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Results from the revision of MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data
<p>afr47041.zip, lat36378.zip, and eur115620.zip contain All of Us Summary Statistics used in the revised version of "MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data". Summary statistics for three cohorts are included: Afr47k, Lat36k, and Eur116k. These cohorts have not been downsampled to have equal levels of missingness.</p> <p>pips.tsv contains fine-mapped variants with PIP > 0.01 via MultiSuSiE from the revised version of "MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data". Subcohorts with the _unmatched suffix have not been downsampled to have equal levels of phenotyped missingness across ancestries. </p> <p>MultiSuSiE-main.zip contains the MultiSuSiE software packages (corresponds to the Github repo on 10/16/2025).</p> <p>Please cite:</p> <p>Rossen, Jordan, et al. "MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data." <em>medRxiv</em> (2024): 2024-05.</p> <p> </p>
Construction of Parallel Addition Algorithms by the Extending Window Method - results
<p>An algebraic number <span class="math-tex">\(\beta \in \mathbb{C}\)</span> with no conjugate of modulus 1 can serve as the base of a numeration system <span class="math-tex">\((\beta, \mathcal{A})\)</span> with parallel addition, i.e., the sum of two operands represented in base <span class="math-tex">\(\beta\)</span> with digits from <span class="math-tex">\(\mathcal{A}\)</span> is calculated in constant time, irrespective of the length of the operands.</p> <p>In the paper <a href="https://arxiv.org/abs/1801.01062">Construction of Algorithms for Parallel Addition</a>, a so-called <em>Extending Window Method </em>is introduced. This method is an algorithm to construct Parallel Addition algorithms. See the paper for the details, or the <a href="https://jan.legersky.cz/project/paralleladdition/">project website</a>.</p> <p>We present here the results of this method for selected numeration systems, see the <a href="http://doi.org/10.5281/zenodo.1542942">implementation</a>.</p>
TCGA BioBombe Results
<p>BioBombe analysis applied to gene expression data from The Cancer Genome Atlas (TCGA) PanCanAtlas. Method and results described in https://github.com/greenelab/BioBombe</p>
On the Performance of Method-Level Bug Prediction: A Negative Result. Appendix
<p>Abstract Bug prediction is aimed at identifying software artifacts that are more likely to be defective. Most approaches defined so far target the prediction of bugs at class/file level. Nevertheless, past research has provided evidence that this granularity might be too coarse-grained, thus reducing the usability of bug prediction in practice. As a consequence, researchers have started proposing defect prediction models targeting a finer granularity, particularly targeting methods, providing promising evidence that it is possible to operate at this granularity. Particularly, models based on a mixture of product and process metrics provided the best results. </p> <p>In this paper, we first replicate previous research on method-level bug- prediction using different systems and timespans. Afterward, based on the limitations of existing research, we (1) re-evaluate method-level bug prediction models more realistically and (2) analyze the whether textual features— previously shown as valuable sources of information for the evaluation of software quality (yet surprisingly unexplored in this research field)—can be exploited to improve method-level bug prediction abilities. Key results of our study include that (1) the performance of the previously proposed models, tested using the same strategy but with different systems/timespans, is con- firmed. However, (2) when evaluated with a more realistic strategy all the models show a dramatic drop in performance exhibiting results close to that of a random classifier. In addition, we find that (3) the contribution of textual features within such models is limited and unable to improve the prediction capabilities significantly. As a consequence, our replication and negative results indicate that method-level bug prediction is still an open challenge.</p>
GTEX BioBombe Results - Randomly Permuted Data
<p>BioBombe analysis applied to <strong>randomly permuted </strong>gene expression data from The Genotype-Tissue Expression (GTEx) project. Method and results described in https://github.com/greenelab/BioBombe</p>
DATASET of INTEGRADDE Expression of Interest, including aggregated and anonymized results.
