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5,738 results for “Standards”
Section 5.1 "Task Area 1: Image (meta)data formats and standardization)" Figure 8
<p>Figure 8. A FAIR-IO bundle combines the necessary acquisition and provenance metadata together with multi-resolution, chunked binary data in a single cloud-compatible format for simplified sharing and re-use.</p> <p>from NFDI Grant Application, "<strong>National Research Data Infrastructure for Microscopy and Bioimage Analysis</strong>" (NFDI4BIOIMAGE)</p>
Data associated with "Developing a standardized but extendable framework to increase the findability of infectious disease datasets"
<p><strong>Data associated with "Developing a standardized but extendable framework to increase the findability of infectious disease datasets"</strong></p> <p> </p> <p>Includes:</p> <ul> <li>NIAID Dataset schema</li> <li>NIAID ComputationalTool schema</li> <li>Crosswalk between NIAID schemas and common schemas</li> <li>Survey of Schema.org-compliant repositories</li> </ul> <p><br> The open access movement and scientific reproducibility concerns have led the biomedical research community to embrace efforts to make scientific datasets openly accessible. While many datasets are now available, there are still challenges in ensuring that they are Findable, Accessible, Interoperable, and Reusable (FAIR). To improve the FAIRness of datasets, we evaluated dataset repositories for compliance with Schema.org standards – a collection of standards developed to increase metadata searchability across the internet. Adoption of the Schema.org Dataset standard was highly variable in biomedical research datasets, and the standard omitted many desirable metadata fields. We customized the Schema.org Dataset standard to catalog datasets collected across a Systems Biology research consortium consisting of 15 Centers. We developed a reusable process for creating a schema which is interoperable with other standards, but still extendable and customizable to a particular context. Here, we describe our process along with the associated gains in FAIRness, and discuss ongoing challenges with dataset discoverability – the first step to ensure that the vast amount of open data published by the research community is reused to its maximum value.</p>
Nos_Machine translation gold standard English-Galician 1
<p>1777 carefully curated Galician-English parallel sentences for the evaluation of automatic machine translation. Sentence pairs are classified according to whether the translations are highly literal or not.</p>
Nos_Machine translation gold standard Spanish-Galician 2
<p>1998 carefully curated Galician-Spanish parallel sentences for the evaluation of automatic machine translation. Sentence pairs are classified according to whether the translations are highly literal or not.</p>
Nos_Machine translation gold standard English-Galician 2
<p>1777 carefully curated Galician-English parallel sentences for the evaluation of automatic machine translation. Sentence pairs are classified according to whether the translations are highly literal or not.</p>
Nos_Machine translation gold standard Spanish-Galician 1
<p>1998 carefully curated Galician-Spanish parallel sentences for the evaluation of automatic machine translation. Sentence pairs are classified according to whether the translations are highly literal or not.</p>
Indoor Environmental Quality in Schools: NOTECH Solution vs. Standard Solution - dataset
<p>Dataset for "Indoor Environmental Quality in Schools: NOTECH Solution vs. Standard Solution"</p>
Data for: BetaScan2: Standardized Statistics to Detect Balancing Selection Utilizing Substitution Data
<p>Genome-wide scan using BetaScan2 in 1KG populations report in:</p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/32011695/">BetaScan2: Standardized Statistics to Detect Balancing Selection Utilizing Substitution Data.</a></p> <p>Siewert KM, Voight BF.Genome Biol Evol. 2020 Feb 1;12(2):3873-3877. doi: 10.1093/gbe/evaa013.</p> <p>PMID: 32011695 </p> <p>Code available at: https://github.com/ksiewert/BetaScan</p>
PretoxTM Corpus: a gold standard corpus of preclinical treatment-related findings annotated from toxicology reports
