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8,537 results for “Openness”
Figure 2. from: Unicorn–Open science for assessing environmental state, human health and regional economy - Research Ideas and Outcomes 2: e9232 (16 May 2016) https://doi.org/10.3897/rio.2.e9232
Figure 2. - Time line of the tasks in UNICORN-project
Figure 1. from: Unicorn–Open science for assessing environmental state, human health and regional economy - Research Ideas and Outcomes 2: e9232 (16 May 2016) https://doi.org/10.3897/rio.2.e9232
Figure 1. - Links and interactions between the work packages
OAPEN usage report for open access books funded by the Austrian Science Fund (FWF) 2014 - 2016
<p>For several years now, the Stand-Alone Publications Programme by the Austrian Science Fund (FWF) has been funding the production and simultaneous open-access publication of academic books, see: <a href="http://www.fwf.ac.at/en/research-funding/fwf-programmes/stand-alone-publications/">http://www.fwf.ac.at/en/research-funding/fwf-programmes/stand-alone-publications/</a></p> <p>All FWF-funded books are accessible free of charge in the FWF E-Book Library <a href="https://e-book.fwf.ac.at/">https://e-book.fwf.ac.at/</a> and in the OAPEN Library <a href="http://www.oapen.org/search">http://www.oapen.org/search</a>. </p> <p>The OAPEN Library’s annual data on book usage for 2014 to 2016 shows that the number of archived open-access books is rising steadily, as is the number of downloads per book.</p>
Survey: Open Science in Higher Education
<p>Open Science in (Higher) Education – data of the February 2017 survey</p> <p>This data set contains: </p> <ul> <li>Full raw (anonymised) data set (completed responses) of Open Science in (Higher) Education February 2017 survey. Data are in xlsx and sav format.</li> <li>Survey questionnaires with variables and settings (German original and English translation) in pdf. The English questionnaire was not used in the February 2017 survey, but only serves as translation.</li> <li>Readme file (txt)</li> </ul> <p>Survey structure</p> <p>The survey includes 24 questions and its structure can be separated in five major themes: material used in courses (5), OER awareness, usage and development (6), collaborative tools used in courses (2), assessment and participation options (5), demographics (4). The last two questions include an open text questions about general issues on the topics and singular open education experiences, and a request on forwarding the respondent’s e-mail address for further questionings. The online survey was created with Limesurvey[1]. Several questions include filters, i.e. these questions were only shown if a participants did choose a specific answer beforehand ([n/a] in Excel file, [.] In SPSS). </p> <p>Demographic questions</p> <p>Demographic questions asked about the current position, the discipline, birth year and gender. The classification of research disciplines was adapted to general disciplines at German higher education institutions. As we wanted to have a broad classification, we summarised several disciplines and came up with the following list, including the option “other” for respondents who do not feel confident with the proposed classification:</p> <ul> <li>Natural Sciences</li> <li>Arts and Humanities or Social Sciences</li> <li>Economics</li> <li>Law</li> <li>Medicine</li> <li>Computer Sciences, Engineering, Technics</li> <li>Other</li> </ul> <p>The current job position classification was also chosen according to common positions in Germany, including positions with a teaching responsibility at higher education institutions. Here, we also included the option “other” for respondents who do not feel confident with the proposed classification:</p> <ul> <li>Professor</li> <li>Special education teacher</li> <li>Academic/scientific assistant or research fellow (research and teaching)</li> <li>Academic staff (teaching)</li> <li>Student assistant</li> <li>Other</li> </ul> <p>We chose to have a free text (numerical) for asking about a respondent’s year of birth because we did not want to pre-classify respondents’ age intervals. It leaves us options to have different analysis on answers and possible correlations to the respondents’ age. Asking about the country was left out as the survey was designed for academics in Germany.