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11,718 results for “life”
Proteins required for stereocilia elongation during mammalian hair cell development ensure precise and steady heights during adult life
<p>This dataset contains all source data for Hartig <em>et al </em>2024, PNAS, including:</p> <p>Data files</p> <p>Raw images and TDT ABR/DPOAE files</p> <p>ROIS and raw measurements from quantifications in ImageJ</p> <p>R scripts for data visualization and statistics</p> <p>Reports of statistical analyses including diagnostic qq plots and distributions</p>
Life Cycle Impact Assessment method for ozone depletion based on WMO 2022
<p>This dataset provides the most recent <a>characterization factors</a> for ozone depletion based on the latest ozone depletion potentials from the 2022 World Meteorological Organization (WMO) scientific assessment. The dataset is formatted for easy import into life cycle assessment (LCA) software such as Brightway, the Activity Browser, and SimaPro. The characterization factors are available for both 100-year and infinite time horizons.</p> <p>When using the dataset, please cite the folllowing publication:</p> <p>van den Oever, A. E.M., Puricelli, S., Costa, D., Thonemann, N., Lavigne Philippot, M., Messagie, M., Dataset with updated ozone depletion characterization factors for life cycle impact assessment, Data in Brief (in press), 2024, <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.111103" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dib.2024.111103</a></p>
Kinetochore life histories reveal an Aurora B dependent error correction mechanism in anaphase
<p>Dataset of kinetochore tracks in human RPE1 cells showing chromosome dynamics and segregation from prometaphase through to anaphase as described in detail in Sen, Harrison, Burroughs and McAinsh, 2021, https://doi.org/10.1101/2021.03.30.436326 Tracks correspond to 3D time-lapse movies of Ndc80-eGFP and were acquired in the 488nm channel using 1\% laser power, 50 ms exposure time/z-plane, 93 z-planes, 307 nm z-step, which results in 4.7 s/z-stack time frame. Cells are subject to nocodazole arrest-and-release or equivalent treatment with DMSO as indicated in the folder names, and some cells are subject to additional treatment with ZM to inhibit Aurora B (also indicated in folder names). Tracks were produced using kinetochore tracking software, KiT v2.3 (see Armond et al., 2016, Bioinformatics), available from https://github.com/cmcb-warwick/KiT/ </p>
Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse
<p>Datasets and R source code of the article Depeux C, Branger A, Moulignier T, Moreau J, Lemaître J-F, Dechaume-Moncharmont F-X, Laverre T, Paulhac H, Gaillard J-M, Beltran-Bech S (2023) Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse. <strong><em>Peer Community Journal</em></strong> 3:e7 http://dx.doi.org/<a href="https://doi.org/10.24072/pcjournal.228">10.24072/pcjournal.228</a></p> <p>This article previously appeared as preprint Depeux C, Branger A, Moulignier T, Moreau J, Lemaître J-F, Dechaume-Moncharmont F-X, Laverre T, Pauhlac H, Gaillard J-M, Beltran-Bech S (2022) Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse. <em><strong>bioRxiv</strong>, 2022.09.26.509512 </em> https://doi.org/10.1101/2022.09.26.509512</p> <p><em>Peer-reviewed and recommended by <strong>Peer Community in Ecology</strong>: </em> Belsare A (2022) An experimental approach for understanding how terrestrial isopods respond to environmental stressors. <em>Peer Community in Ecology, 100506. </em><a href="https://doi.org/10.24072/pci.ecology.100506"><strong>https://doi.org/10.24072/pci.ecology.100506</strong></a></p>
Synthetic Multimodal Dataset for Daily Life Activities
