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470 results for “tool use”
Short example of Biceps Brachii muscle surface HDEMG decomposition using the DEMUSE Tool
<p>This dataset contains 4 examples of synthetic high density surface EMG signals of the Biceps Brachii muscle and results of their decomposition into separate motor unit activity. It is intended as a demonstration of the DEMUSE Tool software for sEMG decomposition and as a basis for practical example of dataset preparation for the HybridNeuro project webinar on Data management and ethics (<a href="https://www.hybridneuro.feri.um.si/results.html#webinars">https://www.hybridneuro.feri.um.si/results.html#webinars</a>). Two sets of data are included: the raw simulated sEMG signals and the results of decomposition of those signals with the DEMUSE Tool.</p>
Hand-selective visual regions represent how to grasp 3D tools for use: brain decoding during real actions
Open the record for dataset details and reuse information.
MiRoR15-P1-Tools used to assess the quality of peer review reports: a methodological systematic review
<p>Database, data extraction form, R codes and protocol related to: Superchi C, González JA, Solà I, Cobo E, Hren D, Boutron I. <em>Tools used to assess the quality of peer review reports: a methodological systematic review</em>. BMC Med Res Methodol. 2019;19(48):1–14. DOI: <a href="https://doi.org/10.1186/s12874-019-0688-x">https://doi.org/10.1186/s12874-019-0688-x</a></p> <p> </p>
Software Tools to Collect and Use Provenance in R
The software tools that scientists use to process and analyze data are typically optimized for performance and ease of use. Few if any such tools are designed to capture and record the details of what happens as the tool performs its task. This detailed information, and more generally the history of an item of data from its creation to its present state, is known as provenance. Provenance has the potential to make science more transparent, reliable, and reproducible. This project focused on collecting and using provenance for scripts written in the R statistical language, which is widely used by ecologists and environmental scientists for data analysis and visualization. Our tools include a provenance collector (rdtLite), which collects provenance as an R script executes (or during a console session), as well as other tools that use the collected provenance to document and visualize the execution or to support activites such as script debugging. The R packages included here are also available on CRAN. For more details, see the project website on GitHub (https://end-to-end-provenance.github.io).
Survey questions and raw data for the study in the paper "Educational Technology for Tutors – What are Useful Tools and Information?"
<p>The data include the questions data set, the answers dataset and the codebook for the questions conducted with soscisurvey (https://www.soscisurvey.de/de/index). The survey itself can be imported in soscisurvey (via the XML data) and reused.</p> <p>The answers are unedited.</p>
Results from retrospective Baltic Sea biodiversity indicator status assessment using BEAT 3.0 tool
<p>Here we present all our results from retrospective Baltic Sea biodiversity indicator data analysis using BEAT 3.0. The BEAT tool (Nygård et al. 2018) is an R coded software (Murray & Nygård 2018, available online: <a href="https://zenodo.org/record/1288315#.XRxNp2cXYg4">https://zenodo.org/record/1288315#.XRxNp2cXYg4</a>) developed for analyzing marine biodivesity status. It follows the strucuture of EU's Marine Strategy Framework Directive. For more detailed metadata about the tool, see Nygård et al. 2018.</p> <p>We used data from various biodiversity indicators in two areas of the Baltic Sea: Bothnian Sea and Gulf of Finland. BEAT integrates indicators to ecosystem components and aggregates them spatially (more details can be found in Nygård et al. 2018). We produced retrospective time series of integrated and aggregated indicators. The yearly assessments are done using the moving average of the indicator status of the past 5 years in order to gain a more robust assessment result. The assessment follows the protocol of biodiversity assessment in the HELCOM Holistic assessment <a href="http://www.helcom.fi/baltic-sea-trends/holistic-assessments">http://www.helcom.fi/baltic-sea-trends/holistic-assessments</a></p> <p>In the results table, all different spatial levels as well as ecosystem components are shown. All indicator results have a value between 0 and 1. If the indicator has a value over 0.6, it is considered to be in a good environmental status. Below are short description of the different columns:</p> <ul> <li>SAUID: ID of the spatial assessment unit (SAU) used. The largest SAU is Baltic Sea with an ID 1. It is divided to smaller SAUs and all individual SAUs have their own ID.