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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>
Commit metadata for projects in TravisTorrent
<p>This dataset contains git metadata for 1,262 Java and Ruby projects hosted at GitHub up to January 27, 2017, 19:18:08 +0000. These are the projects in the TravisTorrent dataset released on January 11, 2017.</p> <p>It consists of an SQLite table containing the following columns:</p> <ul> <li><strong>project</strong>: project name on GitHub, in the form "owner/project"</li> <li><strong>sha</strong>: the commit id</li> <li><strong>message</strong>: the commit message</li> <li><strong>date</strong>: commit date</li> <li><strong>author_name</strong>: name of the commit author (not the committer)</li> <li><strong>author_email</strong>: email of the commit author (not the committer)</li> </ul>
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>
The Ó Riada Projects (from Seán Ó Riada Collection/Bailiúchán Sheáin Uí Riada)
<p>Music-related projects discovered during PhD research on the Seán Ó Riada Collection at the Boole Library, University College Cork, publications relating to Seán Ó Riada, and metadata from archives throughout Ireland.</p> <p> </p> <p>Dataset originally created 16/03/2016 UPDATE: Packaged on 19/09/2024</p> <p>I. About this Data Set</p> <p>This data set is a result of close reading conducted by Patrick Egan (Pádraig Mac Aodhgáin) at the Boole Library, University College Cork, publications relating to Seán Ó Riada, and metadata from archives throughout Ireland. Research was conducted between 2014-2018. It contains a combination of metadata from searches of the Seán Ó Riada Collection finding aid (or "descriptive list") concerning musical scores or "score cards" that were created by 1960s Irish artist Seán Ó Riada, but also includes other keyword searches relating to Seán Ó Riada's artistic output in a broad sense (play scripts, film documents, scripts for radio, amongst others. The PhD project was published in 2020, entitled, “Exploring ethnography and digital visualisation: a study of musical practice through the contextualisation of music related projects from the Seán Ó Riada Collection” You are invited to use and re-use this data with appropriate attribution.</p> <p>The Ó Riada Projects dataset consists of 456 rows.</p> <p>II. What’s included? This data set includes:</p> <p>The Ó Riada Projects Dataset – a .CSV containing <br>Type of Project Project Date Start Project Date Finish Published? Recording? In Special Collection? Title of Project Description of Project Source<br><br>This .xlsx file is the direct export of the Ó Riada Scores Google Spreadsheet</p> <p>III. How Was It Created? These data were created by daily visits to the Seán Ó Riada Collection, archives in Ireland, and online databases over the course of four years, through close reading, description, and general collection of metadata.</p> <p>IV. Data Set Field Descriptions</p> <p>a) The Ó Riada Projects dataset field descriptions</p> <p>Type of Project - the type of production, format, or activity <br>Project Date Start - the earliest known date when work started on the project <br>Project Date Finish - the date when work was completed on the project <br>Published? - if the project has been published, broadcast, or shared publicly <br>Recording? - if there is a recording of the project <br>In Special Collection? - does the project exist in the Seán Ó Riada Collection<br>Title of Project - project title <br>Description of Project - a brief description of the project <br>Source - document or place, the source of information for details about the project</p> <p>V. Rights statement The text in this data set was created by the researcher and can be used in many different ways under creative commons with attribution. All contributions to this PhD project are released into the public domain as they are created. Anyone is free to use and re-use this data set in any way they want, provided reference is given to the creator of this dataset.</p> <p>VI. Creator and Contributor Information</p> <p>Creator: Patrick Egan (Pádraig Mac Aodhgáin)</p> <p>VII. Contact Information Please direct all questions and comments to Patrick Egan via his website at www.patrickegan.org. You can also get in touch with the Library via UCC website.</p>
An Exploratory Study of Documentation Strategies for Product Features in Popular GitHub Projects [Replication Package]
