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198 results for “open systems”
Data for "Breaking the Paywall: The role of Open Journal System as key Open Science infrastructure"
<h3><strong>Context</strong></h3> <p>This research was conducted within the NSF-SEEKCommons Project, a research initiative dedicated to supporting Open Science and Open Access in disciplinary research. The project has a special interest in understanding the role that critical infrastructure has in supporting open initiatives. The Open Journal System (OJS) serves as a long-standing fundamental piece for Open Access throughout the globe. Hence, it provides valuable information about experiences developing, deploying, and maintaining open technologies. </p> <h3><strong>Methods<br></strong></h3> <div> <div>We used mixed methods for our research, triangulating repository data, installation data, interviews, and documentary analysis. We collected repository data using a report generator (Kopp [2018] 2024) that uses repository metadata to present general statistics about a Git project. The resulting information was manually curated, disambiguated, and annotated to have a homogeneous set of developers with information about their institutional affiliation and country. </div> <div> </div> <div>Names are normalized based on the information in qualitative interviews and by browsing the full-extent commits in the GitHub repository. Other sources for this were the institutional materials (available in current and archived versions of the PKP website), meeting minutes, the user forum, and further project documentation available online. GitHub handles are homologated to their most comprehensive version. For institutional and country affiliation, we resorted to GitHub profiles, PKP documentation and forums, institutional domains available in emails, and researchers' ORCID IDs. </div> </div> <h3><strong>Available files</strong></h3> <ol> <li><strong>Information about the codebase</strong> (number of files, lines of code, and timestamp) organized by <strong>month, quarter, and semester. </strong><br>See file: OJS_GitStats_04-24.csv</li> <li>Information about the historical evolution of the codebase (number of files, lines of code, and timestamp), including <strong>a description of the top committers for each month</strong>. Commiters are described by including their institutional affiliation and country of origin. <br>See file: OJS_DevStats_Institution-Country_1.tsv</li> <li>Information about the <strong>historical evolution of the codebase </strong>focusing on <strong>top committers</strong>, along with their institution and country. This file is formatted to map the co-occurrence of developers and attributes by month between 2004-2024.<br>See file: OJS_DevStats_Institution-Country_2.tsv</li> <li>Selected fields to describe<strong> working and regularly maintained plugins for OJS as of October 2024.</strong> Includes name of the plugin, homepage, description, maintainer, and institutional affiliation. <br>See file: OJS_Plugins_2024_Processed.tsv</li> <li>Details of the aggregated <strong>information</strong> included in <strong>Table</strong> <strong>5</strong> of the article.<br>See file: OJS_Plugins_2024_Table5.tsv</li> <li><strong>Snapshot</strong> to XML information of the <strong>plugin gallery of OJS </strong>(October 21) retrieved from PKP website (Smecher 2024)<br>See file: OJS_Plugins_2024.csv</li> </ol> <h3>Funding</h3> <p><span>The SEEKCommons Project is funded by the U.S. National Science Foundation (NSF), grant #2226425</span></p>
Raw data to "Series expansions in closed and open quantum many-body systems with multiple quasiparticle types"
<p>This collection of data is complementary to the publication "Series expansions in closed and open quantum many-body systems with multiple quasiparticle types", Lea Lenke, Andreas Schellenberger, Kai Phillip Schmidt, <a href="https://arxiv.org/abs/2302.01000">arXiv:2302.01000</a> (<a href="https://arxiv.org/abs/2302.01000">https://arxiv.org/abs/2302.01000</a>).</p> <p>It contains all data used for Figure 2 given in the file `Figure_2_complementary_data.yaml` and all needed data to recalculate the energies of the visualized modes in the files `Figure_2_coefficients_expectation_values.yaml` and `Figure_2_broad_signum_coefficients_expectation_values.yaml`.</p> <p>For the last two files, we used a program to calculate the coefficients. The source code for coefficient calculation is openly available under GitHub (<a href="https://github.com/FAU-kpslab/pcstpp_CoefficientGenerator">https://github.com/FAU-kpslab/pcstpp_CoefficientGenerator</a>) including configuration files to reproduce the coefficients given here.</p> <p>All files are self-consistent, for further information we recommend the comments directly in the files.</p> <p>For further details on the used method pcst<sup>++ </sup>and discussion of the results we refer to the linked publication.</p> <p>If any question may arise, you are highly welcome to contact us (see e.g. contact information on the publication).</p>
