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1,916 results for “software,”
Estimated height of the OpenStreetMap buildings of 24 French communes using the GeoClimate Software (version 0.0.1)
<p>This repository contains:</p> <ul> <li>a folder called "_toReproduceResults" containing data, script and methodology to reproduce most of the work described in the research manuscript,</li> <li>the main output of the research work: 24 folders (each of them corresponding to a French city), containing building geometry footprints and their corresponding building height as well as averaged building height value aggregated at rectangular grid cell (100 m by 100 m). The footprint geometries comes from the OpenStreetMap project and the building height has been estimated using a RandomForest algorithm using as independent variables indicators describing the building size and shape and the building environment. The data has been produced using the GeoClimate Software (version 0.0.1).</li> </ul> <p>A more detailed description of the content can be used in the file "Metadata.csv".</p>
Replication data [The who, what and how of the current research at the Brazilian Symposium on Software Engineering]
<p>Replication Data for the SBES paper <em>"The who, what and how of the current research at the Brazilian Symposium on Software Engineering"</em></p> <p>Dataset containing analysis (who, what and how) of 90 SBES papers: 27 from SBES’19, 43 from SBES’20, and 20 from SBES’21.</p>
Dataset of Open-Source Software Developers Labeled by their Experience Level and Associated with their Software Metrics
<p>This dataset contains 703 anonymized developers extracted from 17 open-source projects from GitHub. Projects were chosen because they use:</p> <ul> <li>the Java programming language</li> <li>the <a href="https://spring.io/projects/spring-framework">Spring framework</a></li> <li><a href="https://maven.apache.org/">Maven</a> / <a href="https://gradle.org/">Gradle</a> build tools</li> </ul> <p>For all these developers, 23 software metrics were calculated for each project to which they contribute. These metrics are either calculated by analyzing the source code or relative to project management metadata. Each of these developers then have been manually annotated. To do this, developers have been searched for in professionnal social media such as:</p> <ul> <li><a href="https://www.linkedin.com/">Linkedin</a></li> <li><a href="https://twitter.com/">Twitter</a></li> <li><a href="https://github.com/">Github</a></li> </ul> <p><strong>This dataset is published in the following journal article: </strong></p> <p><strong>Dataset of Open-Source Software Developers Labeled by their Experience Level in the Project and their Associated Software Metrics, Q. Perez, C. Urtado and </strong><strong>S. Vauttier, Data In Brief, </strong></p> <p><a href="https://www.sciencedirect.com/science/article/pii/S2352340922010459">https://www.sciencedirect.com/science/article/pii/S2352340922010459</a></p>
Machine Learning for Software Engineering: A Tertiary Study
<p>Dataset of the research paper: <strong>Machine Learning for Software Engineering: A Tertiary Study</strong></p> <p>Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009–2022, covering 6,117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions including: conducting further empirical validation and industrial studies on ML; reconsidering deficient SE methods; documenting and automating data collection and pipeline processes; reexamining how industrial practitioners distribute their proprietary data; and implementing incremental ML approaches.</p> <p>The following data and source files are included.</p> <ul> <li><strong>review-protocol.md</strong>: The protocol employed in this tertiary study</li> </ul> <p><strong>data/</strong></p> <p><strong> dl-search/</strong></p> <p><strong> input/</strong></p> <ul> <li><strong>acm_comput_surveys_overviews.bib</strong>: Surveys of ACM Computing Surveys journal</li> <li><strong>acm_comput_surveys_overviews_titles.txt</strong>: Titles of surveys</li> <li><strong>acm_comput_ml_surveys.bib</strong>: Machine learning (ML)-related surveys of ACM Computing Surveys journal</li> <li><strong>acm_comput_ml_surveys_titles.txt</strong>: Titles of ML-related surveys</li> <li><strong>dl_search_queries.txt</strong>: Search queries applied to IEEE Xplore, ACM Digital Library, and Elsevier Scopus</li> <li><strong>ml_keywords.txt</strong>: ML-related keywords extracted from ML-related survey titles and used in the search queries</li> <li><strong>se_keywords.txt</strong>: Software Engineering (SE)-related keywords derived from the 15 SWEBOK Knowledge Areas (KAs—except for Computing Foundations, Mathematical Foundations, and Engineering Foundations) and used in the search queries</li> <li><strong>secondary_studies_keywords.txt</strong>: Survey-related keywords composed of the 15 keywords introduced in the tertiary study on SLRs in SE by Kitchenham <em>et al.