<p>This dataset corresponds to the one generated with information about those applying to the Expression of Interest (EOI) including participants’ attributes, e.g. country of origin, TRLs, type of applicants, scores obtained in the evaluation process, and funding status after the funnel process. All of this, for statistical purposes and analysis of the performance of the Expression of Interest</p>
Results from the FLEX Model for the paper "Impact of variable electricity price on heat pump operated buildings"
<p>The sqlite database contains the results of the Flex model for the Austrian single family house building stock ( insert GITHUB LINK). The building stock is represented by 36 different representative building archetypes (“OperationScenario_Component_Building”). Each building is simulated in twice. In the "reference" mode the energy demand is simply met and indoor comfort is kept constant. In the "optimization" mode the indoor temperature can be varied and thermal storages are charged and discharged minimizing the households energy cost based on a variable electricity price. The sqlite database “Variable_Price_Paper” contains the results for the building stock without any storage implemented. In “Variable_Price_Paper_TS” all buildings have a 750l hot water buffer storage and a 400l DHW storage implemented. The sqlite files SFH_23/25/27 contain the results for a single building where the maximum inside room temperature was changed to 23, 25 and 27 °C respectively.</p> <p>Following columns were used for generating the results for the Paper:</p> <p>In the hourly results relevant parameters used in the publication were:</p> <ul> <li>ID_Scenario: each building simulated under a different electricity price has a unique scenario number. “OperationScenario” gives an overview of the scenarios.</li> <li>Grid: describes the electricity demand from the grid by the household.</li> </ul> <p>In the yearly results relevant parameters used in the publication were:</p> <ul> <li>ID_Scenario</li> <li>TotalCost: represent the yearly operation cost</li> <li>Grid: electricity demand summed up for the whole year</li> </ul> <p>5 different electricity prices are used for scenario generation. The first price is constant, “electricity_2” is the real time price from 2021 plus a hypothetical grid fee of 20cents/kWh. “electricity_3, electricity_4, electricity_5” are prices generated for 2030 for Austria with the Balmorel model. Their profiles can be found under “OperationScenario_EnergyPrice”.</p> <p> </p>
Climate-ecosystem modelling made easy: the Land Sites Platform - simulation results
<p>Model data to reproduce the experiments and figures presented in "Climate-ecosystem modelling made easy: the Land Sites Platform" (Keetz & Lieungh et al., accepted; publication details to be added).</p> <p>The repository includes model input data for the BOR1 site, and model output for two cases (simulations) executed for that site. Case "bor1-1000y-allpfts" was run with default settings, whereas case "bor1-1000y-grasspfts" was run with C3 grass and Arctic C3 grass as the only plant functional types.<br> Concatenated versions of the model output (made by combining monthly history files) for each case are stored in the top folder as NetCDF files (.nc). The full case folders, including monthly output under /archive/lnd/hist, are stored under "cases/" as zipped directories.<br> The "data/" folder contains model input data for the site, and is identical for the two cases.</p> <p>Jupyter notebooks to analyse the data and create the plots in Figure 4 of the article are stored under "notebooks/technical_paper_results/" in the NorESM-LSP GitHub repository, which is accessible in the same version as the submitted manuscript here: 10.5281/zenodo.7310652</p> <p>Description: Model data to reproduce the experiments and figures presented in "Climate-ecosystem modelling made easy: the Land Sites Platform".<br> Coverage: Geographical coordinates 9.07876, 61.0355. Forcing data from the closest 0.5-degree model grid cell.<br> Format: NetCDF (.nc), zipped directories (.zip), shell scripts (.sh), XML files (.xml), text files (.txt), and others<br> Language: English<br> Relation: 10.5281/zenodo.7310652, <br> Source: GSWP3 for input data (Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T. and Hanasaki, N. (2006) GSWP-2: Multimodel Analysis and Implications for Our Perception of the Land Surface. Bulletin of the American Meteorological Society, 87(10), 1381–98.)</p>
Figure 17. Phylogenetic results. A in Phylogeny and revision of the Neotropical genus Grumichella Müller (Trichoptera: Leptoceridae), including nine new species and a key
Figure 17. Phylogenetic results. A, most parsimonious tree from equal (E1) and implied weighting analyses (I1), using k values from 13 to 100. B–D, other three most parsimonious trees from equal analyses (E2, E3 and E4, respectively). E, tree representing the strict consensus from equal weighting analyses. F, tree from implied weighting analyses (I2) using k values from 1 to 12 (* indicating the type-species, G. rostrata; dashed line indicating G. blahnik, G. cressa and G. trujilloi).
Dataset Literature Review Digital Forensic and Reproducibility of Result
<p>Data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci "<em>digital forensic" dan</em> "<em>reproducibility of result"</em></p>
The results of SOCOLv4 simulations
<p>The results of SOCOLv4 simulations. Fin... - based on SSP2-4.5; s... - based on SSP5-8.5.</p>
Synchrotron X-ray Diffraction Results - Measuring Bulk Crystallographic Texture from Differently-Orientated Ti-6Al-4V Samples