Weather Data Cutouts for PyPSA-Eur: An Open Optimisation Model of the European Transmission System
<p><strong>PyPSA-Eur</strong> is an open model dataset of the European power system at the transmission network level that covers the entire ENTSO-E area. It can be built using the code provided at <a href="https://github.com/PyPSA/PyPSA-eur">https://github.com/PyPSA/PyPSA-eur</a>.</p> <p><strong>It contains</strong> alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.</p> <p><strong>Not all data dependencies</strong> are shipped with the <a href="https://github.com/PyPSA/PyPSA-eur">code repository</a> since git is not suited for handling large changing files. Instead, we provide separate data bundles and cutouts to be downloaded and extracted, as noted in the documentation.</p> <p>The provided <strong>cutouts </strong>are merged spatiotemporal subsets of the European weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V003">CMSAF SARAH-3</a> solar surface radiation dataset for the years 1996, 2010, 2012, 2013, 2019, 2020 and 2023. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>).</p> <p>Solar irradiation data is taken from SARAH-3 while all other weather data is from ERA5.</p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul> <p><strong>CMSAF SARAH-3</strong></p> <ul> <li>Pfeifroth, Uwe; Kothe, Steffen; Drücke, Jaqueline; Trentmann, Jörg; Schröder, Marc; Selbach, Nathalie; Hollmann, Rainer (2023): Surface Radiation Data Set - Heliosat (SARAH) - Edition 3, Satellite Application Facility on Climate Monitoring, DOI:10.5676/EUM_SAF_CM/SARAH/V003, <a href="https://doi.org/10.5676/EUM_SAF_CM/SARAH/V003" target="_blank" rel="noopener">https://doi.org/10.5676/EUM_SAF_CM/SARAH/V003</a>.</li> <li><strong>Terms of Use:</strong> All intellectual property rights of the CM SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "copyright (year) EUMETSAT" on each of the products used.</li> </ul>
Global Environmental and Weather data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided <strong>cutouts </strong>are spatiotemporal subsets of the Earth weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002">CMSAF SARAH-2</a> solar surface radiation dataset for the <strong>year 2013</strong>. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>). They can be reproduced or extended for other weather years (approx. 40-50 years) around the world by using the <a href="https://github.com/pypsa-meets-africa/pypsa-africa/blob/main/scripts/build_cutout.py">build.cutout.py</a></p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul>
Open and Lite Techno-economic Dataset for Long-term Energy Systems Modelling in the Republic of South Africa
<p>An open-source lite techno-economic dataset for long term energy systems modelling in the Republic of South Africa. Includes data on electricity generation and demand, electricity imports and exports, power transmission and distribution, residual capacity, capacity factor, operational lifetime, and fixed, variable and capital costs of electricity generation technologies. It also contains estimates for renewable potential and fossil fuel reserves in South Africa.</p>
Open dataset for publication "Systematic Mapping Study on Requirements Engineering for Regulatory Compliance of Software Systems"