<h2>Artefact Summary</h2> <p>This repository contains the replication package for the paper 'An Exploratory Study of Documentation Strategies for Product Features in Popular GitHub Projects,' presented at the <em><a href="https://cyprusconferences.org/icsme2022/" target="_blank" rel="noopener">38th IEEE International Conference on Software Maintenance and Evolution (ICSME'22)</a></em>.</p> <p>The purpose of the package is to facilitate the verification and reproduction of the study results.<br>It provides all computational notebooks used to collect and analyse data, as well as the slides of the conference presentation.</p> <h2>Paper Abstract</h2> <p>[Background] In large open-source software projects, development knowledge is often fragmented across multiple artefacts and contributors such that individual stakeholders are generally unaware of the full breadth of the product features. However, users want to know what the software is capable of, while contributors need to know where to fix, update, and add features. [Objective] This work aims at understanding how feature knowledge is documented in GitHub projects and how it is linked (if at all) to the source code. [Method] We conducted an in-depth qualitative exploratory content analysis of 25 popular GitHub repositories that provided the documentation artefacts recommended by GitHub’s Community Standards indicator. We extracted strategies used to document software features in textual artefacts and which strategies were used to link the feature documentation with source code. [Results] We observed feature documentation in all studied projects in artefacts such as READMEs, wikis, and website resource files. However, the features were often described in an unstructured way. Additionally, tracing techniques to connect feature documentation and source code were rarely used. [Conclusions] Our results suggest a lacking (or a low-prioritised) feature documentation in open-source projects, little use of normalised structures, and a rare explicit referencing to source code. As a result, product feature traceability is likely to be very limited, and maintainability to suffer over time.</p> <h2>References</h2> <p>The published paper is available on <a href="https://doi.org/10.1109/ICSME55016.2022.00043" target="_blank" rel="noopener">IEEE Xplore</a> and the preprint on <a href="https://doi.org/10.48550/arXiv.2208.01317" target="_blank" rel="noopener">arXiv</a>.</p>
ardoco/benchmark: Release v1.1: SAD-Code TLR, New Project, Inconsistencies, and small Bugfixes
<p dir="auto"><strong>This is not the version of the benchmark from the original ECSA 2022 paper. The original version is located at: <a href="https://doi.org/10.5281/zenodo.6966832">https://doi.org/10.5281/zenodo.6966832</a><br></strong><strong>Please check the Release Notes for further information: <a href="https://github.com/ardoco/benchmark/releases/tag/v1.1">https://github.com/ardoco/benchmark/releases/tag/v1.1</a></strong></p> <p dir="auto"> </p> <p dir="auto">This repository contains a benchmark for traceability link recovery (TLR) between textual Software Architecture Documentation (SAD) and Software Architecture Models (SAM). It was initially published in the paper <a href="https://doi.org/10.1007/978-3-031-36889-9_30" rel="nofollow">Establishing a Benchmark Dataset for Traceability Link Recovery Between Software Architecture Documentation and Models</a>.</p> <p dir="auto">Each project of the benchmark is structured as follows:</p> <ul> <li>The README of each project contains some information about the used languages and lines of code created with <a href="https://github.com/AlDanial/cloc">cloc</a>.</li> <li>The folder <code>model_<year></code> contains the architecture model of the project. <ul> <li>The folder <code>pcm</code> contains a Palladio Component Model (PCM) of the system. It has at least the repository view (components) of the system.</li> <li>The folder <code>uml</code> contains a Papyrus UML model. It is created from the PCM Repository using <a href="https://github.com/InFormALin/PCM2UML">PCM2UML</a>.</li> <li>The folder <code>code</code> contains a code model. The version of the code is stated in a README.md next to the model. The model is an <code>ArDoCo Code Model</code>. The model can be loaded using the <a href="https://github.com/ArDoCo/Core/blob/main/stages/model-provider/src/main/java/edu/kit/kastel/mcse/ardoco/core/models/connectors/generators/code/CodeExtractor.java#L47">ArDoCo Code Extractor</a>.</li> </ul> </li> <li>The folder <code>text_<year></code> contains a documentation of the project. <ul> <li>The text file(s) in the project folder contains the SAD of the projects as plain text.</li> </ul> </li> <li>The folder <code>diagrams_<year></code> contains the informal diagrams of the project.