</p> <p>Remark on OER question</p> <p>Data from earlier surveys revealed that academics suffer confusion about the proper definition of OER<sup><sup>[2]</sup></sup>. Some seem to understand OER as free resources, or only refer to open source software (Allen & Seaman, 2016, p. 11). Allen and Seaman (2016) decided to give a broad explanation of OER, avoiding details to not tempt the participant to claim “aware”. Thus, there is a danger of having a bias when giving an explanation. We decided not to give an explanation, but keep this question simple. We assume that either someone knows about OER or not. If they had not heard of the term before, they do not probably use OER (at least not consciously) or create them. </p> <p>Data collection</p> <p>The target group of the survey was academics at German institutions of higher education, mainly universities and universities of applied sciences. To reach them we sent the survey to diverse institutional-intern and extern mailing lists and via personal contacts. Included lists were discipline-based lists, lists deriving from higher education and higher education didactic communities as well as lists from open science and OER communities. Additionally, personal e-mails were sent to presidents and contact persons from those communities, and Twitter was used to spread the survey.</p> <p>The survey was online from Feb 6<sup>th</sup> to March 3<sup>rd </sup>2017, e-mails were mainly sent at the beginning and around mid-term. </p> <p>Data clearance</p> <p>We got 360 responses, whereof Limesurvey counted 208 completes and 152 incompletes. Two responses were marked as incomplete, but after checking them turned out to be complete, and we added them to the complete responses dataset. Thus, this data set includes 210 complete responses. From those 150 incomplete responses, 58 respondents did not answer 1st question, 40 respondents discontinued after 1st question. Data shows a constant decline in response answers, we did not detect any striking survey question with a high dropout rate. We deleted incomplete responses and they are not in this data set.</p> <p>Due to data privacy reasons, we deleted seven variables automatically assigned by Limesurvey: submitdate, lastpage, startlanguage, startdate, datestamp, ipaddr, refurl. We also deleted answers to question N<sup>o</sup> 24 (email address).</p> <p>References</p> <p>Allen, E., & Seaman, J. (2016). <em>Opening the Textbook: Educational Resources in U.S. Higher Education, 2015-16.</em></p> <p> </p> <p>First results of the survey are presented in the poster:</p> <p>Heck, Tamara, Blümel, Ina, Heller, Lambert, Mazarakis, Athanasios, Peters, Isabella, Scherp, Ansgar, & Weisel, Luzian. (2017). Survey: Open Science in Higher Education. Zenodo. http://doi.org/10.5281/zenodo.400561</p> <p>Contact:</p> <p>Open Science in (Higher) Education working group, see http://www.leibniz-science20.de/forschung/projekte/laufende-projekte/open-science-in-higher-education/.</p> <p> </p> <p>[1] https://www.limesurvey.org</p> <p>[2] The survey question about the awareness of OER gave a broad explanation, avoiding details to not tempt the participant to claim “aware”. </p>
An open-access CMIP5 pattern library for temperature and precipitation: Description and methodology.
<p>The pattern library is available on GitHub through the Joint Global Change Research Institution repository (https://github.com/JGCRI/CMIP5_patterns). GitHub is a web-based version control repository and Internet hosting service which uses git concepts and commands. The purpose of creating this pattern library was to allow for researchers across various fields to be able to efficiently use the statistical patterns generated by the described regression method to examine model response to change in global mean temperature for all the available CMIP5 models (41 models, at present). We also further intend for those patterns to be easy to scale using a scaler generated from a SCM of ones choosing.</p> <p>The pattern library contains patterns, generated by the least squared regression methodology, for the first realization of the 41 CMIP5 climate models. Annual, seasonal, and monthly patterns are provided for surface temperature and precipitation. For temperature patterns, units in Celsius were used as it is the standard temperature units for impact analysis.</p> <p>Included in each netCDF file for each model is:<br> 1. The individual model pattern (2-D);<br> 2. The adjusted R2 between the predictor and dependent terms (2-D);<br> 3. The standard error of the estimated regression coefficient (2-D);<br> 4. A historical climatology based on the 1961-1990 average from each model (2-D), which can be used to construct absolute values at time X;</p> <p>5. The 95th percentile confidence level pattern for model parameters.