<p><strong>Outline</strong></p> <ul> <li>This dataset is originally created for the <a href="https://challenge.knowledge-graph.jp/2022/">Knowledge Graph Reasoning Challenge for Social Issue</a>s (KGRC4SI)</li> <li>Video data that simulates daily life actions in a virtual space from Scenario Data.</li> <li>Knowledge graphs, and transcriptions of the Video Data content ("who" did what "action" with what "object," when and where, and the resulting "state" or "position" of the object).</li> <li>Knowledge Graph Embedding Data are created for reasoning based on machine learning </li> <li>This data is open to the public as open data</li> </ul> <p><strong>Details</strong></p> <ul> <li> <p><a href="https://github.com/KnowledgeGraphJapan/KGRC-RDF/blob/kgrc4si/Movie">Videos</a></p> <ul> <li>mp4 format</li> <li>203 action scenarios</li> <li>For each scenario, there is a character rear view (file name ending in 0), an indoor camera switching view (file name ending in 1), and a fixed camera view placed in each corner of the room (file name ending in 2-5). Also, for each action scenario, data was generated for a minimum of 1 to a maximum of 7 patterns with different room layouts (scenes). A total of 1,218 videos</li> <li>Videos with slowly moving characters simulate the movements of elderly people.</li> </ul> </li> <li> <p><a href="https://github.com/KnowledgeGraphJapan/KGRC-RDF/blob/kgrc4si/RDF">Knowledge Graphs</a></p> <ul> <li>RDF format</li> <li>203 knowledge graphs corresponding to the videos</li> <li>Includes schema and location supplement information</li> <li>The schema is described below</li> <li><a href="http://kgrc4si.ml:7200/sparql">SPARQL endpoints</a> and <a href="https://github.com/KnowledgeGraphJapan/KGRC-RDF/tree/kgrc4si#%E3%83%8A%E3%83%AC%E3%83%83%E3%82%B8%E3%82%B0%E3%83%A9%E3%83%95%E3%81%AE%E4%BD%BF%E7%94%A8%E6%96%B9%E6%B3%95">query examples</a> are available</li> </ul> </li> <li> <p><a href="https://github.com/KnowledgeGraphJapan/KGRC-RDF/blob/kgrc4si/Program">Script Data</a></p> <ul> <li>txt format</li> <li>Data provided to VirtualHome2KG to generate videos and knowledge graphs</li> <li>Includes the action title and a brief description in text format.</li> </ul> </li> <li>Embedding <ul> <li>Embedding Vectors in TransE, ComplEx, and RotatE. Created with DGL-KE (<a href="https://dglke.dgl.ai/doc/">https://dglke.dgl.ai/doc/</a>)</li> <li>Embedding Vectors created with jRDF2vec (<a href="https://github.com/dwslab/jRDF2Vec">https://github.com/dwslab/jRDF2Vec</a>).</li> </ul> </li> </ul> <p><strong>Specification of Ontology</strong></p> <ul> <li>Please refer to the specification for descriptions of all classes, instances, and properties: <a href="https://aistairc.github.io/VirtualHome2KG/vh2kg_ontology.html">https://aistairc.github.io/VirtualHome2KG/vh2kg_ontology.htm</a></li> </ul> <p><strong>Related Resources</strong></p> <ul> <li><a href="https://www.youtube.com/watch?v=Ajbn8hNXiZ8&list=PLHaRK-B0LUwjvrPgmIBTrf3DsPhmdnFTW">KGRC4SI Final Presentations with automatic English subtitles (YouTube)</a></li> <li><a href="https://github.com/aistairc/VirtualHome2KG">VirtualHome2KG (Software)</a></li> <li><a href="https://github.com/aistairc/virtualhome_unity_aist">VirtualHome-AIST (Unity</a>)</li> <li><a href="https://github.com/aistairc/virtualhome_aist">VirtualHome-AIST (Python API</a>)</li> <li><a href="https://github.com/aistairc/virtualhome2kg_visualization">Visualization Tool</a> (Software)</li> <li><a href="https://github.com/aistairc/virtualhome2kg_generation">Script Editor</a> (Software)</li> </ul>
MCR LTER: Coral Reef: Early life stage bottleneck determines rates of coral recovery following severe disturbance; Data for Speare et al., 2024, Ecology
The data included in this data package were collected on the north shore of Moorea, French Polynesia, from 2011-2018 to evaluate drivers of different recovery rates of corals at two depths (10m and 17m). Data on juvenile coral densities, growth, and mortality, were collected from annual time series photoquadrats. Data from two experiments on coral settlement tiles were used to evaluate how exclusion of fishes influences the density of coral recruits, and the survival of coral recruits at 10 and 17m. These data were used for analyses in the manuscript entitled "Early life stage bottleneck determines rates of coral recovery following severe disturbance". These data are in support of a publication Speare et al. (2024) Ecology. This material uses data collected by the U.S. National Science Foundation's (NSF) Moorea Coral Reef Long Term Ecological Research (MCR LTER) site under Grant No. OCE 2224354 (and earlier awards). Additional financial support to the MCR LTER site was provided through 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 2005-2024).