</li> <li>The folder <code>goldstandards</code> contains all gold standards for the project. In the following, the <code><year></code> refers to the year of the artifact. Additional classifiers are added to the end of the file names. You will find more information about these classifiers in the README of the respective project artifacts. <ul> <li>The file <code>goldstandard_sad_<year>-sam_<year>.csv</code> contains the traceability links between SAD and SAM. It links the model elements by id with the sentences by their number</li> <li>The file <code>goldstandard_sad_<year>-sam_<year>_UME.csv</code> contains all IDs of model elements that are contained in the model but not described in the text.</li> <li>The file <code>goldstandard_sad_<year>_code_<year>.csv</code> contains the traceability links between SAD and code models.</li> <li>The file <code>goldstandard_sam_<year>-code_<year>.csv</code> is a gold standard for mapping the architecture elements and code elements.</li> <li>The file <code>goldstandard_sad_id_<year>.json</code> contains the traceability links between SAD and informal diagrams. The JSON schema is specified in the <a href="https://github.com/ardoco/benchmark/blob/main/DiagramSchema.json">DiagramSchema</a> file.</li> </ul> </li> </ul> <div dir="auto"> <h2>Using the benchmark</h2> </div> <p dir="auto">In order to provide an easy approach to use the benchmark, we provide an example TLR approach called <a href="https://github.com/ArDoCo/SimpleTracelinkDiscovery/">Simple Tracelink Discovery (STD)</a> that uses this benchmark in its <a href="https://github.com/ArDoCo/SimpleTracelinkDiscovery/tree/main/src/test/java/io/github/ardoco/simpletracelinkdiscovery/eval">evaluation</a>. Therefore, the benchmark is linked to the STD repository via a <a href="https://github.com/ArDoCo/SimpleTracelinkDiscovery/tree/main/src/test/resources/benchmark">git subtree</a>.</p> <div dir="auto"> <h2>Projects</h2> </div> <div dir="auto"> <h3>BigBlueButton</h3> </div> <p dir="auto">BigBlueButton (BBB) is a non-scientific application that provides a web conferencing system with the focus on creating a "global teaching platform".</p> <div dir="auto"> <h3>MediaStore</h3> </div> <p dir="auto">MediaStore is a "model application built after the iTunes Store". Its architecture was used for exemplary performance analyses on software architecture models.</p> <div dir="auto"> <h3>Teammates</h3> </div> <p dir="auto">TEAMMATES is an open-source "online tool for manageing peer evaluations and other feedback paths of your students".</p> <div dir="auto"> <h3>Teastore</h3> </div> <p dir="auto">Teastore is a scientific application that is used as a "micro-service reference test application".</p> <div dir="auto"> <h3>JabRef</h3> </div> <p dir="auto">JabRef is a tool to manage citations and references in your bibliographies. It has features to collect, organize, cite, and share research work.</p> <div dir="auto"> <h2>LICENSE</h2> </div> <blockquote> <p dir="auto"><strong>Note</strong></p> <p dir="auto">Our LICENSE does only apply to the models and the Gold Standards (CSV & JSON files). The texts and diagrams are licensed w.r.t. to the actual projects. More details about the LICENSE can be found in the README files of the respective texts and diagrams.</p> </blockquote>
Supplemental Figures for: "The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves"
<p>Additional figures for the paper The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves. </p> <h2> </h2> <h2>Interactive Figure Data</h2> <p>Data files used to create the intreactive version of Figure 5 in the publication. There is a version of each file for each line species in the plot (i.e., Hα, Hβ, and MgII).</p> <p><strong>clouds_{line_name}.csv</strong>: A CSV file containing the cloud positions, line-of-sight velocities, and weights. The columns of the file are x [light-day], y [light-day], z [light-day], velocity [km/s], and weight.</p> <p><strong>transfer_function_velocity_{line_name}.csv</strong>: A CSV file containing x-axis of the transfer function panels, the rest-frame velocity.</p> <p><strong>transfer_function_tau_{line_name}.csv</strong>: A CSV file containing the y-axis of the transfer function panels, the rest-frame time delay τ in days.</p> <p><strong>transfer_function_{line_name}.csv</strong>: A CSV file containing the transfer function <span lang="el">Ψ.</span></p> <p> </p> <h2>Model-Related Figures</h2> <p><strong>fitplot_low.pdf</strong>: Same as Figure 4 in the publication, but for the low state.</p> <p><strong>fitplot_high.pdf</strong>: Same as Figure 4 in the publication, but for the high state.</p> <p><strong>geoplot_low.pdf</strong>: Same as Figure 5 in the publication, but for the low state.</p> <p><strong>geoplot_high.pdf</strong>: Same as Figure 5 in the publication, but for the high state.</p> <p><strong>lagplot_low.pdf</strong>: Same as Figure 6 in the publication, but for the low state.