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641 results for “guidelines”
Interview guideline for manager of maker space
<p>In the framework of the EU funded project Make-IT, 10 case studies of maker spaces in different European countries have been compiled based on four interviews each (amongst other data). One interview was conducted with the manager of the maker space and three interviews with makers who regularly made use of the maker space. The results are publised in aggregated form in D3.1 and D3.2 on the project's website: http://make-it.io/</p>
Compound Flood Risk Guidelines test data
<p>Time series data of flood drivers (discharge, rainfall, coastal water levels) for various case studies in support of compound fllood risk guidelines.</p> <p>version 1: data for Charleston, NC, USA & BrisBane, AUS.</p> <p>version 2; added data for Toamasina, MDG</p> <p>version 3: consistent file structure</p> <p>version 4: reduce coastal time series to 30min temporal resolution</p>
Figure 1 in Construction of a phylogenetic matrix: Scripts and guidelines for phylogenomics
Figure 1. Flowchart of constructing a phylogenetic matrix for phylogenomics. The custom scripts used in each step are marked as italic. Dashed boxes indicate that these strategies of each step choose only one or more suitable strategies.
Ethics guidelines for AI
<p>This is the dataset corresponding to the supplementary information (Table S2) published in the following article:</p> <p>Jobin, A., Ienca, M. & Vayena, E. The global landscape of AI ethics guidelines. <em>Nat Mach Intell</em> <strong>1</strong>, 389–399 (2019). https://doi.org/10.1038/s42256-019-0088-2</p> <p>You are free to use any of its data on the condition of citing this dataset: Jobin, A., Ienca, M., & Vayena, E. (2019). Ethics guidelines for AI [Data set]. <em>Zenodo/The Authors</em>. https://doi.org/10.5281/zenodo.10966287</p>
Figure 1 in Development and objectives of the PHYCOMORPH European Guidelines for the Sustainable Aquaculture of Seaweeds (PEGASUS)
Figure 1: Seaweed aquaculture to meet the goals of the European bioeconomy strategy (© Michele Barbier, based on EC documentation, 2018, source photos: iStock, © roxyminder #94394792; Fotolia_110024322_Subscription_XXL_© Countrypixel.jpg).
Figure 3 in Development and objectives of the PHYCOMORPH European Guidelines for the Sustainable Aquaculture of Seaweeds (PEGASUS)
Figure 3: Different European legislation with implications for seaweed aquaculture (© Michele Barbier).
Figure 2 in Development and objectives of the PHYCOMORPH European Guidelines for the Sustainable Aquaculture of Seaweeds (PEGASUS)
Figure 2: The development of sustainable seaweed aquaculture in Europe faces a number of challenges: market size, potential environmental impact, and preservation of local genetic diversity, the need to intensify research – both fundamental and applied, regulation of food quality, heavy metals or alien species, and cultivation constraints ranging from automation to issues of epiphytism (© Michele Barbier).
Figure 4 in Development and objectives of the PHYCOMORPH European Guidelines for the Sustainable Aquaculture of Seaweeds (PEGASUS)
Figure 4: Actions promoting the preservation of European marine biodiversity (© Michele Barbier, source photo © freepick.com).