<p>This publication contains open dataset for the journal publication "Systematic Mapping Study on Requirements Engineering for Regulatory Compliance of Software Systems".</p> <p>The dataset contains the data extracted from 280 selected primary studies.</p> <p>The dataset includes the following data:</p> <ul> <li>study metadata (title, venue, publication year, authors, authors’ affiliation, abstract);</li> <li>challenges to regulatory compliance (direct excerpts from studies);</li> <li>categories of challenges to compliance;</li> <li>principles and practices (direct excerpts from text);</li> <li>categories of principles and practices;</li> <li>types of automation of principles and practices;</li> <li>involved stakeholders (direct excerpts from studies);</li> <li>categories of involved stakeholders;</li> <li>phase of the principle and practice life cycle for which involvement of stakeholders was considered;</li> <li>SDLC process areas covered by the study;</li> <li>regulations considered in the study;</li> <li>fields of regulations that were considered;</li> <li>domains of application that were considered;</li> <li>assessment of rigor and relevance of the study.</li> </ul>
Advanced open source data formats for geometrically and physically coupled systems - examples
<p>Some model files to describe a geometrically and physically coupled PDE and a ODE system, arising from discretization in space. The files correspond to:</p> <ul> <li>a simplified two component problem with coupling and an FMU in <strong>withFMU.json</strong></li> <li>the corresponding ODE in <strong>io_withFMU_FECoupled.json</strong></li> <li>a PDE model of a complete machine in <strong>ictimt_coupledModel.zip</strong>. This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> <li>the corresponding discrete model in <strong>ictimt_feCoupled.zip </strong>This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> </ul> <p> </p> <p> </p>
Accompanying data for the open-source book Modeling of Hydrological Systems in Semi-Arid Central Asia
<p>This data set is used to reproduce examples in the open-source book <a href="https://hydrosolutions.github.io/caham_book/">"Modeling of Hydrological Systems in Semi-Arid Central Asia"</a> which is part of a free course on hydrological modeling in Central Asia. The course teaches how to use publicly available data to implement a hydrological model for climate impact studies (Marti et al., 2023). </p> <p>To use the data set to reproduce the examples in the book: Download the book from https://doi.org/10.5281/zenodo.6350042 and this data set to the same hierarchical level in your file system: </p> <p>|- caham_book<br> |- caham_data<br> |- AmuDarya<br> |- central_asia_domain<br> |- student_case_study_basins<br> |- SyrDarya</p> <p>You will need a working installation of R (https://www.r-project.org/) and a GUI (e.g. Posit, formerly RStudio https://posit.co/) to reproduce the scripted examples in the book. Once your software is set up, you can proceed to run the examples. </p> <p> </p>
DeepScenario: An Open Driving Scenario Dataset for Autonomous Driving System Testing
<p>With the rapid development of autonomous driving systems (ADSs), testing ADSs under various driving conditions has become a key method to ensure the successful deployment of ADS in the real-world. However, it is impossible to test all the scenarios due to the inherent complexity and uncertainty of ADSs and the driving tasks. Further, testing of ADSs is expensive regarding time and computational resources. Therefore, a large-scale driving scenario dataset consisting of various driving conditions is needed. To this end, we present an open driving scenario dataset <em>DeepScenario</em>, containing over 30<em>K</em> <em>executable</em> driving scenarios, which are collected by 2880 test executions of three driving scenario generation strategies. Each scenario in the dataset is labeled with six attributes characterizing test results. We further show the attribute statistics and distribution of driving scenarios. For example, there are 1050 collision scenarios, in 917 scenarios there were collisions with other vehicles, 105 and 28 with pedestrians and static obstacles, respectively.</p> <p>This dataset contains:</p> <ol> <li><strong><a href="https://github.com/Simula-COMPLEX/DeepScenario/tree/main/deepscenario-dataset">deepscenario-dataset</a></strong> - DeepScenario dataset, which includes driving scenarios generated by executing three scenario generation strategies: <em>Reinforcement Learning (RL)-based Strategy</em>, <em>Random-based Strategy</em>, <em>Greedy-based Strategy</em>;</li> <li><strong><a href="https://github.com/Simula-COMPLEX/DeepScenario/tree/main/deepscenario-toolset">deepscenario-toolset</a></strong> - The toolset for <em>DeepScenario</em> dataset, including <em>ScenarioCollector</em> that can automatically collect driving scenarios, and <em>ScenarioRunner</em> that can support replaying driving scenarios. We also provide <a href="https://github.com/Simula-COMPLEX/DeepScenario/tree/main/deepscenario-toolset/lgsvl/scenariotoolset">source code</a> and <a href="https://github.com/Simula-COMPLEX/DeepScenario/tree/main/deepscenario-toolset#usage">usage examples</a> for the toolset. </li> </ol> <p>More information about DeepScenario dataset is available in our Github repository: <a href="https://github.com/Simula-COMPLEX/DeepScenario">https://github.com/Simula-COMPLEX/DeepScenario</a>.</p>