SSARA A-LIFE measurement data
<p>This dataset contains the measurement data of the SSARA-P polarized sun and sky photometer during the A-LIFE field campaign in Cyprus during April 2017.</p> <p>The data is provided in several stages of preprocessing:</p> <ul> <li><em>L0</em>: raw instrument data</li> <li><em>L1</em>: calibrated measurements</li> <li><em>L2</em>: Aerosol Optical Thickness (AOT) derived from direct sun measurements</li> </ul> <p>The datasets are also split for three different observation geometries:</p> <ul> <li><em>direct</em>: direct sun observation</li> <li><em>almuc</em>: almucantar geometry (scan at solar elevation)</li> <li><em>pplane</em>: principle plane scan (hemispheric scan through sun and zenith)</li> </ul>
The Research Life Cycle
<p>A diagram of the Research Life Cycle as used for communication by the Vrije Universiteit (VU) Amsterdam.</p> <p>This diagram was partly inspired by a diagram by <a href="https://www.jisc.ac.uk/guides/research-data-management">JISC and Bonner McHardy</a> that was released under a <a href="http://creativecommons.org/licenses/by-nc-nd/3.0">CC BY-NC-ND</a> licence.</p>
The Daily Life of Software Engineers during the COVID-19 Pandemic -- Replication Package
<p>Following the onset of the COVID-19 pandemic and subsequent lockdowns, software engineers' daily life was disrupted and abruptly forced into remote working from home. This change deeply impacted typical working routines, affecting both well-being and productivity. Moreover, this pandemic will have long-lasting effects in the software industry, with several tech companies allowing their employees to work from home indefinitely if they wish to do so. Therefore, it is crucial to analyze and understand how a typical working day looks like when working from home and how individual activities affect software developers' well-being and productivity. We performed a two-wave longitudinal study involving almost 200 globally carefully selected software professionals, inferring daily activities with perceived well-being, productivity, and other relevant psychological and social variables. Results suggest that the time software engineers spent doing specific activities from home was similar when working in the office. (e.g., coding > emails > code review > networking). However, we also found some meaningful mean differences. The amount of time developers spent on each activity was unrelated to their well-being, perceived productivity, and other variables. We conclude that working remotely is not per se a challenge for organizations or developers.</p>
Practices and policies of preprint platforms for life and biomedical sciences
<p>Given the increase in the use and profile of preprint servers – and alternative publishing hybrid platforms such as F1000 Research – in the life sciences, it is increasingly important to identify how many such servers and hybrids exist, to describe their scope in terms of the scientific disciplines they cover, and to compare and contrast their characteristics and policies.</p> <p>We surveyed forty-four (44) platforms that host preprints relevant to life and biomedical sciences and that were active online and accepting submissions on 25 June 2019. Information on preprint platform policies, features and practices was collected through online research by the authors and by surveying preprint platform representatives directly. </p> <p>Full data sheets include an additional 5 platforms hosted on OSF Preprints (rows 49-53) to fulfil the wider scope for the ASAPbio project, not in disciplinary scope (biology and medical sciences) for the manuscript with Jamie Kirkham.</p> <p><strong>Tables 1-5: </strong>Data (44 platforms, manuscript) are separated into five main tables of information and a list of preprint platform websites for reference.</p> <p>Table 1: Scope and ownership of each server<br> Table 2: Content-specific characteristics and information relating to submission, journal transfer options, and external discoverability<br> Table 3: Screening, moderation, and permanence of content<br> Table 4: Usage metrics and other features<br> Table 5: Metadata<br> Preprint platform websites</p> <p>Data for each platform are listed as ‘Verified’ in the tables if these tables (V1.0 or V2.0) were seen and approved by a platform representative between January 13 and January 27, 2020.