Research Data Management Framework - POC-Study: Guideline and protocol
<p>Dataset for the following paper:<br><br>Proof-Of-Concept-Studie für das FDM in den Ingenieur:innenwissenschaften</p> <p><strong>Ein Forschungsdatenmanagement-Rahmenwerk</strong><br>T. Hamann, C. Florides, A. Abdelrazeq, R. H. Schmitt</p> <p>Forschungsdatenmanagement (FDM) gewinnt seit Jahren an Bedeutung. Das Ziel, Daten wiederverwendbar aufzubereiten und nachzunutzen anstatt sie aufwändig neu zu erheben, wird von Forschenden der deutschen Ingenieur:innenwissenschaften jedoch nur selten verfolgt. Um dem entgegenzuwirken, wurde ein Rahmenwerk für das FDM in den Ingenieur:innenwissenschaften entwickelt. In einer Proof-Of-Concept-Studie soll dieses nun erstmals anhand des Forschungsprojekts KIOptiPack validiert werden.</p> <p><strong>A Research Data Management Framework</strong></p> <p>Research data management (RDM) has been gaining in importance for years. However, the goal of preparing data sustainably and reusing existing data instead of laboriously collecting it from scratch is rarely pursued by researchers in the German engineering sciences. To counteract this, a framework for RDM in the engineering sciences was developed. In a proof-of-concept study, this framework will be validated for the first time using the research project KIOptiPack.<br>Stichwörter: Forschung, Informationsmanagement, Digitalisierung<br><br><br>The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number 442146713.</p>
Figure 1 in Why we should develop guidelines and quantitative standards for using genetic data to delimit subspecies for data-poor organisms like cetaceans
Figure 1. Depiction of the divergence of lineages with four times (T1–T4) chosen to illustrate different levels of biological organization. At T1 the yellow lineage is found across the distribution and although there are likely Demographically Independent Populations (DIPs) that differ in frequencies of the blue, yellow, and red lineages, there are no discontinuities. At T2 some lineages may be diagnosable but likely do not yet appear to be separate lineages. At T3 three groups (the blue/green, yellow, and orange/red lineages) meet the subspecies definition (they are diagnosable and appear to be diverging separately). The divergence level is not sufficient that reconvergence can be ruled out. Between T3 and T4, barriers to gene flow change such that the yellow lineage comes into contact with the blue/green and red-dominated lineages. Blue has diverged in a manner by which gene flow does not resume and the green/yellow lineage dies out. The yellow lineage reconverges and persists alongside the red lineage with a small level of gene flow (orange). At T4 the blue lineage is a species evolving separately from the yellow/red species. The yellow/red species has two subspecies that are both diagnosable and partially diverged.
Figure 3 in Guidelines and quantitative standards to improve consistency in cetacean subspecies and species delimitation relying on molecular genetic data
Figure 3. Flow diagram for subspecies delineation using combined quantitative and qualitative standards. The threshold values assume the user is evaluating a case relying on mtDNA control region data. Percent Diagnosable (PD) is the smallest strata-specific correct classification score in a given comparison (e.g., PD50 in two-strata comparisons in Archer et al. 2017). The second box in the second row (other evidence to meet subspecies definition) allows for subspecies delineation when both conditions are not met using mtDNA. This box could be used either for the case when one condition is met and one unmet or when both just barely miss meeting the standards. For example, consider the case with PD <95% and dA> 0.004. Diagnosability could be achieved with morphological data or nuclear data that are sufficient for subspecies but not for full species.
Figure 2. A in Guidelines and quantitative standards to improve consistency in cetacean subspecies and species delimitation relying on molecular genetic data
Figure 2. A comparison of the pairs of populations (red triangles), subspecies (green squares) and species (blue circles) estimated by Rosel et al. (2017a). Net nucleotide divergence (dA) is shown on a natural log scale to better illustrate differences between the pairwise comparisons at low levels of divergence. Bars show the central 95th-pecentile of the estimate distributions. The solid vertical line at dA = 0.020 delimits all but one species and correctly excludes all subspecies pairs. The vertical dashed line at dA = 0.004 delimits all populations from the higher taxonomic levels and correctly delimits seven of eleven subspecies. The horizontal dashed lines are two potential thresholds for percent diagnosable (80% and 95%) that are discussed in the text.