</p> <p><strong>lagplot_high.pdf</strong>: Same as Figure 6 in the publication, but for the high state. </p> <p> </p> <h2>Spectral Reduction Method Comparison</h2> <p><strong>spec_decomp_pyqsofit.pdf</strong>: A figure showing the spectral decomposition performed in PyQSOFit for the processed line profiles for Hβ, Hα, and MgII for an example epoch. The total spectrum is shown in black, and each of the decomposed elements are shown, color-coded using the legend above the three panels.</p> <p><strong>input_method_comp.pdf</strong>: A figure showing the processed multi-epoch line profiles for each spectral reduction method (PyQSOFit and PrepSpec). Each column corresponds to a given line (labeled above), and each row corresponds to a given spectral reduction method (labeled on the right). Note that the scales for each panel are different.</p> <p> </p> <h2>Published Value Comparison</h2> <p><strong>pubval_table.pdf</strong>: A table comparing the values obtained for certain physically relevant parameters obtained from our BRAINS modeling to those obtained in Shen et al. (2024). </p> <p> </p> <h2>Joint Posterior Analysis</h2> <p><strong>joint_line_posterior_table.pdf</strong>: A table containing the median values (and their uncertainties) extracted from the joint posteriors for a few key model parameters. These joint posteriors are produced for a given state, across all line species. </p> <p> </p> <h2>Virial Factor Analysis</h2> <p><strong>fcomp.pdf</strong>: A comparison of the virial factor values obtained by using the line dispersion (σ) and FWHM of each of the lines in each of the states.</p> <p><strong>fcorr_table.pdf</strong>: A table showing the correlations between the virial factor and model parameters (i.e., the slopes obtained using <a href="https://github.com/jmeyers314/linmix">LinMix</a> assuming a linear relationship, and the correlation coefficients). Values are given for virial factors obtained using both the line dispersion (σ) and FWHM.</p>
CMIP6 projections
<p>Climate maps (raster layers .tif) of basic-ecvs with a spatial resolution of 5.5 km (1 km for Azores) for different climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (reference, short time-horizon, medium time-horizon, long time-horizon. This v2 includes the metadata.</p>
Dataset for project Lipobodies, related to the development of a new multicomponent process based on the combination of the isonitrile-tetrazine (4+1) cycloaddition and the Ugi four-component reaction
<p>This dataset contains primary (including raw data) that supports the results of the design and development of a new multicomponent process based on the combination of the isonitrile-tetrazine (4+1) cycloaddition and the Ugi four-component reaction</p>
Reanalysis and future wave climate projections of the wave climate of the Gulf of Riga 1993-2100
<h4><strong>Data sets</strong></h4><p>There are two data sets: (1) reanalysis (1993-2021) and (2) future projection (2015-2100).</p><p>The dataset provides gridded monthly mean values of the parameters of the wind waves in the Gulf of Riga, Baltic Sea. The variables of the dataset of the wave field state of the Gulf of Riga are as follows (Long name: <i>acronym</i>, <i>units</i>) </p><ul><li>Mean wave direction: <i>VMDR_WW, </i>°</li><li>Spectral significant wave height: <i>VHM0_WW, m</i></li><li>Spectral moment (0,1) of wave period or mean wave period: <i>VTM01_WW, s</i></li><li>Eastward wave energy flux: <i>WWEFu, W/m</i></li><li>Northward wave energy flux: <i>WWEFv, W/m</i></li></ul><p> </p><p>The grid size of the dataset is 101 (latitude) x 93 (longitude). The horizontal grid spacing is 1 nm. The time resolution of the dataset is monthly – the monthly mean value is provided in the 1st day of the month in the time dimension.</p><p>The original climatic calculations are based on the University of Latvia (UL) set-up of the SWAN model for the Gulf of Riga. The original output of the model run is hourly data series. </p><h4><strong>Reanalysis</strong></h4><p>Time period: 1993-2021, 29 years.</p><p>The main characteristics of the input data and approach for the reanalysis run are as follows: </p><ul><li>EMODNET2020 bathymetry.</li><li>Atmospheric forcing (eastward and northward components of the near surface wind) – ERA5 meteorology.</li><li>Ice conditions – LU HBM, see Frishfelds et. al. 2023.</li><li>Boundary conditions – Baltic Sea Wave Hindcast.</li></ul><h4><strong>Future climate projection</strong></h4><p>Time period: 2015-2100, 86 years.</p><p>The main characteristics of the input data and approach for the future wave climate projections run are as follows: </p><ul><li>Emodnet2020 bathymetry.