</p> <p>The patterns from all 41 CMIP5 models range in size (165 kB to 1 MB) due to spatial resolution, but all patterns were kept at the native resolution of the dependent variable and no regridding of input/output variables were done. This was done to retain model specific information, which may have been lost if regridded to a common spatial resolution.</p> <p>All source code used to produce patterns is available in the aforementioned repository. Source code is written in NCAR Command Language (Version 6.3.0; http://dx.doi.org/10.5065/D6WD3XH5).</p>
OD-1 Open Data Decision Tree Version 0.4
<p>This is a prototype/proof of concept upload to Zenodo to test and demonstrate the concept of working in a 'Commons Compliant' way. This involves conducting all stages of research and research communication openly (Open by Design and Open by Default). </p> <p>Decision trees record a set of actions that can be taken with scholarly objects to make them as Open, FAIR and Citable as possible. They represent the most practical framework for putting the Principles of the Scholarly Commons into practice.</p> <p>This tree was uploaded using a Draw.io - Dropbox - Zenodo workflow.</p> <p> </p>
Mandelbugs in Open-Source Software
<p>This dataset contains a list bugs from four open-source projects (the Linux kernel, the MySQL DBMS, the Apache HTTPD web server, and the Apache AXIS WS framework). The bugs have been classified into Mandelbugs, Bohrbugs, or Aging-Related Bugs, by analyzing the conditions that exercise the bug (i.e., the "fault trigger"). This classification is useful to get insights into bugs and failures that can occur in OSS projects, and to tune testing and fault-tolerance strategies according to the distribution of bug types in a project.</p> <p>The dataset contains an ARFF file for each subsystem of the four open-source projects. Each row of the ARFF file contains:</p> <p>- An IDs of the bug, which can be used to retrieve more information about the bug from the issue tracker of the project;</p> <p>- A string that represents the class of the bug (BOH = Bohrbug; NAM = Mandelbug; ARB = Aging-Related Bug; UNK = Unknown class);</p> <p>- A string that represents the sub-class of the bug (for Bohrbugs, the sub-class is not available; the subclasses for Mandelbugs are LAG, ENV, TIM, SEQ; the subclasses for Aging-Related bugs are MEM, STO, LOG, NUM, TOT).<br> </p>
Austrian Science Fund (FWF) Open Access Compliance Monitoring 2016
<p><strong>I. Executive summary</strong></p> <p>The Austrian Science Fund (FWF), which is Austria's main funding organisation for basic research, encourages and helps all project leaders and project staff members to make their peer-reviewed research results freely available on the Internet. Since 1 January 2016, open access has been mandatory for all peer-reviewed publications listed in final reports on FWF-funded projects. Any exceptions must be clearly indicated and justified. For projects that started after 1 January 2015, open access has been compulsory for all peer-reviewed publications.</p> <p>All principal investigators in FWF-funded projects are obliged to submit a final report within three months of completing their projects.</p> <p>The publications and other data mentioned in those reports are archived and analysed by the FWF. This report examines the state of compliance with open access requirements on the basis of final project reports submitted in the year 2016.</p> <p>Main findings:</p> <ul> <li>A total of 6,994 publications were listed in the final project reports submitted in 2016.</li> <li>Of those publications, 5,146 were conclusively identified as peer-reviewed.</li> <li>Regarding compliance with the FWF's Open Access Policy, the report shows that 92% of the peer-reviewed publications that arose from FWF projects in 2016 are openly accessible (2015: 83%).</li> <li>The most frequently chosen option is hybrid open access (40%). Since last year's report, the use of green open access has increased by 3%, other open access by 5%, and gold open access by 2%.</li> <li>The majority of peer-reviewed publications submitted are journal articles, 93% of which are openly accessible.</li> <li>The lowest rate of compliance with the FWF's Open Access Policy can be found in editions, contributions to edited volumes and monographs (58%).