<p>The PretoxTM Corpus is a gold standard corpus of preclinical treatment-related findings annotated from toxicology reports.</p> <p>Example documents annotated by domain experts, also known as a gold standard corpus, are needed in order to develop, train, and validate text mining tools. To this aim we designed and performed an annotation activity for the development of the corpus of treatment-related findings: the PretoxTM corpus.</p> <p>A treatment-related finding expression enclose several named entities; the most relevant one is the abnormal effect detected; which depending on the study domain of the finding, can be given by a measurement, test, or examination named Study Test and an abnormal Manifestation result obtained for that study test; or by an abnormal Finding in study domains where there is no associated test or measurement (e.g., clinical, macroscopic and microscopic). Other related named entities that could be present to complete the treatment-related finding are; the Specimen of the abnormal observation, the Sex of the subject, the Group of subjects in which the observation was detected and the Dose level administration of the compound. Examples of sentences with treatment-related findings are: “The decrease in food consumption and body weight of the animals from the mid dose onwards is regarded as evidence of general toxicity.” and "At dose level 3, absolute and relative liver weights were increased in male rats.”.</p> <p>Contributions: The PretoxTM corpus was developed by BSC, with the contribution of IMIM and a team of experts from the eTRANSAFE EFPIA partners.</p> <p>License: Creative Commons Attribution-ShareAlike 4.0 International (cc by sa 4.0).</p> <p>The PretoxTM resources have been developed as part of the eTRANSAFE project.</p> <p>For more information about PretoxTM please visit:</p> <p>PretoxTM Corpus Gitlab: <a href="https://gitlab.bsc.es/inb/etransafe/preclinical-toxicological-corpus/">https://gitlab.bsc.es/inb/etransafe/preclinical-toxicological-corpus/</a></p> <p>PretoxTM central documentation: <a href="https://pretoxtm.gitlab.io/documentation/">https://pretoxtm.gitlab.io/documentation/</a></p>
16S RC-PCR control samples and mock standards
<p>16S RC-PCR datasets of control samples and mock standards.<br> Produced during the 16S RC-PCR study.</p> <p>Datasets are produced using 2x150 Illumina MiniSeq.<br> A short description of each sample is given in the METADATA_zenodo file.</p>
A Standardized European Hexagon Gridded Dataset Based on OpenStreetMap POIs
<p>Point of interest (POI) data refers to information about the location and type of amenities, services, and attractions within a geographic area. This data is used in urban studies research to better understand the dynamics of a city, assess community needs, and identify opportunities for economic growth and development. POI data is beneficial because it provides a detailed picture of the resources available in a given area, which can inform policy decisions and improve the quality of life for residents. This paper presents a large-scale, standardized POI dataset from OpenStreetMap (OSM) for the European continent. The dataset's standardization and gridding make it more efficient for advanced modeling, reducing 7,218,304 data points to 988,575 without significant resolution loss, suitable for a broader range of models with lower computational demands. The resulting dataset can be used to conduct advanced analyses, examine POI spatial distributions, conduct comparative regional studies, enhancing understanding of the economic activity, distribution, attractions, and subsequently, economic health, growth potential, and cultural opportunities. The paper describes the materials and methods used in generating the dataset, including OSM data retrieval, processing, standardization, and hexagonal grid generation. The dataset can be used independently or integrated with other relevant datasets for more comprehensive spatial distribution studies in future research.</p>
Polar Iridium Surface Velocity Profilers (p-iSVP), and standard Iridium Surface Velocity Profilers (iSVP) during SCALE 2019 Winter and Spring Cruises
<p><strong>Brief data description</strong></p> <p>In 2019, winter and spring scientific research expeditions aboard the SA Agulhas II were conducted along the Good-Hope line (0<sup>o</sup> E) to the Antarctic marginal ice zone (MIZ) in the north-eastern Weddell Sea region as part of the <em>Southern oCean seAsonal Experiment</em> (SCALE; Ryan-Keogh and Vichi, 2022).</p> <p>During the winter expedition, three polar Iridium Surface Velocity Profilers (p-iSVPs; MetOcean model) were deployed by the South African Weather Service (SAWS) between 27 July and 28 July 2019. These buoys were analysed in de Vos et al. (2022). The region of deployment consisted of pancake-ice conditions with an average ice thickness of 40-60 cm. The instruments were deployed by hand by three people, lowered by crane from the ship to the ice on a basket cradle. The first buoy (p-iSVP 1) was deployed in water, in between pancake ice floes, while the other two buoys (p-iSVP 2 and p-iSVP 3) were deployed on roughly circular ice floes > 3 m in diameter.