Video: Crashkurs Digitale Langzeitarchivierung - Das Referenzmodell Open Archival Information System (OAIS)
<p>Dieser Crashkurs stellt das Referenzmodell Open Archival Information System (OAIS) vor, das die verschiedenen Tätigkeitsbereiche eines Langzeitarchivs beschreibt. Als internationaler Standard (ISO 14721) bietet das OAIS-Modell eine Kommunikationsgrundlage zum Thema der Digitalen Langzeitarchivierung. So sind zentrale OAIS-Begriffe wie Preservation Planning, Archival Information Package und Designated Community in der Langzeitarchivierungs-Community etabliert.</p> <p>In dieser Einführung werden die Aufgaben eines Digitalen Langzeitarchivs anhand der verschiedenen OAIS-Funktionseinheiten beschrieben. Ebenso bietet der Crashkurs einen Überblick über die verschiedenen Verarbeitungsstadien von Informationspaketen (SIP, AIP, DIP) in einem Langzeitarchiv.</p> <p>Dieser Crashkurs wurde als Lehrvideo für das Teil-Modul "Digitale Langzeitarchivierung" des <a href="https://www.th-koeln.de/weiterbildung/zertifikatskurs-data-librarian_63393.php">Zertifikatskurses "Data Librarian"</a> erstellt. Der Zertifikatskurs wurde 2019/2020 vom Zentrum für Bibliotheks- und Informationswissenschaftliche Weiterbildung der Technischen Hochschule Köln unter wissenschaftlicher Leitung von Prof. Dr. Konrad Förstner ausgerichtet.</p>
Antwerp precipitation, open water streams and sewer system sensor data
<p>This csv dataset includes historical data for the period 2018-2020 from multiple sensors deployed in Antwerp that can help city services to have a clear view on the actual precipitation in different regions, the water level of different water flows as well as the water flows in the sewer system of the city. This data was used in CUTLER (visualized in Antwerp’s dashboard) to assist in the impact modelling of garden streets.</p> <p>The data set contains:</p> <p>- 6 water level sensors: lora.0004A30B00202D0C, lora.0004A30B00204B8B, lora.0004A30B00200BFE, lora.0004A30B0021F1D4, lora.0004A30B002041F6, lora.0004A30B001FC6DF</p> <p>- 4 pluvio meters: lora.0004A30B002025F5, lora.0004A30B00201DCC, lora.0004A30B001FF6F7, lora.0004A30B001FA140<br> <br> - 3 sewer level meters: lora.0004A30B001FD07B, lora.0004A30B0020112D, lora.0004A30B001F9B4B</p>
ThermoCyte: an inexpensive open-source temperature control system for in vitro live cell imaging
<p>Live-cell imaging is a common technique in microscopy to investigate dynamic cellular behaviour and permits the accurate and relevant analysis of a wide range of cellular and tissue parameters, such as motility, cell division, wound healing responses, and calcium (Ca2+) signalling in cell lines, primary cell cultures, and ex vivo preparations. Furthermore, this can take place under many experimental conditions, making live-cell imaging indispensable for biological research. Systems which maintain cells at physiological conditions outside of a CO<sub>2</sub> incubator are often bulky, expensive, and use proprietary components. Here we present an inexpensive, open-source temperature control system for in vitro live cell imaging. Our system 'ThermoCyte', which is constructed from standard electronic components, enables precise tuning, control, and logging of a temperature 'set point' for imaging cells at physiological temperature. We achieved stable thermal dynamics, with reliable temperature cycling and a standard deviation of 0.42°C over 1 hour. Furthermore, the device is modular in nature, and is adaptable to the researcher's specific needs. This represents simple, inexpensive, and reliable tool for laboratories to carry out custom live-cell imaging protocols, on a standard lab bench, at physiological temperature.</p>
Scripts, Data, and Figures for "MesoHOPS: Size-invariant scaling calculations of multi-excitation open quantum systems"