</p> <p><strong>Original online survey:</strong> a blank copy of the original survey form used by online researchers (the authors) and supplied pre-filled (or empty, in some cases) to preprint platform representatives for verification (or completion, in some cases). </p> <p><strong>Final data:</strong> survey data is presented in .txt and .xlsx, as follows:</p> <ul> <li>Row 1: Heading (where field is included in manuscript tables, the heading presented here replaces any heading used in original survey. All columns are presented in the order the information was requested on the original survey form, with some supplementary columns added and columns removed (detailed below).</li> <li>Row 2: Schema or description of field</li> <li>Row 3: Whether and where included in manuscript tables. For supporting information for table data (e.g. source information, URLs), the table location for supported data is indicated in brackets, e.g. (Table 2) and supporting information is not included in tables. Data included in manuscript tables is presented in its final form, which in some cases is simplified from the original survey data. This simplified version of the data was presented to platform representatives for additional verification (v1.0/v2.0 verification). Data not included in manuscript tables is presented here as verified by platform representatives and/or found online. Some columns from the original survey have been removed due to the information not being informative or useful: specifically, Print ISSN (not reported for any platform); End date (no platforms have an end date; although two platforms stopped accepting submissions after survey completed; Personal contact information for platform representative(s) has been removed).</li> <li>Rows 4 onwards: data for each preprint platform (44 included in manuscript (rows 4-47), plus 5 additional OSF platforms (rows 48-52)</li> <li>Columns 3-6 (D-G) report online research and verification information and Column 13 (M) reports an additional data field (number of articles) – these are supplementary to the original survey columns</li> <li>Verification status: Released V1/V2 data applies to data included in manuscript tables (as indicated in row 3); Online survey data applies to data used for manuscript tables and also to original survey data included here but not included in manuscript tables (‘Not included’ in row 3)</li> <li>Note that data fields are presented as individual columns in these sheets, while some entries in Tables 1-5 combine several data fields.</li> </ul> <p>These data were collected in collaboration and as part of:<br> i. An ASAPbio project, led by Dr Naomi Penfold, to develop an online directory of preprint platforms<br> ii. A research project led by Prof Jamie Kirkham<br> These data are supplementary outputs for both projects.</p> <p>Data v1.0 were presented during the ASAPbio January 2020 workshop – see Penfold, Naomi C, & Polka, Jessica. (2020, January). ASAPbio Preprint Platform Directory: 2019 data (presentation) (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.3626770.<br> <br> <strong>Version 3.0 updates (December 14, 2020): added new files with updated information about servers from the ASAPbio preprint directory (https://asapbio.org/preprint-servers), provided by Jessica Polka (now included as author).</strong></p>
Research Data Life cycle
<p>Research Life cycle Headings & Key Points.</p> <p>1- Planning:</p> <ul> <li>Data management planning (DMPs)</li> <li>Data description and metadata extraction</li> <li>Data documentation</li> <li>Choice of repositories</li> <li>Choices of file formats</li> <li>Data re-use</li> <li>Funders requirements</li> <li>File naming</li> <li>Ethics and Research conduct</li> <li>Funding for RDM activities</li> </ul> <p>2- Managing:</p> <ul> <li>Storage and backup & security</li> <li>Active Metadata collection</li> <li>Tools and software solutions</li> <li>Curation</li> <li>Versioning</li> <li>Provenance</li> </ul> <p>3- Sharing</p> <ul> <li>Data access and Sharing rights</li> <li>Data privacy and GDPR compliance</li> <li>Data ownership, licensing</li> <li>Data Transfer</li> <li>GDPR</li> </ul> <p>4- Preservation and Publication</p> <ul> <li>Citation</li> <li>PrePrint</li> <li>DOI</li> <li>Publishing requirements</li> <li>Long Term Storage</li> <li>Archival and Disposal policies</li> </ul>
Social networks predict the life and death of honey bees - Data