</em> (2010), and the survey titles, and used in the search queries</li> </ul> <p><strong> output/</strong></p> <ul> <li><strong>acm/</strong> <ul> <li><strong>acm{1–9}.bib</strong>: Search results from ACM Digital Library</li> </ul> </li> <li><strong>ieee.csv</strong>: Search results from IEEE Xplore</li> <li><strong>scopus_analyze_year.csv</strong>: Yearly distribution of ML and SE documents extracted from Scopus's <em>Analyze search results</em> page</li> <li><strong>scopus.csv</strong>: Search results from Scopus</li> </ul> <p><strong> study-selection/</strong></p> <ul> <li><strong>backward_snowballing.csv</strong>: Additional secondary studies found through the backward snowballing process</li> <li><strong>backward_snowballing_references.csv</strong>: References of quality-accepted secondary studies</li> <li><strong>cohen_kappa_agreement.csv</strong>: Inter-rater reliability of reviewers in study selection</li> <li><strong>dl_search_results.csv</strong>: Aggregated search results of all three digital libraries</li> <li><strong>forward_snowballing_reviewer_{1,2}.csv</strong>: Divided forward snowballing citations of quality-accepted studies assessed by reviewer 1 and 2, correspondingly, based on IC/EC</li> <li><strong>study_selection_reviewer_{1,2}.csv</strong>: Divided search results assessed by reviewer 1 and 2, correspondingly, based on IC/EC</li> </ul> <p><strong> quality-assessment/</strong></p> <ul> <li><strong>dare_assessment.csv</strong>: Quality assessment (QA) of selected secondary studies based on the Database of Abstracts of Reviews of Effects (DARE) criteria by York University, Centre for Reviews and Dissemination</li> <li><strong>quality_accepted_studies.csv</strong>: Details of quality-accepted studies</li> <li><strong>studies_for_review.bib</strong>: Bibliography details and QA scores of selected secondary studies</li> </ul> <p><strong> data-extraction/</strong></p> <ul> <li><strong>further_research.csv</strong>: Recommendations for further research of quality-accepted studies</li> <li><strong>further_research_general.csv</strong>: The complete list of associated studies for each general recommendation</li> <li><strong>knowledge_areas.csv</strong>: Classification of quality-accepted studies using the SWEBOK KAs and subareas</li> <li><strong>ml_techniques.csv</strong>: Classification of the quality-accepted studies based on a four-axis ML classification scheme, along with extracted ML techniques employed in the studies</li> <li><strong>primary_studies.csv</strong>: Details of reviewed primary studies by the quality-accepted secondary</li> <li><strong>research_methods.csv</strong>: Citations of the research methods employed by the quality-accepted studies</li> <li><strong>research_types_methods.csv</strong>: Research types and methods employed by the quality-accepted studies</li> </ul> <p><strong>src/</strong></p> <ul> <li><strong>data-analysis.ipynb</strong>: Analysis of data extraction results (data preprocessing, top authors and institutions, study types, yearly distribution of publishers, QA scores, and SWEBOK KAs) and creation of all figures included in the study</li> <li><strong>scopus-year-analysis.ipynb</strong>: Yearly distribution of ML and SE publications retrieved from Elsevier Scopus</li> <li><strong>study-selection-preprocessing.ipynb</strong>: Processing of digital library search results to conduct the inter-rater reliability estimation and study selection process</li> </ul>
Impact of Software Engineering Research in Practice: A Patent and Author Survey Analysis
<p>Dataset of the research paper: <strong>Impact of Software Engineering Research in Practice: A Patent and Author Survey Analysis</strong></p> <p>Existing work on the practical impact of software engineering (SE) research examines industrial relevance rather than adoption of study results, hence the question of how results have been practically applied remains open. To answer this and investigate the outcomes of impactful research, we performed a quantitative and qualitative analysis of 4 354 SE patents citing 1 690 SE papers published in four leading SE venues between 1975–2017. Moreover, we conducted a survey on 475 authors of 593 top-cited and awarded publications, achieving 26% response rate. Overall, researchers have equipped practitioners with various tools, processes, and methods, and improved many existing products. SE practice values knowledge-seeking research and is impacted by diverse cross-disciplinary SE areas. Practitioner-oriented publication venues appear more impactful than researcher-oriented ones, while industry-related tracks in conferences could enhance their impact. Some research works did not reach a wide footprint due to limited funding resources or unfavorable cost-benefit trade-off of the proposed solutions. The need for higher SE research funding could be corroborated through a dedicated empirical study. In general, the assessment of impact is subject to its definition. Therefore, academia and industry could jointly agree on a formal description to set a common ground for subsequent research on the topic.