</p> <p>These buoys were expendable devices that recorded GPS position, air and ice temperature, and barometric pressure. The temporal resolution is 30 minutes for p-iSVP 1 and hourly for p-iSVP 2 and p-iSVP 3. The survival of these sensors depended on their battery life, since p-iSVPs can continue to drift in the ocean after ice melting and can be further refrozen in between floes. p-iSVP 1 and p-iSVP 3 continued to transmit data until 15 October 2019. p-iSVP 2 stopped transmitting data on 25 August 2019.</p> <p>During the spring expedition, three standard Iridium Surface Velocity Profilers (iSVPs 4-6; Pacific Gyre model) were deployed by SAWS between 24 October and 28 October 2019 (de Vos et al., 2022). Specifically-designed frames were built around these three iSVPs to allow them to stand securely on the ice, without damaging the non-polar battery, and also to make sure they operated as Lagrangian ice trackers. These buoys were deployed during first-year ice conditions, with an average ice thickness of 80-90 cm. The instruments were deployed with the same protocol as the winter buoys.</p> <p>These buoys recorded GPS position, air temperature and barometric pressure, every hour. Their survival, like the winter p-iSVPs, also depended on their battery life, and therefore it was possible for them to continue to drift after ice melting. The iSVPs transmitted data until 19 December 2019.</p> <p><strong>Buoy names and raw data:</strong></p> <p>p-iSVP 1: 300234067003010-300234067003010-20191015T064320UTC.csv</p> <p>p-iSVP 2: 300234067002060-300234067002060-20191015T064316UTC.csv</p> <p>p-iSVP 3: 300234066992870-300234066992870-20191015T064314UTC.csv</p> <p>iSVP 4: 300234066433050.xlsx</p> <p>iSVP 5: 300234066433051.xlsx</p> <p>iSVP 6: 300234066433052.xlsx</p> <p><strong>Related code: </strong>The buoy data has been processed using https://github.com/mvichi/antarctic-buoys/. </p>
Standardized reference grids for spatial analyses at various grain sizes
<p><strong>Description:</strong><br> These Reference grids have been created for the <a href="https://naturaconnect.eu/">NaturaConnect project</a> and are based on an intersection of the<a href="https://www.eea.europa.eu/data-and-maps/data/eea-coastline-for-analysis-1/gis-data/europe-coastline-shapefile"> European Coastline delineation</a> and the <a href="https://gadm.org/">GADM database</a>.<br> Thee reference grids have been created in a way so that they are fully consistent with the EEA reference grid (https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2), meaning that for example two 5km gridded cells fully match a 10km grid cell in width.</p> <p><strong>Filestructure:</strong><br> ReferenceGrid_Europe_{format}_{grain}</p> <ul> <li> format is either "frac" for fractional data (which has been multiplied with 10000 to save in integer format) or binary (0,1).</li> <li> grain is provided as layers in 100m, 1000m, 5000m, 10000m, 50000m spatial resolution. Alternative aggregations can be provided on request.</li> </ul> <p><strong>File format:</strong><br> The layers are gridded geoTiff files and can be loaded in any conventional Graphical Information System (GIS) or specific analytical programming languages (e.g. R or python). In addition external pyramids (.tfw) have been precreated to enable faster rendering.</p> <p><strong>Geographic projection:</strong><br> We use the <a href="https://epsg.io/3035">Lamberts-Equal-Area Projection</a> by default for all layers in NaturaConnect. This is an equal-area (but distorted shape) projection and commonly used by European institution with a focus on the European continent. For global layers the <a href="https://epsg.io/54009">equal-area World Mollweide projection</a> is used.<br> <br> <strong>Sourcecode:</strong><br> The code to reproduce the layers has been made available in the "code" file.<br> </p>
Translation Alignment: Ancient Greek to Latin. Annotation Style Guide and Gold Standard
<p>This dataset contains guidelines and a gold standard for the alignment of Ancient Greek texts with Latin scholarly translations. </p> <p>The gold standard consists of 100 fragments randomly selected from the <em>Digital Fragmenta Historicorum Graecorum </em>(DFHG) (https://www.dfhg-project.org/), which were aligned manually by Chiara Palladino and David J. Wright using Ugarit (https://ugarit.ialigner.com/). The Annotation Style Guide was developed for this project. The resulting Inter-Annotator-Agreement (IAA) is 90.5%. </p> <p>The materials available in this repository can be used to perform and evaluate alignments of various texts in Ancient Greek, to create gold standards, and to train automated translation alignment models. </p> <p>The Guidelines can be further adapted to address similar language pairs including inflected languages, or can provide a structure for the alignment of other historical texts against modern translations. However, the guidelines are not project-specific: they were specifically intended for the scenario of machine translation. Different research questions, such as translation history or pedagogy, may need further tweaking of these guidelines. </p> <p>For further information on Ugarit and translation alignment of historical languages, see http://ugarit.aligner.com/bib.php and follow us on Twitter (@ugarit_ty). <br> </p> <p> </p>