<p><strong>Interaction matrices and metadata used in "Social networks predict the life and death of honey bees"</strong></p> <p><a href="https://www.biorxiv.org/content/10.1101/2020.05.06.076943v2">Preprint: Social networks predict the life and death of honey bees</a></p> <p>See the README file in <a href="https://doi.org/10.5281/zenodo.4435058">bb_network_decomposition</a> for example code.</p> <p><strong>The following files are included:</strong></p> <p><strong>interaction_networks_20160729to20160827.h5</strong></p> <p>The social interaction networks as a dense tensor and metadata.</p> <p>Keys:</p> <ul> <li>interactions: Tensor of shape (29, 2010, 2010, 9) (days x individuals x individuals x interaction_types). I_{d,i,j,t} = log(1 + x), where x is the number of interactions of type t between individuals i and j at recording day d. See the methods section of paper of the interaction types.</li> <li>labels: Names of the 9 interaction types in the order they are stored in the interactions tensor.</li> <li>bee_ids: List of length 2010, mapping from sequential index used in the interaction tensor to the original BeesBook tag ID of the individual</li> </ul> <p><strong>alive_bees_bayesian.csv </strong></p> <p>This file contains the results of the bayesian lifetime model with one row for each bee.</p> <p>Columns:</p> <ul> <li>bee_id: Numerical unique identifier for each individual.</li> <li>days_alive: Number of bees the bees was determined to be alive. If the individual was still alive at the end of the recording, the number of days from the day she hatched until the end of the recording.</li> <li>death_observed: Boolean indicator whether the death occurred during the recording period.</li> <li>annotated_tagged_date: Hatch date of the individual, i.e. the date she was tagged.</li> <li>inferred_death_date: The death date as determined by the model.</li> </ul> <p><strong>bee_daily_data.csv</strong></p> <p>This file contains one row per bee per day that she was alive for the focal period.</p> <p>Columns:</p> <ul> <li>bee_id: Numerical unique identifier for each individual.</li> <li>date: Date in year-month-day format.</li> <li>age: Age in days. Can be NaN if the bee has no associated death_date.</li> <li>network_age, network_age_1, network_age_2: The first three dimensions of network age.</li> <li>dance_floor, honey_storage, near_exit, brood_area_total: Normalized (sum to 1). Can be NaN if a bee had no high confidence detections (>0.9) for a given day. Can be 0 if a bee was only seen outside of the annotated areas.</li> <li>location_descriptor_count: The number of minutes the bee was seen in one of the location labels during that day. I.e., dance_floor * location_descriptor_count calculates the number of minutes, the bee was seen on the dance floor on the given day.</li> <li>death_date: Date the bee was last seen in the colony in year-month-day format. Can be NaN for individuals that did not die until the end of the recording period.</li> <li>circadian_rhythm: R² value of a sine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</li> <li>velocity_peak_time: Phase of the circadian sine fit in hours as an offset to 12:00 UTC. Can be NaN if circadian_rhythm is NaN.</li> <li>velocity_day, velocity_night: Mean velocity of the individual between 09:00-18:00 UTC and 21:00-06:00 UTC, respectively. Can be NaN if no velocity data was available for that interval.</li> <li>days_left: Difference in days between date and death_date. Can be NaN if death_date is NaN.</li> </ul> <p><strong>location_data.csv</strong></p> <p>This file contains subsampled position information for all bees during the focal period. The data contains one row for every individual for every minute of the recording if that individual was seen at least once during that minute with a tag confidence of at least 0.9. The first matching detection for each individual is used.</p> <p>Columns:</p> <p>In addition to the bee_id and date columns as in the bee_daily_data.csv, the file contains these additional columns:</p> <ul> <li>cam_id, cams: The cam_id is a numerical identifier from {0, 1, 2, 3}. Each side of the hive is filmed by two cameras where {0, 1} and {2, 3} record the same side respectively. The cams column contains values either “(0, 1)” or “(2, 3)” and indicates to which sides of the hive this detection belongs.</li> <li>x_pos_hive, y_pos_hive: The spatial positions in millimeters on the hive. The two cameras from one side share a common coordinate system.</li> <li>location: The label that was assigned to the comb at (x_pos_hive, y_pos_hive) on the given date. The label “other” indicates detections that were outside of any annotated region. The label “not_comb” indicates the wooden frame or empty space around the comb.</li> <li>timestamp, date: The timestamp indicates the beginning of each one-minute sampling interval and is given in UTC, as indicated (example: “2016-08-13 00:00:00+00:00”). The date part of the timestamp is repeated in the “date” column. Both are given in year-month-day format.