</p> <p>The following data files are included.</p> <ul> <li><em>./fields</em>: <ul> <li><strong>engi-fields.csv</strong>: Publication and PhD dissertation counts of main engineering branches</li> <li><strong>engi-fields-queries.txt</strong>: Queries applied to Elsevier's Scopus and Open Access Theses and Dissertations databases to retrieve the publication and dissertation counts</li> </ul> </li> <li><em>./patents</em>: <ul> <li><strong>sample-se-references-verified.csv</strong>: Manual verification of a random sample of references by software engineering (SE) patents to SE papers</li> <li><strong>se-cpc.tsv</strong>: Manually-identified SE-related Cooperative Patent Classification (CPC) categories</li> <li><strong>se-references-in-patents.csv</strong>: SE references made by SE patents to SE papers</li> <li><em>./patents/litigation</em>: <ul> <li><strong>case-values.csv</strong>: Manually-retrieved litigation damages of citing SE patents</li> <li><strong>lit-per-paper.csv</strong>: Litigation cases of citing SE patents</li> </ul> </li> <li><em>./patents/maintenance</em>: <ul> <li><strong>maint-code-fee-mapping.csv</strong>: Mapping of patent maintenance fee codes to their fee values</li> <li><strong>maint-fees.csv</strong>: Fee values of maintenance fee codes</li> <li><strong>maint-per-paper.csv</strong>: Maintenance fee events of citing SE patents</li> </ul> </li> <li><em>./patents/reports</em>: <ul> <li><strong>lit-sum-per-paper.csv</strong>: Counts and total damages of litigation cases of patent-cited SE papers</li> <li><strong>maint-sum-per-paper.csv</strong>: Counts and total values of maintenance fee events of patent-cited SE papers</li> <li><strong>patent-ref-counts.csv</strong>: SE patent citation counts of patent-cited SE papers</li> </ul> </li> </ul> </li> <li><em>./survey</em>: <ul> <li><strong>emse-top.csv</strong>: Most-cited papers of the Empirical Software Engineering (EMSE) journal</li> <li><strong>icse-bp.csv</strong>: Distinguished papers of the International Conference of Software Engineering (ICSE)</li> <li><strong>icse-mip.csv</strong>: Most influential ICSE papers</li> <li><strong>icse-top.csv</strong>: Most-cited ICSE papers</li> <li><strong>survey-questionnaire-emse.pdf</strong>: The EMSE survey questionnaire</li> <li><strong>survey-questionnaire.pdf</strong>: The ICSE, TSE, and TOSEM survey questionnaire</li> <li><strong>survey-responses.csv</strong>: The anonymized survey responses</li> <li><strong>tosem-top.csv</strong>: Most-cited papers of the ACM Transactions on Software Engineering and Methodology (TOSEM)</li> <li><strong>tse-top.csv</strong>: Most-cited papers of the IEEE Transactions on Software Engineering (TSE)</li> <li><em>./survey/manual-coding</em>: <ul> <li><strong>feedback.txt</strong>: Manual coding of survey feedback</li> <li><strong>practical-impact.csv</strong>: Manual coding of responses about practical impact of work</li> <li><strong>practical-impact-lack.csv</strong>: Manual coding of responses about lack of practical impact</li> <li><strong>research-methods.csv</strong>: Manual coding of additional research methods of surveyed papers</li> <li><strong>state-of-practice.csv</strong>: Manual coding of responses about changes in state of practice</li> </ul> </li> </ul> </li> <li><em>./venues</em>: <ul> <li><strong>se-venues.csv</strong>: Top SE venues according to Google Scholar Metrics</li> <li><strong>se-venues-impact.csv</strong>: SE patent citations and patent-based impact factors of SE venues</li> <li><strong>se-venues-scopus-queries.txt</strong>: Queries applied to Scopus to retrieve the publication counts of the SE venues</li> </ul> </li> </ul>
A Systematic Review on Techniques and Approaches to Estimate Mobile Software Energy Consumption (SUSCOM Dataset)
<p>Dataset and replication data for the systematic review entitled "A Systematic Review on Techniques and Approaches \\to Estimate Mobile Software Energy Consumption".</p>
Dataset: Economical Accommodations for Neurodivergent Students in Software Engineering Education: Experiences from an Intervention in Four Undergraduate Courses