</li> <li>Although open access is not compulsory, 48% of non peer-reviewed publications are freely available through the internet.</li> <li>The absolute number of publications listed in final reports in 2016 includes 321 publications that are mentioned several times in different projects; however, those repetitions do not ultimately affect the relative share of open access publications (92%).</li> </ul> <p><strong>II. Bias</strong></p> <p>All publications were entered in the FWF’s database manually and with the utmost care when the final project reports were received. Nevertheless, it is not possible to rule out minor errors entirely. Moreover, some systematic problems need to be solved in the future; the FWF is currently preparing to implement a research information system in which FWF-funded researchers will be able to enter and update their research data on an ongoing basis.</p> <ul> <li>Due to the amount of data involved, it was not possible to check the current open access status of all publications twice. The last check was performed in January 2017.</li> <li>In some cases, the embargo period may have expired after submission, and publications labelled "Other open access" in the FWF’s database may now be accessible through green open access.</li> <li>Whenever the status "peer-reviewed" could not be clearly identified according to FWF guidelines, the publications were classified as non peer-reviewed.</li> </ul>
Biotea Linked Data for Open Access Full text PubMed Central
<p>this is the dataset from a-b</p>
IPBES Invasive Alien Species Assessment in Linked Open Data format
<p>This dataset contains the Thematic Assessment Report on Invasive Alien Species and their Control of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services in linked open data format.</p> <p>The structure of the file follows the IPBES ontology version 06: <a href="https://github.com/IPBES-Data/IPBES_Ontology">https://github.com/IPBES-Data/IPBES_Ontology</a></p> <p>The report is published in 2023 and consists of 6 chapters and a Summary for Policy Makers. For more information about the report, see: <a href="https://www.ipbes.net/ias" rel="nofollow">https://www.ipbes.net/ias</a></p> <p>For any questions and enquiries, please contact the IPBES Data and Knowledge Unit <a href="mailto:aidin.niamir@senckenberg.de">aidin.niamir@senckenberg.de</a></p>
IPBES Values Assessment in Linked Open Data format
<p>This dataset contains the Methodological Assessment Report on the Diverse Values and Valuation of Nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services in linked open data format.</p> <p>The structure of the file follows the IPBES ontology version 06: <a href="https://github.com/IPBES-Data/IPBES_Ontology">https://github.com/IPBES-Data/IPBES_Ontology</a></p> <p>The report is published in 2022 and consists of 6 chapters and a Summary for Policy Makers. For more information about the report, see: <a href="https://www.ipbes.net/the-values-assessment">https://www.ipbes.net/the-values-assessment</a></p> <p>For any questions and enquiries, please contact the IPBES Data and Knowledge Unit <a href="mailto:aidin.niamir@senckenberg.de">aidin.niamir@senckenberg.de</a></p>
Komplementäres Datenmodell für das Open-Source-Tool OptIES
<p>Der hier verfügbare Datensatz stellt das komplementäre Datenmodell zum Open-Source-Tool <a href="https://github.com/znes/OptIES" target="_blank" rel="noopener">OptIES</a> dar. </p> <p>Das Tool <code>OptIES</code> dient der Optimierung regionaler Energiesysteme und wurde im Rahmen des Forschungsprojekts "OptIES Dörpum - Offene Optimierung sektorgekoppelter regionaler Energiesysteme am Beispiel des IES Dörpum" entwickelt. Es basiert auf der offenen Software-Toolbox <a href="https://github.com/PyPSA/PyPSA" target="_blank" rel="noopener">PyPSA</a>.</p> <p>Das Forschungsprojekt <a href="https://www.uni-flensburg.de/eum/forschung/laufende-projekte/opties-doerpum" target="_blank" rel="noopener">OptIES Dörpum</a> wurde durch die <a href="https://www.uni-flensburg.de/" target="_blank" rel="noopener">Europa-Universität Flensburg</a> und die <a href="https://www.ecowert360.com" target="_blank" rel="noopener">EcoWert360° GmbH</a> bearbeitet und durch das Förderprogramm HWT Energie und Klimaschutz der <a href="https://www.eksh.org/" target="_blank" rel="noopener">Gesellschaft für Energie und Klimaschutz Schleswig-Holstein (EKSH)</a> finanziert. Es stellte die wissenschaftliche Begleitung des Praxisprojekts <a href="https://www.aktivregion-nf-nord.de/fileadmin/user_upload/KT_Klimawandel_Energie/Projekte/IES_D%C3%B6rpum/07.51_-_Beschreibung_-_Projekt_57_IES_D%C3%B6rpum.pdf" target="_blank" rel="noopener">IES Dörpum</a> dar.