AGENT Guidelines for dataflow
<p>The AGENT project aims at integrating data from different sources (genebanks, research institutes, international archives) and types (passport, phenotypic, genomic data).</p> <p>These guidelines have been developed to explain the data flow within the AGENT project and should be useful for other projects.</p> <p>The phenotypic data templates are included.</p>
Hand Washing Video Dataset Annotated According to the World Health Organization's Handwashing Guidelines - METC Subset
<p><strong>Overview:</strong> This is a lab-based dataset with videos recording volunteers (medical students) washing their hands as part of a hand-washing monitoring and feedback experiment. The dataset is collected in the Medical Education Technology Center (METC) of Riga Stradins University, Riga, Latvia. In total, 72 participants took part in the experiments, each washing their hands three times, in a randomized order, going through three different hand-washing feedback approaches (user interfaces of a mobile app). The data was annotated in real time by a human operator, in order to give the experiment participants real-time feedback on their performance. There are 212 hand washing episodes in total, each of which is annotated by a single person. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code.</p> <p>This dataset is part on three dataset series all following the same format:</p> <ul> <li><a href="https://zenodo.org/record/4537209">https://zenodo.org/record/4537209 </a>- data collected in Pauls Stradins Clinical University Hospital</li> <li><a href="https://zenodo.org/record/5808764">https://zenodo.org/record/5808764</a> - data collected in Jurmala Hospital</li> <li><a href="https://zenodo.org/record/5808789">https://zenodo.org/record/5808789</a> - data collected in the Medical Education Technology Center (METC) of Riga Stradins University</li> </ul> <p><strong>Note #1:</strong> we recommend that when using this dataset for machine learning, allowances are made for the reaction speed of the human operator labeling the data. For example, the annotations can be expected to be incorrect a short while after the person in the video switches their washing movements.</p> <p><strong>Application: </strong>The intention of this dataset is to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control.</p> <p><strong>Statistics:</strong></p> <ul> <li>Frame rate: ~16 FPS (slightly variable, as the video are reconstructed from a sequence of jpg images taken with max framerate supported by the capturing devices).</li> <li>Resolution: 640x480</li> <li>Number of videos: 212</li> <li>Number of annotation files: 212</li> </ul> <p>Movement codes (in JSON files):</p> <ul> <li>1: Hand washing movement — Palm to palm</li> <li>2: Hand washing movement — Palm over dorsum, fingers interlaced</li> <li>3: Hand washing movement — Palm to palm, fingers interlaced</li> <li>4: Hand washing movement — Backs of fingers to opposing palm, fingers interlocked</li> <li>5: Hand washing movement — Rotational rubbing of the thumb</li> <li>6: Hand washing movement — Fingertips to palm</li> <li>0: Other hand washing movement</li> </ul> <p><strong>Note #2: </strong>The original dataset of JPG images is available upon request. There are 13 annotation classes in the original dataset: for each of the six washing movements defined by the WHO, "correct" and "incorrect" execution is market with two different labels. In this published dataset, all incorrect executions are marked with code 0, as "other" washing movement.</p> <p><strong>Acknowledgments: </strong>The dataset collection was funded by the Latvian Council of Science project: "Automated hand washing quality control and quality evaluation system with real-time feedback", No: lzp - Nr. 2020/2-0309.</p> <p><strong>References: </strong>For more detailed information, see this article, describing a similar dataset collected in a different project:</p> <ul> <li> <p>M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis, A. Elsts. <em>Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines</em>. Data. 2021; 6(4):38. <a href="https://doi.org/10.3390/data6040038">https://doi.org/10.3390/data6040038</a></p> </li> </ul> <p><strong>Contact information: </strong>atis.elsts@edi.lv</p>
Hand Washing Video Dataset Annotated According to the World Health Organization's Handwashing Guidelines - Jurmala Hospital Subset
<p><strong>Overview:</strong> This is a large-scale real-world dataset with videos recording medical staff washing their hands as part of their normal job duties in the Jurmala Hospital located in Jurmala, Latvia. There are 2427 hand washing episodes in total, almost all of which are annotated by two persons. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code.</p> <p>This dataset is part on three dataset series all following the same format:</p> <ul> <li><a href="https://zenodo.org/record/4537209">https://zenodo.org/record/4537209</a> - data collected in Pauls Stradins Clinical University Hospital</li> <li><a href="https://zenodo.org/record/5808764">https://zenodo.org/record/5808764</a> - data collected in Jurmala Hospital</li> <li><a href="https://zenodo.org/record/5808789">https://zenodo.org/record/5808789</a> - data collected in the Medical Education Technology Center (METC) of Riga Stradins University</li> </ul> <p><strong>Applications: </strong>The intention of this dataset is twofold: to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control, and to allow to investigate the real-world quality of washing performed by working medical staff.