</li> <li>SAUlevel: The highest possible level of SAU is the Baltic Sea and it is the level 1. The sea basins (for example Bothnian Sea) are the level 2 and so on.</li> <li>ECID: Ecosystem component ID. All possible indicators have their own ID. See the list of ecosystem components in the input files of the tool (Murray & Nygård 2018).</li> <li>EClevel: Ecosystem component level. The level 1 is biodiversity, in the level 2 it is divided to pelagic habitat, birds, fish, benthic habitat and mammals and so on.</li> <li>EcosystemComponent: this column tells the ecosystem component. It can be higher level e.g. biodversity or an individual indicator for certain taxa.</li> <li>EQR: ecological quality ratio. The status of the certain ecosystem component in a certain SAU. The value varies between 0 and 1. If it is over 0.6, the ecosystem component is considered to be in a good status.</li> <li>Columns H-T: These refer to certain descriptors of Marine Strategy Framework Directive. If the ecosystem component is considered to have a link to a certain descriptor, an EQR value is given.</li> <li>year: year of the assessment. Note: all the yearly values are a moving average of past 5 years.</li> </ul> <p> </p>
Dataset for "Too Simple? Notions of Task Complexity used in Maintenance-based Studies of Programming Tools"
<p>This dataset contains the data to replicate the findings in the publication "Too Simple? Notions of Task Complexity used in Maintenance-based Studies of Programming Tools". The dataset includes the bibliographies and intermediate analysis tables for the analyzed literature. Further, it contains the experiment materials providing context for the task discussed in Section V of the publication.</p> <p>The artifact contains the following files:</p> <ul> <li>icpc-from-survey.bib: The bibliographic entries of all publications selected from the corpus of the paper "Confounding parameters on program comprehension: a literature survey" by Janet Siegmund and Jana Schumann (https://doi.org/10.1007/s10664-014-9318-8)</li> <li>icpc-manually-added.bib: The bibliographic entries of manually added publications.</li> <li>icpc-phase-2-and-3.[csv|xlsx]: The intermediate analysis tables for phases 2 and 3 as described in the paper. The table also contains columns transferred from the original dataset of the Siegmund and Schumann study (marked as "transferred")</li> <li>experiment-materials.zip: Includes the materials for the experiment used in Section V <ul> <li>introduction-english.[docx|rtf]: The experiment protocol we read to participants, including the introduction to the system architecture.</li> <li>JumpODrom.package.zip: The code of the used system.</li> <li>task-16-description.txt: The textual description of the task discussed in Section V.</li> <li>task-16.patch: The change applied to the system code prior to the task, which causes the faulty behavior outlined in the task description.</li> </ul> </li> </ul> <p> </p>
Brachypodium distachyon images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p>Brachypodium distachyon images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Euphorbia peplus images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p>Euphorbia peplus images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Arabidopsis thaliana images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Arabidopsis thaliana</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Oryza sativa images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Oryza sativa</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Solanum lycopersicum images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Solanum lycopersicum</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Ocimum basilicum images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Ocimum basilicum</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Software-based decision support tools used in the sanitation sector
<p>This dataset includes data used in a scoping review on how decision support tools used in the sanitation sector address resource recovery considerations. The dataset is an accompaniment to the publication "A review of how decision support tools address resource recovery in sanitation systems", which was submitted to the Journal of Cleaner Production.</p>
Automatic labelling of HeLa "Kyoto" cells using Deep Learning tools
