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13,499 results for “researcher”
Cikapundung riverbank research spectrum
<p>This research spectrum was derived from Google Scholar search limited to earth science and environmental science. The search is conducted in May 2017. We used "Freemind" open source app to make the figure (https://sourceforge.net/projects/freemind/).</p> <p>Method: literature review via scientometric</p> <p>Database: Google Scholar, filter: Cikapundung (intitle)</p> <p>App: Freemind, Google Sheets </p>
Eco-hydrology Cikapundung Project: Research Sphere
<p>This image is uploaded as an integrated part of Eco-Hydrology Cikapundung project. This image will be cited across all future publications related to this project as CC-BY image. Therefore it should not be treated as prior publication of any kind.</p> <p>We used www.Draw.io and the source code is available on GIthub (https://github.com/dasaptaerwin/CikapundungProject/blob/master/researchStructure).</p>
Eco-hydrology Cikapundung Project: citation connections and research profile building
<p>This image is uploaded as an integrated part of Eco-Hydrology Cikapundung project. This image will be cited across all future publications related to this project as CC-BY image. Therefore it should not be treated as prior publication of any kind.</p> <p>We used www.Draw.io and the source code is available on GIthub (https://github.com/dasaptaerwin/CikapundungProject/blob/master/citationConnection.xml).</p> <p>---<br> Dokumen ini disusun sebagai pelengkap riset untuk menggambarkan kaitan sitasi antar dokumen dari hulu ke hilir. Setiap dokumen diupayakan ber-DOI agar dapat <em>autosync</em> dengan profil riset yang tersedia: Google Scholar, Sinta, ORCID. Dengan dibuatnya dokumen hubungan sitasi ini, maka diharapkan dapat menjelaskan bahwa tidak terjadi duplikasi dalam publikasi.</p>
Research data supporting "Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy"
<p>Research data supporting the publication:</p> <p>M. Bergholt, 2017, Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy, Biomaterials, Volume 140, September 2017, Pages 128–137, DOI: 10.1016/j.biomaterials.2017.06.015</p>
Research data supporting "Enzyme Prodrug Therapy Engineered into Electrospun Fibers with Embedded Liposomes for Controlled, Localized Synthesis of Therapeutics"
<p>Research data supporting the publication: Chandrawati R. et al., 2017, Enzyme Prodrug Therapy Engineered into Electrospun Fibers with Embedded Liposomes for Controlled, Localized Synthesis of Therapeutics, Advanced Healthcare Materials. DOI: 10.1002/adhm.201700385</p>
Data for: Wikipedia as a gateway to biomedical research
<p>Wikipedia has been described as a gateway to knowledge. However, the extent to which this gateway ends at Wikipedia or continues via supporting citations is unknown. This dataset was used to establish benchmarks for the relative distribution and referral (click) rate of citations, as indicated by presence of a Digital Object Identifier (DOI), from Wikipedia with a focus on medical citations.</p> <p>This data set includes for each day in August 2016 a listing of all DOI present in the English language version of Wikipedia and whether or not the DOI are biomedical in nature. Source Code for these data are available at: Ryan Steinberg. (2017, July 9). Lane-Library/wiki-extract: initial Zenodo/DOI release. Zenodo. http://doi.org/10.5281/zenodo.824813</p> <p>This dataset also includes a listing from Crossref DOIs that were referred from Wikipedia in August 2016 (Wikipedia_referred_DOI). Source code for these data sets is available at: Joe Wass. (2017, July 4). CrossRef/logppj: Initial DOI registered release. Zenodo. http://doi.org/10.5281/zenodo.822636 </p> <p>An article based on this data was published in PLOS One:</p> <p>Maggio LA, Willinsky JM, Steinberg RM, Mietchen D, Wass JL, Dong T. Wikipedia as a gateway to biomedical research: The relative distribution and use of citations in the English Wikipedia. PloS one. 2017 Dec 21;12(12):e0190046. </p> <p>https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190046 </p>
Research data supporting "Electrospun aniline-tetramer-co-polycaprolactone fibres for conductive, biodegradable scaffolds"
<p>Research data supporting the publication: Guex, A.G. et al., 2017, "Electrospun aniline-tetramer-<em>co</em>-polycaprolactone fibres for conductive, biodegradable scaffolds", MRS Communications. https://doi.org/10.1557/mrc.2017.45</p> <p> </p>
Research data supporting "Sequence-Dependent Self-Assembly and Structural Diversity of Islet Amyloid Polypeptide-Derived β-Sheet Fibrils"
<p>Research data supporting the publication:</p> <p>Wang, S.-T. et al., 2017, Sequence-Dependent Self-Assembly and Structural Diversity of Islet Amyloid Polypeptide-Derived β-Sheet Fibrils, ACS Nano, http://dx.doi.org/10.1021/acsnano.7b02325</p>