</p> <p><strong>Statistics:</strong></p> <ul> <li>Frame rate: 30 FPS</li> <li>Resolution: 320x240 and 640x480</li> <li>Number of videos: 2427</li> <li>Number of annotation files: 4818</li> </ul> <p>Movement codes (both in CSV and JSON files):</p> <ul> <li>1: Hand washing movement — Palm to palm</li> <li>2: Hand washing movement — Palm over dorsum, fingers interlaced</li> <li>3: Hand washing movement — Palm to palm, fingers interlaced</li> <li>4: Hand washing movement — Backs of fingers to opposing palm, fingers interlocked</li> <li>5: Hand washing movement — Rotational rubbing of the thumb</li> <li>6: Hand washing movement — Fingertips to palm</li> <li>7: Turning off the faucet with a paper towel</li> <li>0: Other hand washing movement</li> </ul> <p><strong>Acknowledgments: </strong>The dataset collection was funded by the Latvian Council of Science project: "Automated hand washing quality control and quality evaluation system with real-time feedback", No: lzp - Nr. 2020/2-0309.</p> <p><strong>References: </strong>For more detailed information, see this article, describing a similar dataset collected in a different project:</p> <ul> <li> <p>M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis, A. Elsts. <em>Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines</em>. Data. 2021; 6(4):38. <a href="https://doi.org/10.3390/data6040038">https://doi.org/10.3390/data6040038</a></p> </li> </ul> <p><strong>Contact information: </strong>atis.elsts@edi.lv</p>
Influence of conspiracy theories and distrust of community health volunteers on adherence to COVID-19 guidelines and vaccine uptake in Kenya
<p>This cross-sectional study collected data between 25 May –27 June 2021 n=447. It involved all registered community health volunteers (CHVs) who had participated in the COVID-19 vaccine hesitancy study. This data was collected as part of an Epidemic Ethics/WHO initiative that FCDO/Wellcome Grant 214711/Z/18/Z has supported. WHO’s specific grant number was 2020/1077878-0). The funders had no role in study design, data collection and analysis, decision to publish, or manuscript preparation. No authors received a salary from the funders.</p>
Video Examples from: Establishment and Implementation of Guidelines for Narrative Audio-based Room-scale Virtual Reality using Practice-based Methods
<p>This video accompanies our paper: Popp, C. and Murphy, D.T., "Establishment and Implementation of Guidelines for Narrative Audio-based Room-scale Virtual Reality using Practice-based Methods", held at the 2022 AES International Conference on Audio for Virtual and Augmented Reality.</p> <p>Room-scale Virtual Reality (VR) presents sound designers with new challenges to tell stories with audio in games with player-driven narratives. These challenges arise from the player moving in and interacting with the virtual environment. The paper performs a small scoping review of VR/non-VR games and associated literature to identify issues and solutions to the placement of speech-based audio using practice-based research methods. The review leads to the proposition of design guidelines and strategies for their implementation. The paper advocates that each instance of speech-based audio should be short, interactive, and complemented by non-speech audio. Furthermore, each instance’s spatial, interactive, visual, aural, and narrative representation should be considered in combination. The paper also suggests that 3D-binaural audio informed by physics can aid storytelling and make virtual environments player-responsive.</p> <p>The paper is part of the research project SuperCharging Audio Storytelling: 3D Audio in Virtual Reality, funded by UK Arts and Humanities Research Council (AHRC) XR Stories Creative Industries Cluster project, grant no. AH/S002839/1. More information about the project can be found at <a href="https://xrstories.co.uk/project/supercharging-audio-storytelling-3d-audio-in-virtual-reality/">XR Stories</a>. Firelight Technologies Pty Ltd and Unity Technologies kindly provided non-commercial licenses for research purposes as part of this research project.</p>
Dataset for Code Review Guidelines for GUI-based Testing Artifacts
<p>The Excel file contains meta-data about collected white and gray literature, applied inclusion/exclusion criteria, the code system, and a list of identified guidelines.</p>
Dataset for "Guidelines for radiation-safe human activities on the Moon"
<p>Dataset for figures in "Guidelines for radiation-safe human activities on the Moon" publication in Nature Astronomy</p>
Figure 2 in Value and impacts of collecting vertebrate voucher specimens, with guidelines for ethical collection
Figure 2. Decision process when considering collecting voucher specimens. Questions to consider are given in blue, with responses to each question given in black. Directions on how to proceed through the process are given in green. Further details and examples are provided in the text
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.