</li> </ul> <p><strong>Software used to acquire and analyze the data:</strong></p> <ul> <li><a href="https://doi.org/10.5281/zenodo.4435058">bb_network_decomposition: Network age calculation and regression analyses</a></li> <li><a href="https://github.com/BioroboticsLab/bb_pipeline/releases/tag/2016">bb_pipeline: Tag localization and decoding pipeline</a></li> <li><a href="https://github.com/BioroboticsLab/bb_pipeline_models/releases/tag/2016">bb_pipeline_models: Pretrained localizer and decoder models for bb_pipeline</a></li> <li><a href="https://github.com/BioroboticsLab/bb_binary/releases/tag/2016">bb_binary: Raw detection data storage format</a></li> <li><a href="https://doi.org/10.5281/zenodo.4436419">bb_irflash: IR flash system schematics and arduino code</a></li> <li><a href="https://github.com/BioroboticsLab/bb_imgacquisition/releases/tag/2016">bb_imgacquisition: Recording and network storage </a></li> <li><a href="https://github.com/BioroboticsLab/bb_behavior/releases/tag/2016">bb_behavior: Database interaction and data (pre)processing, velocity calculation</a></li> <li><a href="https://github.com/BioroboticsLab/bb_circadian/releases/tag/2016">bb_circadian: Circadian rhythm calculations</a></li> <li><a href="https://github.com/BioroboticsLab/bb_tracking_2016/releases/tag/2016">bb_tracking: Tracking of bee detections over time</a></li> <li><a href="https://github.com/BioroboticsLab/bb_wdd/releases/tag/2016">bb_wdd: Automatic detection and decoding of honey bee waggle dances</a></li> <li><a href="https://github.com/BioroboticsLab/bb_interval_determination/releases/tag/2016">bb_interval_determination: Homography calculation</a></li> <li><a href="https://github.com/BioroboticsLab/bb_stitcher/releases/tag/2016">bb_stitcher: Image stitching</a></li> </ul> <p> </p>
Potential effects of invasive plants on mosquito life-history traits.
<p>Invasive plants offer suitable oviposition sites for some vector species (a); invasive plant litter increases proliferation of immature vectors (b); dense canopy cover or thickets of invasive plants provide suitable micro-habitats for adult mosquitoes (c); nectariferous flowers (d) and extra-floral glands (e) of invasive plants are important sugar sources for adult vectors; invasive plants can influence the pathogen transmission ability of the vector (f).</p> <p>A grey-scaled version was published as Figure 1 in <a href="https://doi.org/10.3390/v13010032">Agha et al. (2020)</a>.</p> <p>Required software: <a href="https://krita.org/">Krita</a> and <a href="https://www.gimp.org/">Gimp</a>.</p>
Identification at local and global scale: a case for using the Compact URI (CURIE) for life science data
<p>Panel A) A Local Resource Identifier (LRI) is not suited to global scale identification because of inevitable collisions: “9606” corresponds to a Pubmed article, a CGNC gene, a PubChem chemical, as well as an NCBI taxon (<em>Homo sapiens</em>), a BOLD taxon (<em>Bombycilla</em> <em>cedrorum</em>), and a GRIN taxon (<em>Catha</em> <em>edulis</em>)</p> <p>Panel B) Prefixing is often used to indicate the source of an LRI, but prefixes themselves are often undocumented and collide.</p> <p>Panel C) Prefixes may exist in alternate forms. When all of the alternates are not known, collapsing equivalent identifiers is tedious and incomplete.</p> <p>Panel D) CURIE syntax addresses these issues by having a prefix whose relationship with a resolving namespace is clearly documented.</p>
Real-life instances of a non-commercial indoor football league
<p>This repository accompanies the paper 'Scheduling a Non-Commercial Indoor Football League: a Tabu Search Based Approach' (Van Bulck, Goossens, Spieksma (2017)). More specifically, it stores all input instances and the generated schedules.</p>
Product Images of Life Cycle Assessment Dataset For Peritoneal Dialysis in Madrid, Spain
<p>The database contains a collection of images showcasing the individual components of peritoneal dialysis (PD) products, along with their corresponding weights. These images serve as a visual record for life cycle assessment (LCA) purposes, focusing on the material composition and environmental impact of each product.</p> <ol> <li> <p><strong>Patient Education Materials</strong>: Photographs of educational materials provided to patients, with accompanying data on the weight of the paper and packaging.</p> </li> <li> <p><strong>Catheters and Surgical Kits</strong>: Images display the disassembled components of PD catheters and surgical kits, including tubing, connectors, and packaging. Each image is annotated with the precise weight of the individual components.</p> </li> <li> <p><strong>Dialysis Solution Bags</strong>: The database includes images of both CAPD and APD solution bags, separated into their constituent parts (e.g., plastic bag, solution, and protective wrapping), with weights noted for each component.