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19 results for “scientific software”
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>
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>
Fuzzy modelling and mapping soil moisture in Germany, link to research data and scientific software
<p>Research data and scientific software related to spatio-temporal estimations of ecological soil moisture with available data covering the whole territory of Germany and the Kellerwald National Park (Hesse). Temporal trends of modelled soil moisture for the time period 1961–2070 were statistically analyzed. Soil moisture changes (drying-out) at both national and regional levels were mapped.</p>
NeuroGentoo Presentation - Bringing the Power of Gentoo Package Management to (Neuro)Scientific Software Environments
<p>Neuroscience, one of the most computation-reliant fields in the natural sciences, is dependent upon dozens of highly complex software suites, which scientists are often forced to manage manually. Upstream developers often ship bundled dependencies to better support this flawed workflow, and in the resulting mess documenting and reproducing analysis pipelines is neigh-impossible. We seek to correct these shortcoming of both modern software distribution as well as modern data science, by integrating high-quality ebuilds for neuroscientific software into the Gentoo Science Overlay. We also seek to publish a simple NuroGentoo world file (along with appropriate usage instructions) to allow scientists with access to OpenStack, Amazon Elastic Computing, or Docker to launch up and build a system fit for reproducing data analysis run on other NeuroGentoo systems with minimum effort.</p>
Annexes to the external scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using MCRA software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have acute effects on the nervous system: </p> <ol> <li>brain and/or erythrocyte acetylcholinesterase inhibition (CAG-NAN);</li> <li>functional alterations of the motor division (CAG-NAM).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the nervous system (<a href="https://doi.org/10.2903/j.efsa.2019.5800">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with Monte Carlo Risk Assessment (MCRA) software using a 2-dimensional Monte Carlo simulation, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-NAN</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-NAM</li> <li>Annex B.1 – Output data from the Tier II exposure assessment of CAG-NAN</li> <li>Annex B.2 – Output data from the Tier II exposure assessment of CAG-NAM</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the external scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using MCRA software (<a href="https://doi.org/10.2903/sp.efsa.2019.en-1708">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the nervous system (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Annexes to the external scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using MCRA software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have chronic effects on the thyroid: </p> <ol> <li>hypertrophy, hyperplasia and neoplasia of C-cells, i.e. affecting the parafollicular cells or the calcitonin system of the thyroid (CAG-TCP);</li> <li>hypothyroidism, i.e. affecting the follicular cells and/or the hormone system of the thyroid (CAG-TCF).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the thyroid (<a href="https://doi.org/10.2903/j.efsa.2019.5801">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with Monte Carlo Risk Assessment (MCRA) software using a 2-dimensional probabilistic method, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-TCP</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-TCF</li> <li>Annex B.1 – Output data from the Tier II exposure assessment of CAG-TCP</li> <li>Annex B.2 – Output data from the Tier II exposure assessment of CAG-TCF</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the external scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using MCRA software (<a href="https://doi.org/10.2903/sp.efsa.2019.en-1707">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the thyroid (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Annexes to the scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using SAS® software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have chronic effects on the thyroid: </p> <ol> <li>hypertrophy, hyperplasia and neoplasia of C-cells, i.e. affecting the parafollicular cells or the calcitonin system of the thyroid (CAG-TCP);</li> <li>hypothyroidism, i.e. affecting the follicular cells and/or the hormone system of the thyroid (CAG-TCF).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the thyroid (<a href="https://doi.org/10.2903/j.efsa.2019.5801">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with SAS<sup>®</sup> software using a 2-dimensional probabilistic method, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-TCP</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-TCF</li> <li>Annex B.1 – Output data from the Tier I exposure assessment of CAG-TCP</li> <li>Annex B.2 – Output data from the Tier I exposure assessment of CAG-TCF</li> <li>Annex C.1 – Output data from the Tier II exposure assessment of CAG-TCP</li> <li>Annex C.2 – Output data from the Tier II exposure assessment of CAG-TCF</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using SAS<sup>®</sup> software (<a href="https://doi.org/10.2903/j.efsa.2019.5763">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the thyroid (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Annexes to the scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using SAS® software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have acute effects on the nervous system: </p> <ol> <li>brain and/or erythrocyte acetylcholinesterase inhibition (CAG-NAN);</li> <li>functional alterations of the motor division (CAG-NAM).