite Wang et al. (2022).</p> <p>We also request users to cite Wang et al. (2022) if they use our identified issues or suggested corrections for GRanD v1.3 (as provided in “GRanD_v13_issues.csv”).</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>GeoDAR v1.0 and v1.1 contain knowledge derived from ICOLD WRD (<a href="https://www.icold-cigb.org/GB/world_register/acknowledgements_wrd.asp">https://www.icold-cigb.org/GB/world_register/acknowledgements_wrd.asp</a>) but release no original values of the proprietary WRD attributes (except the storage capacities of a few large dams used to verify/correct Wada et al. (2017); see Table S4 in Wang et al. (2022)). The production and dissemination of GeoDAR abide by ICOLD’s legal policies (<a href="https://www.icold-cigb.org/GB/legal.asp">https://www.icold-cigb.org/GB/legal.asp</a>) and were approved by ICOLD’s Central Office.</p> <p>GeoDAR v1.0 represents an initial effort of georeferencing WRD at the global scale. The resultant dam distribution may be skewed towards regions where georeferencing sources are more abundant, and therefore, may not accurately reflect the distribution of all WRD records. The authors are not responsible for any consequence arising from this limitation.</p> <p>GeoDAR v1.1 absorbed most of the spatial features (i.e., dam point coordinates and reservoir polygons) in GRanD v1.3. To acknowledge the originality of GRanD, we request users to cite Lehner et al. (2011) if they only use the subset of GeoDAR v1.1 from GRanD alone. If the user adopts the spatial coordinates we corrected for GRanD (see “GRanD_v13_issues.csv”), we recommend users citing Wang et al. (2022) as well.</p> <p>The source of each spatial feature in GeoDAR v1.1 is specified in the attributes “har_src” and “pnt_src” for dam points and the attribute “plg_src” for reservoir polygons. For any questions about data citation, please contact the corresponding author JW.</p> <p>Authors of this paper claim no responsibility or liability for any consequences related to the use, citation, or dissemination of GeoDAR.</p> <p> </p> <p><strong>Other notes</strong></p> <p>We provide another auxiliary folder “<strong>GeoDAR_beta_peer_review</strong>” (.zip), which stores the versions of GeoDAR before the completion of peer review with ESSD. We here keep these earlier GeoDAR versions on file, but since improvements and corrections were made during the peer review process, we do NOT recommend any application of these earlier versions. Instead, please use the fully peer-reviewed versions in folder “<strong>GeoDAR_v10_v11</strong>”. </p> <p>Please also see the readme files in each of the folders. </p>
Supp. Info. for Further analysis of metagenomic datasets containing GD and GX pangolin CoVs indicates widespread contamination, undermining pangolin host attribution
<p>Supplemanty Information for <strong>Further analysis of metagenomic datasets containing GD and GX pangolin CoVs indicates widespread contamination, undermining pangolin host attribution</strong></p> <p>Files:</p> <p>Supp_Info_1_PRJNA641544_DG14_DG18.xlsx</p> <p>Supp_Info_3_PRJNA606875_SRR11093270_reads_blast_nt_seq5_hsps1_PCT80_E0.05_hsps.txt</p> <p>Supp_Info_4_PRJNA573298_Analysis.xlsx</p>
BeBOD estimates of attributable mortality and years of life lost due to tobacco use, 2013-2020
<h1>Belgian National Burden of Disease Study</h1> <h2>Estimates of the attributable burden of disease<strong><br></strong></h2> <p>Our results build on the estimates of causes of death from the <a href="https://www.sciensano.be/en/projects/belgian-national-burden-disease-study" target="_blank" rel="noopener">Belgian National Burden of Disease Study</a> and apply comparative risk assessment to estimate the proportion of that burden due to risk factors.</p> <h3>Population attributable fraction</h3> <p>Based on the estimated level of exposure and the associated health risk, a <strong>population attributable fraction (PAF)</strong> is calculated. The PAF is the proportion of the disease burden that is caused by exposure to the risk factor.</p> <h3>Attributable burden</h3> <p>The burden of disease is quantified in <strong>deaths</strong> and in <strong>Years of Life Lost (YLL)</strong>. To derive the burden attributable to a risk factor, its PAF is multiplied with the total observed burden.</p> <h2>Risk factors</h2> <h3>Tobacco use</h3> <p>The prevalence of never, former and current smokers, as well as the daily number of cigarettes smoked by smokers, is derived from the Belgian Health Interview Survey. Bayesian regression models are used to smoothen, interpolate and extrapolate observed estimates.</p> <h2>More information</h2> <p>For additional background on BeBOD, please visit <a href="https://www.sciensano.be/en/projects/belgian-national-burden-disease-study">https://www.sciensano.be/en/projects/belgian-national-burden-disease-study</a>.</p> <p>Explore the estimates via <a href="https://burden.sciensano.be/shiny/risk">https://burden.sciensano.be/shiny/risk</a>.</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.