</p> </li> <li> <p><strong>Connection Devices and Consumables</strong>: Detailed images of connection devices, clamps, and other consumable items, with individual component weights clearly labeled.</p> </li> <li> <p><strong>Packaging and Transport Materials</strong>: Photographs of transport packaging, such as cardboard boxes and plastic wraps, alongside recorded weights for each element.</p> </li> <li> <p><strong>Maintenance Items</strong>: Visuals of terminal catheter sets, cleaning agents, and related products, each accompanied by their respective weight data.</p> </li> <li> <p><strong>Disposal Components</strong>: Images of used solution bags, syringes, and other single-use items, separated into recyclable and non-recyclable components, with weights specified for each.</p> </li> </ol> <p>This image-based database provides a clear and comprehensive reference for the material breakdown and weight distribution of PD product components, essential for conducting a thorough LCA and identifying areas for environmental improvement.</p>
A CO2 valorization plant to produce light hydrocarbons: kinetic model, process design and life cycle assessment
<p>Supplementary material: Reaction indexes, Conservation equations, boundary conditions and used coefficients. Additional experimental results, Experimental data fitting, Stream properties and composition of the CO2 plant, Life Cycle Assessment indicators, assumptions and data input </p>
BeBOD estimates of mortality, years of life lost, prevalence, years lived with disability, and disability-adjusted life years for 38 causes, 2013-2020
<p><strong>Belgian National Burden of Disease Study</strong></p> <p><strong>Estimates of the burden of disease</strong></p> <p><em>Causes of death</em></p> <p>Our estimates are based on the official causes of death database compiled by <a href="https://statbel.fgov.be/en/themes/population/mortality-life-expectancy-and-causes-death/causes-death">Statbel</a>. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.</p> <p><em>Years of Life Lost</em></p> <p>In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.</p> <p><em>Prevalence</em></p> <p>Our estimates are based on the GBD cause list for morbidity by <a href="https://www.healthdata.org/">IHME</a>. We first select for each of the 38 causes, the most suitable local data source as described in the <a href="https://www.sciensano.be/en/biblio/belgian-national-burden-disease-study-guidelines-calculation-dalys-belgium-2">protocol</a>. Next, we calculate the prevalence by year, region, age, and sex, to obtain a prevalence for each of the included diseases.</p> <p><em>Years Lived with Disability</em></p> <p>In addition to calculating the number of prevalent cases, we also calculate Years Lived with Disability (YLDs) as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.</p> <p><em>Disability-Adjusted Life Years</em></p> <p>Disability-Adjusted Life Years (DALYs) are a measure of overall disease burden, representing the healthy life years lost due to morbidity and mortality. DALYs are calculated as the sum of YLLs and YLDs for each of the considered diseases.</p>
Estimated life-cycle-based environmental indicators and social indicators for companies and investment funds
<p>The data files represent the 26 estimated life-cycle-based indicators for a sample of companies and funds, obtained using the methodology described in the linked journal article. The files SD1 and SD2 contain the individual values estimated for the fund and company samples. These estimates are based on the methodology described in the linked article. The data herein is the source for producing all figures of the paper. All companies and funds have been anonymized, as the data is sourced from proprietary databases. At the same link, supplementary file SD3 contains the summary statistics and comparison of sustainable funds versus conventional funds sample. The file SD4 contains the data used to create Figure 4. The file SD5 contains sample data to create Figure 5. The file SD6 contains sample data to create Figure 6. Additional more detailed data can be provided upon reasonable request, but cannot be publicly disclosed as it contains data from licenced databases. </p>
General practice characteristics associated with life expectancy of practice populations: a cross-sectional study
<p>The dataset was used to investgate features of general practice associated with life expectancy of general practice populations in England for the period 2015-2019.</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.