<p><strong>Name</strong>: Automatic labelling of HeLa “Kyoto” cells using Deep Learning tools</p> <p><strong>Data type</strong>: Microscopy images from the dataset “<strong>HeLa “Kyoto” cells under the scope</strong>”, Brightfield (BF), Digital Phase Contrast (DPC, either “raw” or “square-rooted”), Tubulin and H2B fluorescent channel, paired with their corresponding nuclei or cell/cyto label images.</p> <p><strong>Labels images</strong>: Labels images were generated using the script <em>“prepare_trainingDataset_cellpose.ijm</em>”.</p> <p>Briefly, for 5 defined time-points (1,10,50,100,150), channels of interest were duplicated, resaved and :</p> <p>- nuclei label images were obtained using <a href="https://github.com/stardist/stardist">StarDist</a> on H2B channel</p> <p>- cell label images were obtained using <a href="https://github.com/MouseLand/cellpose">Cellpose</a> on Tubulin and H2B channels</p> <p>A quick visual inspection of the resulting label images concluded that they were satisfying enough, despite certainly not being perfect.</p> <p>Notes :</p> <p>- This labelling strategy:</p> <p>o will not produce 100% accurate labels, but they might be more reproducible than labels generated by humans and are (definitely) much faster to obtain.</p> <p>o is <strong>NOT a recommended way of generating labels images</strong>, but for educational purposes.</p> <p>- The fluorescent channels are part of the dataset to ease the process of review of the labels and are NOT used for training. We generated the labels from the fluorescent channels to later predict labels from the BF or DPC channels only. As such, the fluorescent channels should not be “reused” with our labels during training.</p> <p><strong>File format</strong>: .tif (16-bit)</p> <p><strong>Image size</strong>: 540x540 (Pixel size: 0.299 nm)</p> <p> </p> <p><strong><em>NOTE</em></strong>: This dataset uses the “HeLa “Kyoto” cells under the scope” dataset (<a href="https://doi.org/10.5281/zenodo.6139958">https://doi.org/10.5281/zenodo.6139958</a>) to automatically generate annotations</p> <p><strong><em>NOTE</em></strong>: This dataset was used to train cellpose models in the following Zenodo entry <a href="https://doi.org/10.5281/zenodo.6140111">https://doi.org/10.5281/zenodo.6140111</a></p>
Raw data for the journal article "Cracks as efficient tools to mitigate flooding in gas diffusion electrodes used for the electrochemical reduction of carbon dioxide"
<p>This data set corresponds to the article by Kong et al. entitled "Cracks as efficient tools to mitigate flooding in gas diffusion electrodes used for the electrochemical reduction of carbon dioxide", published in Small Methods</p>
Variation in Detected Adverse Events using Trigger Tools: A Systematic Review and Meta-Analysis
<p>Raw data sets for the meta-analysis.</p> <p>Data collection file with all the information extracted from the included studies.</p> <p>QAT file with the information from the quality assessment tool (QAT) for all included studies.</p> <p>ReadMe with information on data sets and updates.</p> <p>Codebooks for both data sets.</p>
Collection of Tools used in the Mastic collection process. (uploaded on 09/29/22)
<p>Documentation material from the Mastic pilot of the Mingei project</p>
The Key to the City: Using Digital Tools to Understand Tablet Provenience
<p>These files are those used in the keyness analysis for unprovenienced "Diyala" texts against tablets from Tutub, Eshnunna, Tell Suleimah, Kish and Girsu. Each site has two files: the cleaned atf used to generate a word list, and the word list giving the frequency of each word (based on the lemmatized version of words in the Lemma List and incomplete words in the Stopword List).</p>
IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 2nd Web-Delphi process to HTA stakeholders, organized in a single panel
<p>IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 2<sup>nd</sup> Web-Delphi process to HTA stakeholders, organized in a single panel (all stakeholder groups in a single panel, 2 rounds), about the views of stakeholders regarding “This aspect should be considered in the evaluation of new medicines on a common basis” (2019)</p> <p>For details on the Web-Delphi process, see: IMPACT HTA, Work Package 7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2, Deliverable 7.2 (Multi-criteria evaluation framework), Advancing knowledge and MCDA tools to assist HTA agencies in evaluating medicines on a common basis (2021) Oliveira, M.D. (IST), Panos Kanavos (LSE), Bana e Costa, C. (IST)</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.