</p> <p>Ziel des Forschungsprojekts war es, lokale sowie nationale Herausforderungen der Energiewende durch Einordnung der kommunalen Bestrebungen in das nationale Energiesystem zu beleuchten und analysieren. Das vorliegende Tool dient der Optimierung des regionalen Energiesystems zur Untersuchung und Bewertung von Betriebsstrategien und Entwicklungspfaden des isolierten Systems. Im weiteren Projektverlauf wurde das regionale System unter Berücksichtigung des übergelagerten Gesamtsystems optimiert, um integrierte Handlungsempfehlungen für die erfolgreiche Energiewende auf kommunaler und nationaler Ebene abzuleiten.</p> <p>Unter den hochgeladenen Dateien findet sich der Projektabschlussbericht mit einer ausführlichen Darstellung des Projekts, der entwickelten Modelle und Tools, der durchgeführten Berechnungen und Analysen sowie der Ergebnisse. Zudem werden die im Rahmen des Projekts angefertigten Masterarbeiten von Matthias Winschu und Mohsen Mansouri bereitgestellt.</p> <p>Die hier veröffentlichten Daten enthalten Informationen zu Verbrauchs- und Erzeugungseinheiten innerhalb des IES Dörpum, welche für Modellrechnungen mit der Software-Toolbox notwendig sind. Die Daten enthalten sowohl synthetische als auch reale Daten in anonymisierter Form. Es werden die innerhalb von <code>PyPSA</code> als Standard definierten Einheiten verwendet (also z. B. MW). Für die Erstellung der synthetische Zeitreihen ist das Jahr 2019 als Referenzjahr herangezogen. </p> <p>Zur Erstellung der synthetischen Lastzeitreihen (Strom und Wärme) wurden Jahresgesamtverbräuche der letzten Jahre herangezogen und basierend auf der Arbeit von <a href="https://doi.org/10.1186/s42162-022-00201-y">Büttner et al.</a> zeitlich verteilt. Zur Erstellung der synthetischen Lastzeitreihen aus Ladevorgängen von Elektrofahrzeugen wurden Daten der Studie <a href="https://www.mobilitaet-in-deutschland.de/">Mobilität in Deutschland</a> innerhalb des Tools <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-mobility</a> verwendet. Die synthetische Zeitreihe der potentiellen Erzeugung durch PV ist mithilfe <a href="https://www.renewables.ninja/">renewables_ninja</a> erzeugt. Alle weiteren Daten zur Parametrisierung der Komponenten sowie die synthetische Gaslast und Eigenverbräuche der enthaltenen Biogasanlage sind aufgrund von Angaben der Biogas Dörpum GmbH festgelegt und mithilfe der Daten der <a href="https://github.com/PyPSA/technology-data">PyPSA technology data</a> ergänzt (insbesondere Kostenannahmen).</p> <p>Die gemessenen Zeitreihen ergeben sich aus dem Zeitraum Mitte Februar 2023 bis Mitte Febraur 2024. Aufgrund von Hard- und Softwarefehlern stehen keine lückenlosen Messungen zur Verfügung, insbesondere die Messungen der Ladevorgänge durch E-Fahrzeuge sind beeinträchtigt. Eine Übersicht über die Verfügbarkeit der gemessenen Daten ist der Abbildung <strong>Verfuegbarkeit_Messungen.png</strong> zu entnehmen.</p> <p><strong>buses.csv - </strong>enthält Informationen, wie Name, Energieträger und wenn anwendbar, die Spannungsebene, der verschiedenen im Modell abgebildeten Knoten. </p> <p><strong>generators.csv - </strong>stellt alle Erzeugungseinheiten mit ihren zugehörigen Knoten, installierten Kapazitäten und Kostenparametern dar. </p> <p><strong>lines.csv - </strong>beschreibt Verlauf, Länge und Art der verbauten Kabel und die zugehörigen elektrischen Parameter. </p> <p><strong>links.csv - </strong>liefert Informationen zu allen kontrollierbaren Komponenten, die zwei Knoten zweier beliebiger Energieträger verbinden. Im vorliegenden Beispiel können das KWK-Anlagen sowie Be- und Entladeeinheiten für Wärmespeicher sein. </p> <p><strong>loads.csv - </strong>beschreibt alle Verbrauchseinheiten mit deren Energieträger und zugehörigem Knoten<strong>. </strong></p> <p><strong>storage_unit.csv - </strong>stellt die vorhandenen Batteriespeicher mit Kapazitäten, Kosten- und Effizienzparametern dar. </p> <p><strong>stores.csv - </strong>enthält Informationen zu anderen Speichereinheiten für Wärme oder Biogas mit Kapazitäten, Kosten- und Effizienzparametern.</p> <p><strong>el_load_synth.csv - </strong>stellt synthetisch erzeugte stündliche elektrischen Lastzeitreihen für alle im Model abgebildeten Anschlussnehmer bereit. Unter 'BGA_AC' ist der elektrische Eigenverbrauch der Biogasanlage angenommen. 