Contextual Factors Research in Continuous Integration (CI) Projects
<p>These files include process documentation for the research on project contextual factors in Continuous Integration (CI). They cover previous research studies and survey details. </p>
Unfair Inequality in Education: A Benchmark for AI-Fairness Research (Aequitas WP7 Use Case S2)
<h1>Unfair Inequality in Education: A Benchmark for AI-Fairness Research</h1> <p>This dataset proposes a novel benchmark specifically designed for AI fairness research in education. It can be used for challenging tasks aimed at improving students' performance and reducing dropout rates which are also discussed in the paper to emphasize significant research directions. By prioritizing fairness, this benchmark aims to foster the development of bias-free AI solutions, promoting equal educational access and outcomes for all students.</p> <h2>Structure</h2> <p><code>benchmark</code> contains:</p> <ul> <li>the proposed dataset (<code>dataset.csv</code>), </li> <li>the mask for dealing with missing values (<code>missing_mask.csv</code>), and</li> <li> <div> <div>the meta-columns providing grouping criteria and sample weights for each student (<code>meta_cols.csv</code>).</div> </div> </li> </ul> <p><code>raw_data</code> includes:</p> <ul> <li>the original dataset (<code>original.csv</code>), and</li> <li>the intermediate stages of the pre-processing and validation pipelines (<code>split</code>, <code>pre_processed</code>, and <code>validation</code>).</li> </ul> <p><code>res</code> contains the documentation, including:</p> <ul> <li>the transformation mapping each column of the original dataset to the proposed one, along with the missingness category and original text (<code>meta_data_mapping.csv</code>),</li> <li>the value type and domains of each column of the proposed datasets (<code>meta_data_stats.json</code>), and</li> <li> <div> <div>the statistical indices of the validation pipeline (<code>bias_preservation_results.json</code>).</div> </div> </li> </ul> <p><code>src</code> contains the source code for running the pre-processing and corresponding analysis:</p> <ul> <li><code>pre_processing</code> and <code>stats</code>contain the code for the two corresponding tasks, and</li> <li><code>pre_processing.py</code> and <code>split.py</code> are two entry points.</li> </ul> <p>Finally, <code>Dockerfile</code> and <code>requirements.txt</code> set up the environment for running the applications across multiple platforms and with Python, respectively.</p>
Roadmap for Developing a Dynamic and Reproducible Research Article with ARTE workflow
<p>The figures illustrates a roadmap for developing a dynamic and reproducible research article using <strong>ARTE (Article Reproducibility Template & Environment) </strong>workflow. The process is categorized into three levels of reproducibility: <strong>Minimal, Proper, and Full</strong>. Each level integrates specific tools and practices to enhance the reproducibility of the research.</p> <p>This proposal is published in the following <strong>OSF project</strong>: <a title="OSF" href="https://osf.io/njdq5/" target="_blank" rel="noopener">https://osf.io/njdq5/</a><br>Shared in the following <strong>GitHub repository</strong>: <a title="GitHub" href="https://github.com/phdpablo/article-template" target="_blank" rel="noopener">https://github.com/phdpablo/article-template</a><br>Exemplified in the following <strong>URL address</strong>: <a title="Article Example" href="https://phdpablo.github.io/article-template/" target="_blank" rel="noopener">https://phdpablo.github.io/article-template/</a></p> <h1>Minimal Reproducibility</h1> <p><strong>1. Use this template</strong>: Start by utilizing the provided template, which is pre-configured with the <strong>TIER Protocol 4.0</strong>. This protocol helps organize research projects in a systematic manner.</p> <p><strong>2. Edit READMEs</strong>: Customize the README files to reflect the details and conclusions of your research. These README files help document the project structure and contents.</p> <p><strong>3. Share on OSF</strong>: Share the project on the <strong>Open Science Framework (OSF)</strong> to ensure accessibility and transparency. This can be done at the beginning, during, or at the end of the research process.</p> <h1>Proper Reproducibility</h1> <p>In addition to the steps mentioned above, the following steps are added:</p> <p><strong>4. Quarto settings:</strong> Adjust the Quarto configuration to fit the needs of your project. This includes modifying the <em>_quarto.yml</em> file for different themes and output formats.