</li><li>Atmospheric forcing (eastward and northward components of the near surface wind) from downscaled CMIP6 climate projection model NorESM2-MM_ssp585_r1i1p1f1 (search string – project:'CMIP6', source_id:'NorESM2-MM', experiment_id:'ssp585', variant_label:'r1i1p1f1').</li><li>Ice conditions – LU HBM, see Frishfelds et. al. 2023. </li><li>Boundary conditions – fetch model according to Shore protection manual, 1984.</li></ul><h4><strong>References</strong></h4><p>Frishfelds, V., Cepīte-Frišfelde, D., Timuhins, A., Bethers, U., Sennikovs, J., Reanalysis and future climate projections of the physical state of the Gulf of Riga 1993-2100, Zenodo, <a href="https://zenodo.org/doi/10.5281/zenodo.8248942">10.5281/zenodo.8248942</a>, (2023).</p><p>Baltic Sea Wave Hindcast. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). doi: <a href="https://doi.org/10.48670/moi-00014">https://doi.org/10.48670/moi-00014</a>.</p><p>Shore protection manual, Army Corps of Engineers, Coastal Engineering Research Center (CERC), (1984).</p>
Dataset Repository for a Botanical Garden Project: Project Based Learning Assessment from a Blended Approach of PBL with the 5E Model Components
<p><strong>Title:</strong> Botanical Explorers: A Journey Through Our School's Flora - Assessment Data</p><p><strong>Description:</strong> This Excel spreadsheet contains the assessment data for the educational project titled "Botanical Explorers: A Journey Through Our School's Flora", a hands-on science initiative for Grade 9 students at Chalermkwansatree School. The project, conducted under the guidance of Teacher Hasan and aligned with the Additional Science subject focusing on Fuel Energy, is designed to engage students in active learning about local plant life, while developing their research and presentation skills, and fostering environmental appreciation.</p><p>The dataset is part of a comprehensive project contributing 20% to the Term 1, Midterm Score of 50 marks. It encompasses a detailed breakdown of the marks distribution across different tasks such as Data Collection, Book Report, Presentation, and Poster creation, reflecting the multifaceted approach to evaluating student learning and engagement.</p><p><strong>Data Organization:</strong> The spreadsheet is meticulously organized to include:</p><ul><li>A plant list with identifiers like school plant name, location, and space for pictures.</li><li>A marks distribution table indicating the scoring for each project component.</li><li>A timeline for group formation, research, data collection, and submission deadlines.</li><li>Details of the Book Report, Presentation, and Poster requirements.</li></ul><p><strong>Methodology:</strong> Students formed groups to research seven specific plants found within the school premises, examining their identification, classification, ecological roles, growth, and development. The data was collected through a blend of direct observations and scholarly research, ensuring a robust and educational exploration of botany.</p><p><strong>Intended Audience:</strong> The dataset is intended for educational purposes, serving as a valuable resource for educators, students, and researchers interested in project-based learning, botany education, and student assessment methods.</p><p><strong>Usage Notes:</strong> The data provided in this spreadsheet is anonymized, with no personal student information disclosed. It serves as an exemplar model for similar educational initiatives and can be adapted for comparative studies or further educational research.</p><p><strong>Conditions for Use:</strong> The dataset is shared openly with the intention that it will be used for educational and research purposes. Users are requested to cite the dataset appropriately and adhere to any academic and ethical guidelines when utilizing the data for their work.</p>
Nordic trial reporting project: Raw data from EU Clinical Trials Registry (EUCTR) and ClinicalTrials.gov
<p>Uploaded on behalf of the author team for the research project "<strong>Systematic evaluation of clinical trial reporting at medical universities and university hospitals in the Nordic countries</strong>".</p><p><strong>Raw data</strong> from EU Clinical Trials Registry (EUCTR) and ClinicalTrials.gov:</p><p><strong>EUCTR</strong>: We retrieved the latest dataset for the EU Trials Tracker of EUCTR trials on Nov 27, 2022, reflecting data from Nov 7, 2022 (1,2). We also used a custom web scraper that automatically extracts data from EUCTR country protocols and results sections (variables described in Appendix Table 2), developed by the EU Trials Tracker team (2).<br>References: <br>1. Goldacre B, DeVito NJ, Heneghan C, Irving F, Bacon S, Fleminger J, Curtis H. Compliance with requirement to report results on the EU Clinical Trials Register: cohort study and web resource. BMJ. 2018 Sep 12;362:k3218.<br>2. EU Trials Tracker — Who's not sharing clinical trial results? [Internet]. [cited 2022 Aug 30]. Available from: http://eu.trialstracker.net/</p><p><strong>ClinicalTrials.gov</strong>: We downloaded the complete Aggregate Analysis of ClinicalTrials.gov dataset (AACT, http://aact.ctti-clinicaltrials.org/) on Nov 27, 2022, reflecting data from Nov 9, 2022. </p><p>See our GitHub and preregistered protocol for more details:<br>https://github.com/cathrineaxfors/nordic-trial-reporting<br>https://osf.io/97qkv/</p>