'LS1' und 'LS2' beinhalten synthetische elektrische Lastzeitreihen zum Verbrauch durch Ladevorgänge von Elektrofahrzeugen unter der Annahme von zehn ('LS1') und fünf ('LS2') Nutzer*innen.</p> <p><strong>ev_load_synth.csv - </strong>stellt zusätzliche, synthetische elektrische Lastzeitreihen zum Verbrauch durch Ladevorgänge von Elektrofahrzeugen bereit. Die verschiedenen Zeitreihen variieren in der Annahme der Anzahl der Nutzer*innen, diese ist in dem Spaltentitel vermerkt.</p> <p><strong>heat_load_synth.csv -</strong> hält synthetische Daten zum Wärmeverbrauch des angeschlossenen Nahwärmenetzes und zum Eigenverbrauch der Biogasanlage in stündlicher Auflösung bereit. Weiterhin ist der Wärmeeigenbedarf der Biogasanlage angenommen.</p> <p><strong>gas_load_synth.csv -</strong> liefert stündliche synthetische Daten zum Gasverbrauch eines Satelliten-BHKWs. </p> <p><strong>pot_pv_timeseries_synth.csv - </strong>enthält Daten zu potenziellen Stromeinspeisezeitreihen aus Photovoltaik in stündlicher Auflösung. </p> <p><strong>el_load_real.csv - </strong>stellt real gemessene elektrische Lastzeitreihen für alle im Model abgebildeten Anschlussnehmer in einer zeitlichen Auflösung von fünf Minuten bereit. Bei Messlücken größer als fünf Minuten wurde interpoliert. Unter 'BGA_AC' ist der elektrische Eigenverbrauch der Biogasanlage angenommen. 'LS1' und 'LS2' beinhaltet die selbe gemessene elektrische Lastzeitreihe zum Verbrauch durch Ladevorgänge von Elektrofahrzeugen. Diese wurde bei Messlücken >1h interpoliert. </p>
Crossref open citations
<p>This text file contains citations between documents classified as journal article, book content, conference paper, or preprint in Crossref. Each line in the file contains the DOI of a citing document followed by the DOI of a cited document. There are 147,161,531 documents classified as journal article, book content, conference paper, or preprint in Crossref. There are 1,723,702,594 citations between these documents in Crossref. The data has been extracted from Crossref’s XML Metadata Plus Snapshot downloaded on February 10, 2025.</p>
Open-Source Cardiac MR Fingerprinting
<p>Magnetic Resonance (MR) raw data acquired with an open-source cardiac MR Fingerprinting (cMRF) sequence of a phantom at four different MR scanners. More details can be found here: https://github.com/PTB-MR/cMRF. The colormaps are taken from https://zenodo.org/records/11185704 because zenodo_get failed on trying to download this record in a jupyter notebook.</p> <p>Additionally cMRF data was acquired in three volunteers who were scanned at two different scanners. Cartesian and golden radial cine data was acquired to verify the anatomical features seen in the quantitative maps.</p>
IAM_COMPACT_Open_access_models_for_case_study_countries_datasets
<p>This dataset contains the excel files with all required input data to run OSeMOSYS model for Ukraine, CLEWs model for Kenya, Sri Lanka, and Ethiopia, and OnSSET model model for Ethiopia. Model factsheets, their scope and structure as well as preliminary results are documented in <em>D6.7 - Open-access models for case-study countries</em> (<a href="../doi/10.5281/zenodo.13643021">link</a>). The dataset also features zip files that can be run following the instructions on the report for each model. </p> <p>The preliminary versions of each model were created as part of the capacity development activities of <a href="https://iam-compact.eu/">IAM COMPACT</a> Horizon Europe project. </p> <p>The update of models is documented in <em>D6.8 - Open-access models for case-study countries - Update</em> and is supplemented by Version 2 of this dataset.</p>
GPT-4 Shows Comparable Performance to Human Examiners in Ranking Open-Text Answers
<p>This repository contains all datasets used in the research paper:<br>"GPT-4 Shows Comparable Performance to Human Examiners in Ranking Open-Text Answers"</p> <p>Files and Descriptions</p> <ol> <li> <p>Data_complete_Can_GPT_Replace_Human_Examiners.xlsx<br>Contains two sheets:</p> </li> </ol> <ul> <li> <p>Data – Main analysis dataset for Ranking and Point Assessment of Five Answers.</p> </li> <li> <p>Robustness & Extensions – Data for Robustness & Extensions, covering Ranking and Point Assessment of Five Answers.</p> </li> </ul> <ol> <li> <p>point_assessment_single_answer_all_robustness_score.xlsx<br>Contains all data related to robustness checks for Point Assessment of Single Answers.</p> </li> <li> <p>point_assessment_single_answer.xlsx<br>Contains the main dataset for Point Assessment of Single Answers (excluding robustness checks).</p> </li> </ol> <p> </p> <p>Note: Any variable termed GPT is output of the model as under <a href="https://openai.com/policies">OpenAI Terms & policies.</a></p> <p> </p>