</p> <p><strong>5. Develop your narrative</strong>: Write the research narrative using <em>Quarto’s .qmd files</em> within RStudio. This narrative forms the main body of your article and integrates text, code, and outputs seamlessly.</p> <p><strong>6. Environment control:</strong> Implement environment control using the <em>renv package</em>. This ensures that the R environment is consistent and reproducible. The <em>renv.lock</em> file captures the exact versions of R packages used in the project.</p> <p><strong>7. Share dynamic article:</strong> Render and share the dynamic document via GitHub Pages. The Quarto-generated HTML files (docs folders) are hosted on GitHub Pages, making the research accessible and interactive.</p> <h1>Full Reproducibility</h1> <p>Building on the proper reproducibility steps, full reproducibility adds:</p> <p><strong>8. Use Docker:</strong> Employ Docker for operating system-level environment control. A Docker container encapsulates the entire project environment, ensuring that the research can be replicated exactly, regardless of the local machine setup.</p> <h2>Tools Utilized</h2> <ul> <li><strong>TIER Protocol 4.0</strong>: Provides a framework for organizing and documenting research projects.</li> <li><strong>OSF:</strong> A platform for sharing research outputs and ensuring open science practices.</li> <li><strong>Quarto:</strong> A tool for creating dynamic documents that integrate text, code, and outputs.</li> <li><strong>RStudio:</strong> An integrated development environment (IDE) for R, facilitating data analysis and reproducible research.</li> <li><strong>Git/GitHub:</strong> Version control systems that track changes and manage project versions.</li> <li><strong>renv: </strong>An R package for managing and reproducing consistent R environments.</li> <li><strong>GitHub Pages:</strong> A service for hosting static websites directly from a GitHub repository.</li> <li><strong>Docker:</strong> A platform for containerizing applications to ensure consistent environments across different systems.</li> </ul> <h2>Summary</h2> <p>This template guides researchers through creating a reproducible and dynamic article using ARTE (Article Reproducibility Template & Environment) workflow. It starts with basic project setup and documentation, progresses through developing the research narrative with environment control, and culminates in full reproducibility with Docker. This structured approach ensures that research is well-documented, versioned, and easily shareable, promoting open science practices.</p>
Research data - taxonomy for revenue models of platform business models (C2E)
<p>Research data to develop a taxonomy of revenue models for platform business models following a conceptual-to-empirical approach.</p>
India Flood Inventory-Impacts (IFI-Impacts) [1967-2023]: A multi-source national geospatial database to facilitate comprehensive flood research
<p>This repository hosts the India Flood Inventory with Impacts (IFI-Impacts) database. It contains flood event data sourced from the Indian Meteorological Department from 1967-2023. It has undergone extensive manual digitization, cleaning, and includes new information to make it suitable for computational research in hydroclimate.</p> <p>v4.0: Development of District Flood Severity Index (DFSI)</p> <p>v3.0: India Flood Inventory (IFI) 1967-2023. Updated with local government codes (LGD) for state and district. </p> <p>v1.0: India Flood Inventory (IFI) 1967-2016.</p> <p>v2.0: India Flood Inventory (IFI) 1967-2023. With impacts and district flooded area.</p> <p><strong>REFERENCES</strong></p> <p>Saharia, M., Jain, A., Baishya, R.R., Haobam, S., Sreejith, O.P., Pai, D.S., Rafieeinasab, A., 2021. India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research. Nat Hazards. <a href="https://doi.org/10.1007/s11069-021-04698-6">https://doi.org/10.1007/s11069-021-04698-6</a></p> <div> <div>Saharia, M., Jain, S.K., Prakash, V., Malik, H., Sreejith, O.P., Joshi, D., 2025. A district-level flood severity index for flood management in India. Nat Hazards. <a href="https://doi.org/10.1007/s11069-025-07493-9">https://doi.org/10.1007/s11069-025-07493-9</a></div> </div>
Research data - taxonomy for platform revenue models (E2C)
<p>Research data to develop a taxonomy of revenue models for platform business models following an empirical-to-conceptual approach.</p>
Package and Dependency Metadata for CZI Hackathon: Mapping the Impact of Research Software in Science