<p>This dataset contains anonymised raw data and examples of accommodations made for neurodiverse students in four undergraduate courses in Computer Science and Software Engineering programmes. The dataset is published as a part of a book chapter in which we report the accommodations.</p> <p>Overall guidelines we followed, including their sources, are contained in <strong>guidelines.md.</strong></p> <p>The raw data for the two surveys is contained in the two Excel files <strong>survey1.xlsx</strong> and <strong>survey2.xlsx</strong>. Free-text answers have been aggregated by neurodiverse and neurotypical students and anonymised, and are available in the files<strong> survey1_freetext_neurodiverse.txt, survey1_freetext_neurotypical.txt, survey2_freetext_neurodiverse.txt, </strong>and<strong> survey2_freetext_neurotypical.txt.</strong></p> <p>The remaining files are examples of the adapted lecture slides and assignment texts. Here, files starting with WEBcourse are from a mandatory undergraduate course on web development, while files starting with SEcourse are from a mandatory undergraduate course giving an overview of Software Engineering.</p>
Perceptions on the utility of community question and answer websites like Stack Overflow to software developers (Replication package)
<p>Interview Questions on the perception of the utility of CQAs like Stack Overflow to software developers. In this study, we focused on the questions highlighted in yellow.</p>
Student's logs and perceptions of an automated assessment tool in a software engineering MOOC specialization
<p>Our dataset contains students' perceptions and usage of an automated assessment tool (MOOCauto) for obtaining formative feedback in software engineering assignments that are part of a MOOC specialization at Universidad Politécnica de Madrid (Spain), delivered by the MiriadaX platform. The dataset has previously been used in a study to evaluate students' perceptions of the tool and to analyze their usage patterns using Growth Mixture Models <a href="https://www.computer.org/csdl/magazine/so/5555/01/10196480/1P9AhkBLYXK">(López-Pernas et al., 2023)</a>. The code of each of the assignments is available on Github: <a href="https://github.com/ging-moocs">https://github.com/ging-moocs</a>.</p> <p>Our dataset contains two files:</p> <h2>MOOCauto usage logs</h2> <p>The first file is called<strong> moocauto_logs.csv </strong>and it contains 9,108 anonymized logs of students' use of the automated assessment tool in the MOOC specialization assignments. The columns of the dataset are as follows:</p> <ul> <li><strong>MOOCid</strong>: Unique numeric identifier for the MOOC (1-4)</li> <li><strong>MOOC: </strong>Name of the MOOC: Frontend Development, Backend Development, Git & Github, Fullstack Development</li> <li><strong>AssignmentName</strong>: Name of the assignment.</li> <li><strong>AssignmentId</strong>: Unique identifier for each assignment (1-17)</li> <li><strong>user: </strong>Unique identifier of the student (it varies per assignment)</li> <li><strong>timestamp: </strong>Time in which the assessment was performed</li> <li><strong>score</strong>: Score obtained (0-10)</li> </ul> <h2>Students' perceptions of MOOCauto</h2> <p>The second file is called <strong>moocauto_questionnaire.csv</strong> and it contains 213 students' responses to the questionnaire conducted at the end of each MOOC in order to evaluate their opinion of the tool and perception on usefulness, ease of use, and other aspects related to the Technology Acceptance Model (TAM). The questions were as follows:</p> <ul> <li><strong>What is your general opinion of MOOCauto?</strong> (1 Horrible - 5 Excellent)</li> <li><strong>Indicate your level of agreement with the following statements </strong>(1 Strongly disagree - 5 Strongly agree) <ul> <li>MOOCauto has been easy to install</li> <li>MOOCauto has been easy to use</li> <li>The feedback provided by MOOCauto was easy to understand</li> <li>The feedback provided by MOOCauto was useful</li> <li>The feedback provided by MOOCauto helped me improve my assignments</li> <li>The documentation Of MOOCauto was useful</li> <li>MOOCauto has increased my motivation to work on the assignments</li> <li>I prefer the feedback from MOOCauto than from peer assessment</li> <li>I would like to have a bot like MOOCauto in other MOOCs</li> </ul> </li> <li><strong>How useful do you perceive the following features of MOOCauto?</strong> (1 Useless - 5 Very useful) <ul> <li>It works locally on my computer</li> <li>It allows to run the test suite as many times as I want</li> <li>It provides instantaneous feedback every time the test suite is executed</li> <li>It has documentation that explains its use and available options</li> </ul> </li> </ul>
SNDATA_ROOT for SNANA software
<p>Environment for the Supernova Analysis software package (https://github.com/RickKessler/SNANA). This environment Includes public light curve data sets, filter transmissions, primary SEDs, calibration files, models for SNIa & CC, cadence and host-galaxy libraries for simulations, etc ...</p>
Replication Package for the Paper Titled "How Well Do Software Practitioners Fix Code Vulnerabilities with Different Types of Explanations?"