Block-level & block group-level income projections for Washington state under different SSPs from 2020 to 2100
<p>The files "bg_binned_income_proj.csv" and "bk_binned_income_proj.csv" contain income projections in 2015 dollars for Washington state (under census geographic boundary 2020) from 2020 to 2100 at the block group and block level, respectively, based on different Shared Socioeconomic Pathways (SSP2, SSP3, and SSP5). The income projections are represented by the projected number of households for each of the three different income bins.</p><p>In the files, GISJOIN is the unique identifier for each block (or block group) . Each number of households projection is stored in a column, where the first four characters of the column name represent the projection year (e.g., 2020, 2030), the following four characters represent the SSP (e.g., SSP2, SSP3, SSP5), and the remaining characters indicate the income bin (Income1 represents annual household income less than $18,150, Income2 represents annual household income between $18,150 and $48,396, and Income3 represents annual household income greater than $48,396). For example, "2020SSP2Income1" indicates the household number projection for the first income bin in 2020 under SSP2.</p><p>"README.txt" describes the general steps for income data generation.</p>
Data for Project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen'
<p>Data for Project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen' consisting of (1) the complete data set of all data analyzed for the project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen' ('Data_Brain-IT-Validation-Qmci_for-publication.xlsx'; and (2) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
Synthesis of [Mim][OTf]-TiO2 catalyst - NCN project OPUS, grant no. 2020/37/B/ST8/00693.
<p>Dataset contains results obtained during the NCN project OPUS, grant no. 2020/37/B/ST8/00693. The file presents the synthetic procedure of [Mim][OTf]-TiO2 catalyst.</p>
Code and data for publication "Assessing carbon cycle projections from complex and simple models under SSP scenarios" published in "Climatic Change"
<p>Data and scripts for the article "Assessing carbon cycle projections from complex and simple models under SSP scenarios" by I. Melnikova, P. Ciais, O. Boucher and K. Tanaka was accepted for publication in Climatic Change (https://doi.org/10.1007/s10584-023-03639-5)</p><p> </p><p>We use bash, CDO, and python.</p><p>SSP2.xlsx contains preprocessed annual estimates of climate and carbon cycle variables from ESMs and SCMs used in the paper.</p><p>Two bash scripts contain preprocessing cdo commands for ESM output.s SCMs were preprocessed directly in python.</p><p>Jupyter notebook (python) contains preprocessing of data and plotting of all figures of the manuscript. The folder "additional" contains some more Excel files needed to run Jupyter-Notebook. Please adapt the folder names.</p><p>If you have any questions, please contact the corresponding author Irina MELNIKOVA at melnikova . irina@nies.go.jp</p><p> </p>
The Genome Project of Trib. Coreopsideae Plants
<p>This project aims to construct the reference genomes of three plants from Trib. Coreopsideae in Asteraceae, including Dahlia pinnata, Cosmos bipinnata and Bidens alba.</p>
1st Newsletter, EVA project
<p>In the first Newsletter of the EVA project, all the updates regarding the first year of the EVA project are described.</p>
Large Spots DeepMIB project, synthetic dataset for testing 2D semantic segmentation
<p>A complete DeepMIB project with a synthetic dataset generated for quick tests of semantic segmentation approaches.<br>The dataset includes a trained DeepLabV3-Resnet18 network for detection of large spots on a black background. </p><p>The network can be opened by loading "2D_LargeSpots_2cl_DeepLabV3.mibCfg" file by</p><ul><li><i>MIB->Menu->Tools->Deep learning segmentation->Options tab->Config files->Load </i></li><li>Drag and drop of the config file into DeepMIB window</li></ul><p>Microscopy Image Browser: <a href="https://mib.helsinki.fi">https://mib.helsinki.fi</a></p>
ScienceDex guides
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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.