<p>A collection of useful datasets extracted from <a href="https://packages.ecosyste.ms">https://packages.ecosyste.ms</a> and <a href="https://repos.ecosyste.ms/">https://repos.ecosyste.ms</a> for use at the CZI Hackathon: Mapping the Impact of Research Software in Science.</p><p>All data is provided as NDJSON (new line delimited JSON), each line represents a valid JSON object, and they are separated by newline characters. There are <a href="https://pypi.org/project/ndjson/">python</a> and <a href="https://www.rdocumentation.org/packages/ndjson/versions/0.9.0/topics/stream_in">R</a> libraries for reading these files, or you can maually read each line and parse each line as a single JSON object.</p><p>Each ndjson file has been compressed with gzip (actual command: `tar -czvf`) to reduce download size, they expand to significantly bigger files after extraction.</p><h4>Package Data</h4><p>Package names from cran, bioconductor and pypi that have been parsed by the <a href="https://github.com/chanzuckerberg/software-mentions">software-mentions</a> project (data: <a href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.6wwpzgn2c">https://datadryad.org/stash/dataset/doi:10.5061/dryad.6wwpzgn2c</a>) are collected together with their latest release at time of publishing along with the names of their dependencies, those dependency names have then also been recursively fetched with latest release and dependencies until the full list of transitive dependencies is included. </p><p>Note: This approach uses a simplified method of dependency resolution, always picking the latest version of each package rather than taking into account each dependencies specific version range requirements, this is primarily due to time constraints and allows all software ecosystems to be processed in the same way. A future improvement would be to use each package ecosystem's specific dependency resolution algorithm to compute the full transitive dependency tree for each mentioned software package.</p><h4>GitHub Data</h4><p>Two different approaches were taken for collecting data for referenced GitHub mentions:</p><p>1. `github.ndjson` is metadata for each repository from GitHub, including "manifest" files which are known files that contain dependency information for a project such as requirements.txt, DESCRIPTION and package.json, parsed using <a href="https://github.com/ecosyste-ms/bibliothecary">https://github.com/ecosyste-ms/bibliothecary</a>, which may include transitive dependencies that have been discovered in a `lockfile` within the repository.</p><p>2. `github_packages.ndjson` is metadata for each package that was found on any package manager that references the GitHub url as it's repository url/source/homepage, these packages, like the cran and pypi data above, include the latest release and their direct dependencies. There may be more than one package for each GitHub URL as it is a one to many relationship. `github_packages_with_transitive.ndjson` follows the same format but also includes the extra resolved transitive dependencies of all packages using the same approach as with cran and pypi data above with the same caveats. </p><p>There are also many more ecosystems referenced in these files than just cran, bioconductor and pypi, https://packages.ecosyste.ms provides a standardized metadata format for all of them to enable comparison and simplification of automation.</p><h4>Contact</h4><p>If you would like any help, support or more data from Ecosyste.ms please do get in touch via email: hello@ecosyste.ms or open an issue on GitHub: https://github.com/ecosyste-ms/packages/issues</p>
PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) of the research entitled "Development of Competences for the Fashion Designer: a Scope Review
<p>PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) 2015 checklist: recommended items to address in a systematic review protocol and Check list CAPSI - Critical analysis of the articles related to the specific objective: map the current themes that permeate the competencies of fashion design professionals through a scoping review.</p>
Research Data Management in Selected Health Research Institutions in Uganda
<p>This data set was collected from Researchers in three purposively selected health reseach Institutions in Uganda. The purpose of the study was to explore compliance to FAIR data princiles and Open science initiative given the increasing dependence on donor funding and need to fulfill the requirement for good research practices. </p>
Vertical profiles of stable water isotopes and thermodynamic properties from research flights during the L-WAIVE field campaign in June 2019
<p>This datasets contains the measurements of stable water isotopes conducted during the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) field campaign taking place in June 2019 in the Annecy valley in the French Alps (Chazette et al. 2021). The measurements were conducted using a Picarro laser spectrometer L2130-i that was installed on an ultralight aircraft. The Picarro measurements of atmospheric humidity are merged measurements of thermodynamic properties by a fast-response temperature and humidity probe (iMet XQ-2; see also Chazette et al. 2021) interpolated on 10s temporal resolution.</p> <p>The data is provided on a one file per flight. All variables are described in README.</p> <p>This dataset has been used in Thurnherr et al. (submitted) for a comparison study of stable water isotopes measurements from various platforms and COSMOiso model simulations.</p>