<p>This is a replication package for the article 'How Well Do Software Practitioners Fix Code Vulnerabilities with Different Types of Explanations?'. The survey questions can be found here, and we encourage the survey to be re-used.</p> <p>We also include survey data (with demographic data and qualitative responses removed for anonymity reasons).</p> <p>The project team consists of Tracy Hall, Emily Winter, Fahad Al Debeyan (Lancaster University) and Lech Madeyski (Wroclaw University of Science and Technology). If you have any questions about the re-use of this survey, feel free to contact Fahad at <a href="mailto:e.winter@lancaster.ac.uk">f.aldebeyan@lancaster.ac.uk</a>.</p>
Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software
<pre>Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software Dataset structure: <strong>- Dynamic_PEFC_data.h5</strong> # Raw projection data for dynamic tomography imaging of PEFC catalyst hydration. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /humidity_readout # Relative humidity value at the time each projection is measured, 1D array with axis (Nangle). - /Deform_X # X/Y/Z components for the deformation vector field which characterize nonrigid deformation of the sample. - /Deform_Y - /Deform_Z <strong>- liquid_simulation.h5</strong> # Numerical simulation of dynamic liquid filling process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). <strong>- phasetran_simulation.h5</strong> # Numerical simulation of gradual linear density change process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). Reconstruction codes: <strong>- astra_nonrigid.zip</strong> # Compressed python package of modified version of astra-toolbox with nonrigid computed tomography implementation. - /astra # Python package folder, need to be added to Python import search path (sys.path). # If the pre-compiled version doesn't work, source code of the pacakge can be downloaded: # https://github.com/zr-gao/astra-toolbox-nonrigid # Follow the instructions and requirements on the website to compile and install the package. <strong>- reconstruction_PEFC.py</strong> # Python script for sparse dynamic tomography of the PEFC dataset. # Need to be in the same folder with Dynamic_PEFC_data.h5 to load data. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra(with nonrigid)*, h5py # * <strong>!!!</strong> Nonrigid computed tomography is used for the reconstruction, therefore the astra package with nonrigid implementation (in astra_nonrigid.zip) is required. <strong>- reconstruction_simulation.py</strong> # Python script for sparse dynamic tomography of numerical simulations. # Need to be in the same folder with liquid_simulation.h5 or phasetran_simulation.h5, loaded filename is selected in the code. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra**, h5py # ** Reconstruction of numerical simulations does not use nonrigid computed tomography, therefore both the astra_nonrigid.zip and the official astra-toolbox package will work. # To download and install the official astra-toolbox refer to the repository: # https://github.com/astra-toolbox/astra-toolbox</pre>
Data Echoes: Tracking Data Availability and Integrity in Software Engineering Research
<p><strong>This is the dataset of the report: Data Echoes: Tracking Data Availability and Integrity in Software Engineering Research</strong></p> <p>It contains the following information of all the papers from ASE, FSE, and ICSE in 2023:</p> <ul> <li>Paper title</li> <li>Keyword</li> <li>Is the source data available and accessible in the paper?</li> <li>If the source data is not available, do the authors explain why?</li> <li>Hosting platforms</li> <li>Access mode</li> <li>License</li> <li>Is their experiment data reused from previous work, or newly generated specifically for this study, or combination of both? </li> <li>Do the authors change/modify their experiment data before experiment?</li> <li>What modifications do they perform?</li> <li>Does the link provide detailed instructions about how to replicate their paper?</li> <li>Does the link contains their complete experiment data, their source code or other materials that are necessary to replicate their experiments?</li> <li>What's the data format inside the link?</li> <li>What's the content of the link?</li> </ul> <p> </p> <p>This is a course project and I collect the data in a rush.</p> <p>If you want to use this dataset and find any error, please contact me ;-)</p> <p>My email: echo.xiangchen@gmail.com</p>