Proof-of-concept AO telemetry data using the AOT standard format
<p>Dataset containing a demonstration of AO telemetry data using the AOT standard format. Contains data from 5 different systems (CIAO, ERIS, GALACSI, NAOMI and PAPYRUS).</p> <p>The data is as follows:</p> <ul> <li>4 CIAO files "CIAO#_*.fits" where # indicates the AT where the data was produced.</li> <li>2 ERIS files, one in LGS (LTAO) mode and another in NGS (SCAO) mode.</li> <li>1 GALACSI file.</li> <li>4 NAOMI files "NAOMI#_*.fits" where # indicates the AT where the data was produced.</li> <li>2 PAPYRUS files, one using a Shack-Hartmann wavefront sensor (SHWFS) and another using a Pyramid wavefront sensor (PWFS).</li> </ul> <p>GALACSI, CIAO, NAOMI and ERIS data were gathered under ESO's program IDs 60.A-9278(B), 60.A-9278(C), 60.A-9278(D) and 60.A-9278(E) respectively. The data was translated via the functions provided in the Python package <em>aotpy</em> (<a href="https://doi.org/10.5281/zenodo.8187230">10.5281/zenodo.8187230</a>).</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101004719 (OPTICON–RadioNet Pilot).</p>
Cipher Machine Kryha Standard_surface meshes (Inv. 2017-384)
<p>Surface Meshes created from reconstructed CT-image stacks.<br> The inital CT dataset is linked as a related identifier.</p>
Gold standard annotation of epilepsy clinic letters for the development of information extraction tools
<p>This folder contains 200 synthetic letters, based on hospital outpatient epilepsy clinic consultations, written by neurology consultants, specialist trainees, and epilepsy specialist nurses. The letters were double annotated by trained researchers, according to annotation guidelines, uploaded separately (What and How of annotating with Markup). The 200 .ann annotation files are also included.</p> <p>We used Markup (https://getmarkup.com) for annotation (the configuration file is within the uploaded set of annotations) with an epilepsy concept list based on the Unified Medical Language System (UMLS) ontology. All annotations were compared, reviewed, and corrected to form a gold standard annotation set.</p> <p> </p>
Procure-To-Payment (P2P) Object-centric Event Log in OCEL 2.0 Standard
<p><strong>Short Description</strong></p> <p>This process describes the Procure-To-Pay (P2P) procedure within an organization, starting from the initiation of a purchase requirement up to the execution of payment. This simulation extensively uses genuine SAP transactions and object types to offer a realistic representation of the P2P process.</p> <p><strong>Overview</strong></p> <p>Within our simulated organization:</p> <ul> <li> <p>Procurement Initiatives: The procurement journey begins when a department or individual recognizes a need and creates a Purchase Requisition using transaction ME51N.</p> </li> <li> <p>Approval Process: Before the purchase can proceed, the requisition must be approved. This is carried out using transaction ME54N. Given the nature of our simulation, there may be instances where the approval process takes an unusually long time, exemplifying the Lengthy Approval Process behavior.</p> </li> <li> <p>Vendor Interactions:</p> <ul> <li>Upon approval, a Request for Quotation is sent out to potential vendors using transaction ME41.</li> <li>Vendors then submit their quotations, which are maintained in the system using transaction ME47.</li> </ul> </li> <li> <p>Purchase Order Creation: Once a vendor's quotation is selected, a Purchase Order is created using transaction ME21N. The purchase order is then subjected to an internal approval process (ME29N). Occasionally, maverick buying—where purchases are made without proper authorization—can be observed.</p> </li> <li> <p>Goods & Invoice Management:</p> <ul> <li>When the goods are received, a Goods Receipt is recorded using transaction MIGO.</li> <li>Invoices from vendors are then received and recorded. A three-way match, which checks the purchase order, goods receipt, and invoice for discrepancies, is performed using transaction MRBR.