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the nervous system (<a href="https://doi.org/10.2903/j.efsa.2019.5800">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with SAS<sup>®</sup> software using a 2-dimensional Monte Carlo simulation, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-NAN</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-NAM</li> <li>Annex B.1 – Output data from the Tier I exposure assessment of CAG-NAN</li> <li>Annex B.2 – Output data from the Tier I exposure assessment of CAG-NAM</li> <li>Annex C.1 – Output data from the Tier II exposure assessment of CAG-NAN</li> <li>Annex C.2 – Output data from the Tier II exposure assessment of CAG-NAM</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using SAS<sup>®</sup> software (<a href="https://doi.org/10.2903/j.efsa.2019.5764">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the nervous system (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Supplementary materials of the paper entitled: "Metamorphic Testing Meets Regression Testing: A Case Study of Scientific Software Maintenance"
<p>Supplementary materials of the paper entitled:</p> <p>“Metamorphic Testing Meets Regression Testing: A Case Study of Scientific Software Maintenance”</p> <p>file01 - source code for detecting and comparing output relations of a given metamorphic relation<br> file02 - interview transcripts and qualitative coding results<br> file03 - system script for compiling SWMM, creating sSWMM simulation outputs, and logging execution time<br> file04 - source code for SWMM 5.1.014<br> file05 - source code for SWMM 5.1.015<br> file06 - a suite of 40 regression tests (40 .inp files)<br> file07 - result table for 760 cases (19 MRs × 40 .inp files)<br> file08 - 491 automatically generated follow-up .inp files via reuse<br> file09 - result table for mutation analysis<br> file10 - output files for mutation analysis with metamorphic testing:<br> a. Original source output<br> b. Original follow-up output<br> c. Mutant source output<br> d. Mutant follow-up output</p>
High-impact Scientific Software in Astronomy and its creators - data
<p>data for the article "High-impact Scientific Software in Astronomy and its creators"</p>
How do developers use Code Snippets in README files? Analyzing between Python Software Development and Scientific Libraries
<p>README files contain crucial information for clients to effectively use the software.<br> However, we sometimes found that developers either present insufficient content or leave the README files empty.<br> Recent works suggest that, in popular repositories, developers tend to present more code snippets in their README files.<br> In this study, we investigate how developers present code snippets in 10,784 README files of PyPI libraries in terms of (i) the types of code snippets and (ii) the sections where code snippets are presented.<br> Our results indicate the prevalence of types of code snippets in various sections across different types of libraries.</p>
Self-Admitted Technical Debt in Scientific Software
<p># Title<br>Self-Admitted Technical Debt (SATD) in Scientific Software Projects</p> <p># Description<br>## Abstract<br>This dataset contains annotated code comments from nine open-source scientific software projects: Astropy, Biopython, CESM, Firedrake, MOOSE, GROMACS, Elmer, Athena, and Root. The comments are labeled to identify instances of Self-Admitted Technical Debt (SATD), with a focus on a novel category termed Scientific Debt (SD). The dataset supports research on the nature and management of technical debt in scientific software.</p> <p>## Purpose<br>The dataset was created to explore the prevalence and characteristics of SATD in scientific software, with the aim of improving software maintainability and scientific validity.</p> <p>## Content<br>The dataset includes over 28,680 annotated code comments, with labels indicating various types of technical debt such as Code Debt, Design Debt, and Scientific Debt. Each comment is accompanied by metadata including the project name, file path, comment introduction date, and comment removal date.</p> <p>## Scope<br>The dataset covers nine projects across different scientific domains, including astronomy, molecular biology, and climate modeling. Data was collected from publicly available repositories and spans from the inception of each project to the present.</p> <p>## Methodology<br>Data was extracted using GitPython to access the version control histories of the selected projects. Comments were manually labeled for SATD, with a focus on identifying Scientific Debt indicators such as assumptions, missing edge cases, computational inaccuracies, translation challenges, and new scientific findings.</p> <p>## Usage Notes<br>This dataset can be used for research on technical debt management, software maintenance, and scientific software development. Users should have a basic understanding of programming and version control systems. Recommended tools for analysis include Python and Pandas.</p> <p>## Ethical Considerations<br>All data was collected from publicly available sources. No personal or sensitive information is included.</p> <p># Technical Details<br>## File Formats<br>- CSV: Contains the annotated comments and metadata</p> <p>## Size<br>- Number of records: 28,680<br>- Total file size: 15MB</p> <p>## Version<br>- Version 1.0, July 2024</p> <p># Access and Use<br>## Access<br>The dataset can be downloaded from Zenodo: [Zenodo Link](https://doi.org/10.5281/zenodo.13174322)</p> <p>## License<br>This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).</p>