Survey on the usage of Mathematical Modelling, Simulation and Optimization software
<p>This dataset contains the result of a survey we carried out in the context of the MSO4SC project in order to know which kinds of tools for simulation were using our stakeholders. The purpose was to prioritize functionalities depending on stakeholders' preferences. It was a survey with 41 questions grouped in 10 areas (impact of simulation software on their entities, usage of pre/post-processing, usage of visualization, etc...). The pdf file includes the list of questions for clarification. Such survey was answered by academia and industry from several European countries.</p>
WSSSPE5.1 presentations: Research software sustainability activity analysis
<p>Distribution of actors, actions and actees from <a href="https://www.slideshare.net/danielskatz/research-software-sustainability-wssspe-urssi">Daniel S. Katz' schematic of research software sustainability</a> over presentations given at the Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE5.1) on 6 September 2017 in Manchester, UK (Proceedings: <a href="https://doi.org/10.6084/m9.figshare.c.3869782.v3">https://doi.org/10.6084/m9.figshare.c.3869782.v3</a>).</p>
Long-term moss monitoring network for atmospheric deposition in Germany, link to research data and scientific software
<p>Research data and scientific software related to a study that aims to restructure a long-term monitoring network using moss as biomonitor for atmospheric deposition in Germany. Data from the European Moss Survey 2005 and a statistically based methodology including a decision support system were used to design the spatial network for the 2005 survey.</p>
Umstrukturierung des deutschen Moos-Monitoring-Messnetzes 2015, Link zu Forschungsdaten und wissenschaftlicher Software
<p>Forschungsdaten und wissenschaftliche Software zu einer Studie, welche der Reduzierung des räumlichen Messnetzes des deutschen Moos Monitorings 2005 (728 Standorte) für das Moos Monitoring 2015 (402 Standorte) diente. Die Studie fußt auf einer statistisch begründeten Methodik, welche kombiniert mit einem Entscheidungsmodell für die Standortauswahl 2015 auf Daten des Moos Monitorings 2005 angewendet wurde.</p>
Veränderungen von Waldökosystemen in Deutschland durch Klimawandel und Stickstoffdeposition, Link zu Forschungsdaten und wissenschaftlicher Software
<p>Forschungsdaten und wissenschaftliche Software zu Kartierungen klimainduzierter Veränderungen von Waldökosystemtypen in Deutschland. Die Methiode basiert auf Classification and Regression Trees (CART) und geographischen Informationen zu Klima-, Boden- und Vegetationsmerkmalen. Deutschlandweite Karten der klimawandelbedingten Veränderungen von Waldökosystemtypen für die Zeiträume 1961-1990, 1991-2010, 2011-2040 und 2041-2070 wurden erstellt.</p>
Estimating heavy metal deposition in Germany using model calculations and biomonitoring data, link to research data and scientific software
<p>Research data and scientific software related to an investigation dealing with modelled data on Cd and Pb deposition (LOTOS-EUROS, EMEP/MSC-East) and monitoring data from the International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops (ICP Vegetation Moss Survey) and the German Environmental Specimen Bank (ESB) providing corresponding parameters on HM concentration in various biota. The study aimed at examining, whether an integrated use of model calculations and monitoring data can extend established methods for estimating and evaluating spatial patterns of atmospheric Pb and Cd deposition across Germany.</p>
Biomonitoring-Messnetz für atmosphärische Deposition in deutschen Wäldern, Link zu Forschungsdaten und wissenschaftlicher Software
<p>Forschungsdaten und wissenschaftliche Software einer Studie zur Restrukturierung des Messnetzes für den deutschen Beitrag zum European Moss Survey 2015. Die Methodik basiert auf einem multikriteriellen Entscheidungsmodell, das auf Daten des Moss Survey 2005 angewendet wurde. Die Reduzierung der Monitoringstandorte von 726 auf 402 erfolgte ohne signifikanten Verlust an statistischer Validität und geostatistischer Repräsentativität der gemessenen Elementkonzentrationen in den Moosen.</p> <p> </p> <p> </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.