</li> </ul> </li> <li> <p>Payment: Once everything is verified, payments are executed using transaction F110. However, there may be instances of Duplicate Payments in our simulation, where the system mistakenly pays the same invoice more than once.</p> </li> </ul> <p><strong>Special Behaviors:</strong></p> <ul> <li>Maverick Buying: Unauthorized purchases, bypassing the standard procedure.</li> <li>Duplicate Payments: An error leading to the same invoice being paid multiple times.</li> <li>Lengthy Approval Process: Delays in approving purchase requisitions or purchase orders, which might lead to operational inefficiencies.</li> </ul> <p><strong>General Properties</strong></p> <p>An overview of log properties is given below.</p> <table> <thead> <tr> <th>Property</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Event Types</td> <td>10</td> </tr> <tr> <td>Object Types</td> <td>7</td> </tr> <tr> <td>Events</td> <td>14671</td> </tr> <tr> <td>Objects</td> <td>9543</td> </tr> </tbody> </table> <p><strong>Authors</strong></p> <p>Gyunam Park and Leah Tacke genannt Unterberg</p> <p><strong>Contributing</strong></p> <p>To contribute, drop us an email! We are happy to receive your feedback.</p>
Marine Stewardship Council (MSC) Fisheries Standard v2.0 assessment scores
<p>MSC fishery assessment scores manually collated by Marine Stewardship Council (MSC). See about file for complete details.</p> <p>This data has been manually collated by MSC from reports prepared by third party assessors. This dataset shows, for each Unit of Assessment (UoA), scoring data from the Public Certification Report of the UoA's most recent initial assessment or re-assessment conducted against the Default Tree v2.0 (including Default Tree v2.01) of the MSC Fisheries Standard as of 26 February 2021. A Unit of Assessment is a unique combination of target stock, fishing method or gear, geographic area, and operators or vessels being assessed which may eventually carry the MSC certification status. This excludes UoA which failed their assessment because failing scores are not consistently reported (they are often simply considered a ‘fail’).</p> <p>The following constraints and limitations apply to this dataset:<br> 1. This data has been manually collated by MSC from fishery reports prepared by third party assessors. MSC carries out data assurance to a standard that is fit for the purpose the information is used for, including being complete, accurate and as up to date as possible. If accuracy is paramount to a finite resolution, receivers are asked to validate data against assessment reports which can all be found published on fisheries.msc.org. The MSC is not responsible for any issues arising to any parties as a result of any information provided therein.<br> 2. This dataset shows, for each UoA, scoring data from the certification report of the UoA's most recent initial assessment or re-assessment conducted against v2.01 of the Standard as of 26 February 2021. This excludes UoA which failed their assessment.<br> 3. UoA details (uoa_details) can be joined to scoring data (scores) by the Event_unitbk. This will join the details of the scored UoA with the score that was awarded to the UoA.</p> <p>This dataset consists of two tables:</p> <p>1. uoa_details<br> This table contains details describing UoA included in the dataset. UoA descriptions include the name of fishery as published on Track a Fishery (fisheries.msc.org), species, gear, and other description of the UoA that was assessed.<br> <br> 2. scores<br> Scoring results to the lowest level (scoring guidepost of the scoring issue) for each UoA, as presented in the scoring tables of the report. One row represents a single guidepost result of a single scoring issue. However, the performance indicator (PI) ‘Score’ will be duplicated for all scoring issues in the same PI. For example, if a UoA received a score of 80 for a performance indicator, and in this performance indicator there are 2 scoring issues each scored at 3 guideposts, there will be 6 total rows showing the results for each guidepost of each scoring issue, but all rows will show 80 for the overall 'Score' of the PI. One row also exists for each Principle score of each UoA, in which the principle will be noted in the 'PI' column, and the Principle score in 'Score'. Data has been copied from the scoring tables in the Public Certification Report.</p>
Graphs for DCRs using SPD with shield for possible variation using standard and armoured fibre
<p>Dark Count Rate (DCR) graphs related to shield with possible variation using standard and armoured fibre for Single Photon Detector (SPD).</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.