<div> <p>This archive contains the scripts required to run all calculations presented in "MesoHOPS: Size-invariant scaling calculations of multi-excitation open quantum systems," the figure generation scripts and attendant processed data, and a copy of MesoHOPS version 1.4.0, as used in the paper.</p> </div>
FIGURE 8 in Comparison between Atlantic salmon Salmo salar post-smolts reared in open sea cages and in the Preline raceway semi-closed containment aquaculture system
FIGURE 8 Mean (S.E.; n = 30) relative gene transcription values for (a) mef2c () Reference, and () Preline, (b) gata4 () Reference, and () Preline and (c) vegf () Reference, and () Preline using ef1α as standard in Salmo salar heart, both in fresh water and after rearing in Preline semiclosed containment system (S-CCS;) and reference group open pen () for 4 months in seawater. Significant differences between groups are indicated by different lower-case letters
FIGURE 7 in Comparison between Atlantic salmon Salmo salar post-smolts reared in open sea cages and in the Preline raceway semi-closed containment aquaculture system
FIGURE 7 Mean (S.E.; n = 30) relative gene transcription values for (a) Igf-I () Reference, and () Preline, (b) igf1ra () Reference, and () Preline, and (c) igf1bp1a () Reference, and () Preline using ef1α as standard in Salmo salar muscle, both in fresh water and during rearing in Preline semiclosed containment system (S-CCS;) and reference group (). Significant differences through time are denoted with capital letters within the reference group and lower-case letters within Preline S-CCS. SW, seawater
FIGURE 3 in Comparison between Atlantic salmon Salmo salar post-smolts reared in open sea cages and in the Preline raceway semi-closed containment aquaculture system
FIGURE 3 Mean [S.E.; n = 30; (a), (c), (d)] Salmo salar growth in mass (M) fork length (LF) and Fulton's condition factor (K) measured in freshwater (15 April 2016) and during the post-smolt phase (1–2 June; 1–2 June and 29–30 August 2016) (a) Measured mass () Preline, and () Reference, (b) estimated mean mass (Fishtalk calculations, CEF = 1.1) () Reference, and () Preline, (c) mean fork length () Preline, and () Reference and (d) condition factor (K) () Preline, and () Reference. Estimated mean mass covers both the post-smolt phase 5 May to 30 August, and the growth phase 31 August to 30 November. Changeover is indicated with a dot in the figure. SW, seawater. Significant difference between groups; *p <0.05; ***p <0.001
FIGURE 4 in Comparison between Atlantic salmon Salmo salar post-smolts reared in open sea cages and in the Preline raceway semi-closed containment aquaculture system
FIGURE 4 Accumulated mortality of Salmo salar in the Preline semiclosed containment system (S-CCS) 30 April to 30 August followed by the open pen growth phase (Buholmen) from 1 September to 30 November (;, changeover from S-CCS to open pen). The accumulated mortality in the reference group covers the period 5 May to 30 November ()
FIGURE 5 in Comparison between Atlantic salmon Salmo salar post-smolts reared in open sea cages and in the Preline raceway semi-closed containment aquaculture system
FIGURE 5 Mean (+S.E.) Salmo salar skeletal muscle fibre diameter frequency distribution reared in Preline semi-closed containment system () and reference S. salar () after 4 months in seawater. Significant difference between groups; *p <0.05; ***p <0.001
FIGURE 2 in Comparison between Atlantic salmon Salmo salar post-smolts reared in open sea cages and in the Preline raceway semi-closed containment aquaculture system
FIGURE 2 (a) Alternate day mean water temperature and (b) salinity at the Salmo salar post- smolt Preline semi-closed containment system () and reference group () rearing systems between 5 May and 30 November 2016. Data from Preline S-CCS represents the Buholmen open-pen between 31 August and 30 November 2016
FIGURE 1 in Comparison between Atlantic salmon Salmo salar post-smolts reared in open sea cages and in the Preline raceway semi-closed containment aquaculture system
FIGURE 1 (a) Location of experiment area in Norway and (b) locations of the Preline semi-closed containment system (S-CCS), reference, freshwater and growing phase groups of Salmo salar post-smolts in Hordaland region; (c) schematic of the S-CCS; (d) standard open sea cages for S. salar production in Norway; (e) drawing of an open conical pen used to hold the reference group of fish
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.