Dataset: A continuous open source data collection platform for architectural technical debt assessment
<p>The dataset and replication package of the study "A continuous open source data collection platform for architectural technical debt assessment".</p> <p> </p> <p>Abstract</p> <p>Architectural decisions are the most important source of technical debt. In recent years, researchers spent an increasing amount of effort investigating this specific category of technical debt, with quantitative methods, and in particular static analysis, being the most common approach to investigate such a topic.</p> <p> </p> <p>However, quantitative studies are susceptible, to varying degrees, to external validity threats, which hinder the generalisation of their findings.</p> <p>In response to this concern, researchers strive to expand the scope of their study by incorporating a larger number of projects into their analyses. This practice is typically executed on a case-by-case basis, necessitating substantial data collection efforts that have to be repeated for each new study.</p> <p> </p> <p>To address this issue, this paper presents our initial attempt at tackling this problem and enabling researchers to study architectural smells at large scale, a well-known indicator of architectural technical debt. Specifically, we introduce a novel approach to data collection pipeline that leverages Apache Airflow to continuously generate up-to-date, large-scale datasets using Arcan, a tool for architectural smells detection (or any other tool).</p> <p>Finally, we present the publicly-available dataset resulting from the first three months of execution of the pipeline, that includes over 30,000 analysed commits and releases from over 10,000 open source GitHub projects written in 5 different programming languages and amounting to over a billion of lines of code analysed.</p>
Sample Stripped Pre-supernova Progenitors for open-source code CHIPS (Complete History for Interaction-Powered Supernovae)
<p>Inlists, mainly based on the test suite "example_make_pre_ccsn" in r12778, with slight amendments for removal of hydrogen (and helium, for Ic progenitors) envelope at core hydrogen (helium) exhausion.</p><p>For details: https://ui.adsabs.harvard.edu/abs/2023arXiv230810785T/abstract</p>
Opening the museum's vault: Historical field records preserve reliable ecological data
<p><span>Museum specimens have long served as foundational data sources for ecological, evolutionary, and environmental research. Continued reimagining of museum collections is now also generating new types of data associated with, but beyond physical specimens, a concept known as "extended specimens". Field notes penned by generations of naturalists contain first-hand ecological observations associated with museum collections and comprise a form of extended specimens with the potential to provide novel ecological data spanning broad geographic and temporal scales. Despite their data-yielding potential, however, field notes remain underutilized in research due to their heterogeneous, unstandardized, and qualitative nature. We introduce an approach for transforming descriptive ecological notes into quantitative data suitable for statistical analysis. Tests with simulated and real-world published data show that field notes and our transformation approach retain reliable quantitative ecological information under a range of sample sizes and evolutionary scenarios. Unlocking the wealth of data contained within field records could facilitate investigations into the ecology of clades whose diversity, distribution, or other demographic features present challenges to traditional ecological studies, improve our understanding of long-term environmental and evolutionary change, and enhance predictions of future change.</span></p>
Direct observation of chirality-induced spin selectivity in electron donor–acceptor molecules. Open data set
<p>Data supporting the original figures 2 and 4 of the related publication.</p>
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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.