Supplementary materials of the paper entitled: "Metamorphic Testing Meets Regression Testing: A Case Study of Scientific Software Maintenance"
<p>Supplementary materials of the paper entitled: “Metamorphic Testing Meets Regression Testing: A Case Study of Scientific Software Maintenance”</p> <p>file01 - source code for detecting and comparing output relations of a given metamorphic relation<br> file02 - system script for compiling SWMM, creating sSWMM simulation outputs, and logging execution time<br> file03 - source code for SWMM 5.1.014<br> file04 - source code for SWMM 5.1.015<br> file05 - a suite of 40 regression tests (40 .inp files)<br> file06 - result table for 760 cases (19 MRs × 40 .inp files)<br> file07 - 491 automatically generated follow-up .inp files via reuse<br> file08 - result table for mutation analysis<br> file09 - source code for three open-source software systems<br> File10 - code change in three open-source software systems</p>
Assessing and mapping the potential development of forest ecosystems, link to research data and scientific software
<p>Research data and scientific software related to: Schröder W. Nickel S, Jenssen M, Riediger J 2015. Methodology to assess and map the potential development of forest ecosystems exposed to climate change and atmospheric nitrogen deposition: a pilot study in Germany. Science of the Total Environment 521-522:108-122</p>
Modelling and mapping heavy metal accumulation in moss and natural surface soil throughout Norway (1990-2010), link to research data and scientific software
<p>Research data and scientific software related to an investigation of statistical relations between the accumulation of heavy metals in moss and natural surface soil and potential influencing factors such as atmospheric deposition. Data were collected in 1995, 2000, 2005 and 2010 throughout Norway. Statistical correlations of a set of potential predictors (elevation, precipitation, density of different land uses, population density, physical properties of soil) with concentrations of cadmium, mercury and lead in moss and natural surface soil were evaluated. Spatio-temporal trends were estimated by use of multivariate regression-kriging and generalized linear models.</p>
Correlating heavy metal deposition and respective concentration in moss and natural surface soil for ecological land classes in Norway (1990-2010), link to research data and scientific software
<p>Research data and scientific software related to a study on statistical correlations between modelled atmospheric heavy metal deposition and respective accumulation in moss and natural surface soil for different natural landscapes in Norway. Data on cadmium, lead, and mercury were collected in 1995, 2000, 2005 and 2010 throughout Norway. The landscape information was derived from the Ecological Land Classification of Europe. Correlations between concentration and respective modelled deposition data were computed for each land class.</p>
Integrative evaluation of biomonitoring data and modelings indicating atmospheric deposition of heavy metals, link to research data and scientific software
<p>Research data and scientific software related to integrative statistical analyses based on deposition data calculated with the model LOTOS-EUROS (LE) and the EMEP/MSC-East model (Germany, Europe) and Biomonitoring data on As, Cd, Cr, Cu, Ni, Pb, Zn concentrations in moss, leaves and needles and soil derived from the European Moss Survey (EMS), the German Environmental Specimen Bank (ESB) and the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests). The modelled HM deposition and respective concentrations in moss (EMS), leaves and needles (ESB, ICP Forests) and soil (ICP Forests) were investigated for their statistical relationships. Regression kriging was applied to calculate maps of Cd and Pb deposition across Germany.</p>
Random Forest models and maps of heavy metal and nitrogen concentrations in moss in 2010 across Europe, link to research data and scientific software
<p>Research data and scientific software related to a study exploring the statistical relations between the concentration of nine heavy metals (As, Cd, Cr, Cu, Hg, Ni, Pb, V, Zn) and N in moss specimens collected in 2010 throughout Europe and a set potential explanatory variables (such as the atmospheric deposition calculated by use of two chemical transport models, distance from emission sources, density of different land uses, population density, elevation, precipitation, clay content of soils). Statistical analysis and modelling relies on Random Forest (RF). RF-models in conjunction with a Geographical Information System (GIS) were then used for mapping spatial patterns of element concentrations in moss across Europe.</p>
Modelling spatial patterns of correlations between concentrations of heavy metals in mosses and atmospheric deposition across Europe in 2010, link to research data and scientific software
<p>Research data and scientific software related to a study investigating the correlations between the concentrations of nine heavy metals in moss and atmospheric deposition within ecological land classes covering Europe. Additionally, it is examined to what extent the statistical relations are affected by the land use around the moss sampling sites.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.