The metadata of books submitted as research outputs to annual Lithuanian research assessments from 2008 to 2020
<p>This dataset contains ISBNs and below described metadata of 5199 books which Lithuanian universities and research institutions submitted as their best research outputs to get state funding after annual research evaluations from 2008 to 2020. The thorough explanation how this dataset was created provided in a preprint: Dagiene, Eleonora. 2023. "The Challenge of Assessing Academic Books: The UK and Lithuanian Cases Through the ISBN Lens." SocArXiv. <a href="https://doi.org/10.31235/osf.io/qpwxn">https://doi.org/10.31235/osf.io/qpwxn</a> </p><p>This dataset contains ISBNs and their metadata, gathered from the Global Register of Publishers <a href="https://grp.isbn-international.org/">https://grp.isbn-international.org/</a></p><p><strong>Description of data columns and remarks:</strong></p><p>ISBNs – unique identifiers – at least one author of the book identified by the ISBNs is affiliated with Lithuanian institution;</p><p>output_types – ['edited volumes', 'authored books'] – types of books eligible for research assessment;</p><p>years – 2008-2020 – book publishing years as reported by submitting institutions;</p><p>unified_title – titles of publishers – parent publishers (e.g. Springer Nature, Informa (Taylor & Francis)). Lithuanian institutions kept in Lithuanian;</p><p>primary_occupations_main – 'academic publishers', 'universities', 'intermediaries', 'other publishers', 'not publishers' – main types of institutions/companies which registered particular ISBNs;</p><p>primary_occupations_detailed – 'academic publishers', 'university presses', 'university departments', 'self-publishing', 'publishing services', 'specialised publishers', 'general publishers', 'societies', 'not publishing', 'research institutions', 'governmental institutions', 'libraries', 'museums', 'non-governmental organisations' – just more types of institutions/companies;</p><p>country – country name – countries where ISBNs were assigned, e.g. books authored or co-authored by Lithuanian researchers issued in 53 countries worldwide;</p><p>sciences – 'natural sciences', 'social sciences', 'agricultural sciences', 'technological sciences', 'medical and health sciences', 'humanities' – as brunches of sciences categorised by the Research Council of Lithuania.</p>
Research data on pavement particle emissions for D6.5
<p>The levels of particles generated by the circulation of the carousel wheels on the different pavements were evaluated in two complementary ways: through mobile or static measurements.</p><p> </p><p>The static measurements correspond to the particles aspirated laterally from the wheels (at a height of 0.15 m and 1.0 ± 0.5 m from the central path of the tread) using an ELPI analyzer. This analyzer provides access to both ultrafine (number-weighted data) and micrometer (volume-weighted data) particle concentrations every second. The ELPI analyzer operates on the principle of electrical impaction. The aspirated particles are electrically charged, separated according to their aerodynamic diameters, and then their impact on the dedicated plates of the analyzer generates an electric current that, after amplification, allows them to be detected and classified by size (between 7 nm and 4 µm). The total volume-weighted concentrations were derived numerically from the number concentrations. Due to the turbulence induced by the passage of the wheels, it was assumed that the samples were relatively homogeneous.</p><p> </p><p>The mobile measurements were based on the use of a Promo 2000 particle analyzer (from PALAS). It is a white light optical analyzer that counts particles between 200 nm and 10 µm following light diffraction at 90°. The analyzer was mounted on one of the arms of the carousel in order to measure every second the level of particles generated behind the wheel (at a distance of 0.10 m from it and at a height of 0.10 m) during the passage of different sections. The aspiration speed was of the same order of magnitude as the air flow behind the wheel (< 20% of the rolling speed): about 6 km/h. A custom-made device was added to the system to mark the moment of passage of the instrumented arm at the beginning of the S1 pavement. It was used to locate the periods of circulation of this arm on the different sections (S1, S2, S3 and S4). </p><p> </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.