<p>A dataset of crystallographic texture results for both α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases, measured from six differently orientated Ti-6Al-4V (Ti-64) samples, using two different analysis techniques of synchrotron X-ray diffraction (SXRD) data. The texture results are produced from two refinement methods for fitting intensities from SXRD pattern images; an established Rietveld refinement method using the software package <a href="http://maud.radiographema.eu">MAUD (Materials Analysis Using Diffraction)</a> and a new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package. The texture results were also compared with electron backscatter diffraction (EBSD) measurements from a single sample orientation. The SXRD and EBSD textures were analysed using <a href="https://mtex-toolbox.github.io">MTEX</a> to enable a direct comparison of the pole figures, orientation distribution functions (ODFs) and numerical values for the texture indices. The SXRD texture is calculated from each of the six different sample orientations, a combination of the six sample orientations, and in a batch processing method for calculating spatially-resolved texture variation from 387 individual X-Y stage-scan SXRD measurements across one of the samples. The texture variation measured using stage-scan SXRD is directly compared with EBSD, by splitting up the EBSD map into an equivalent grid matrix using an automated script in MTEX.</p> <p><strong>Material </strong></p> <p>The Ti-64 material used in this study was pre-rolled to 87.5% reduction at 915ºC and then air-cooled to develop a characteristic texture. The run numbers from the experiment reference six different sample orientations, according to their alignment with the original rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction), and alignment with the horizontal (X) and vertical (Y) axes of the synchrotron detector.</p> <p><strong>MAUD / MTEX Analysis</strong></p> <p>The α and β phase texture for each of the six different sample orientations was calculated using MAUD, included in this <a href="https://doi.org/10.5281/zenodo.7311323">analysis dataset</a>, which produced ODFs in the form of text files. The texture files were analysed in MTEX using scripts from the <a href="https://github.com/LightForm-group/MAUD-batch-analysis">MAUD-batch-analysis</a> package, for plotting of the pole figures and ODF slices, along with calculation of pole figure maxima, ODF maxima and texture indices. The same procedure was used to analyse texture from all six orientations together; using MTEX to fit a single ODF text file. And a series of ODF text files were analysed to calculate texture variation from an X-Y stage scan of Sample 1 (103845). Two different ODF resolutions of 5º and 15º were initially used to fit the texture in MAUD, with the same ODF resolution applied to analyse the data in MTEX. However, an ODF resolution of 15° was found to reproduce the most reasonable texture strength intensity values, with the closest match to the EBSD results.</p> <p><strong>Continuous-Peak-Fit / MTEX Analysis </strong></p> <p>The lattice plane intensities for 21 α and 4 β phase peaks were extracted from the Continuous-Peak-Fit analysis, included in this <a href="https://doi.org/10.5281/zenodo.7311323">analysis dataset</a>, and saved as text files in the form of pole figures. The lattice intensity text files were analysed in MTEX using scripts from the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package, to plot pole figures and ODF slices, and to calculate pole figure maxima, ODF maxima and texture indices. The same procedure was used to analyse texture from all six orientations together, along with combinations of different sample orientations, by fitting combined lattice intensity text files in MTEX. And a series of lattice intensity text files were analysed to calculate texture variation from the X-Y stage scan of Sample 1 (103845). Lattice plane intensity distributions which had been normalised to a Ti-64 powder sample measurement were also analysed, to see if this had any effect on the texture intensities. Nevertheless, the powder-corrected texture was found to exactly match the raw intensity measurements. Three different ODF resolutions of 5º, 10º and 15º were initially used to fit the texture in MTEX. However, a kernel half-width of 10° was found to produce optimal data fitting, for highly accurate texture strength intensity values.</p> <p><strong>EBSD / MTEX Analysis </strong></p> <p>The indexed α-phase EBSD measurements were recorded over an area of around 100 mm<sup>2</sup>, with an equivalent sized map of β-phase orientations reconstructed from the data. Both the α and the β phase maps were analysed using the <a href="https://github.com/LightForm-group/MTEX-texture-block-analysis">MTEX-texture-block-analysis</a> package, which was used to split up the map into 387 individual square sections, with equivalent dimensions to the SXRD stage-scan measurement grid. For each of the 387 sections, MTEX was used to plot pole figures and ODF slices, and to calculate pole figure maxima, ODF maxima and texture indices.</p> <p><strong>Texture Variation Comparison</strong></p> <p>The texture values calculated from the SXRD stage scan measurements, with the two analysis methods, were used for a direct comparison with the texture variation recorded using EBSD. This analysis was recorded in the <a href="https://github.com/LightForm-group/texture-strength-comparison">texture-strength-comparison</a> package. The results show differences in texture variation across the piece depending on the method used to analyse the SXRD data. The Continuous-Peak-Fit analysis method shows the closest match with EBSD, producing clear texture intensity spikes for the different α and β lattice plane pole figure intensities, ODF maxima and texture indices, at the centre of the piece. The results were also used to develop SXRD maps showing the distribution of texture intensities across the sample.</p> <p><strong>Metadata </strong></p> <p>An accompanying YAML text file contains associated processing metadata for the SXRD and EBSD analyses, recording information about the different packages used to process the data, along with details about the different files contained within this results dataset.</p>
Dataset Literature Review Digital Forensic AND Reproducibility of Results
<p>data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci "Digital Forensic" dan "Reproducibility of Results"</p>
Annotation-based Modeling of Non-functional Requirements and Analysis Results in Domain-driven Design
<p>This repo contains all supplementary data sets that we have created and used throughout this thesis. In particular, it contains<br> - expert interview material (elicitation): consent form and question catalogue for requirements elicitation<br> - expert interview material (evaluation): consent form and task description for expert evaluation<br> - Diagrams related to our modeling concept and Dqualizer<br> - Screenshots of the Domain Story Modeler with our Modeling Concept</p>
Results of the evaluation about the expert interviews regarding the benefits of a digital procurement workspace.
<p>The paper "A next-generation Digital Procurement Workspace focusing on Information Integration, Automation, Analytics, and Sustainability" evaluates the set requirements and discovers the benefits of the Digital Procurement Workspace. For that, expert interviews in a qualitative and quantitative manner were conducted. The qualitative evaluation results are summarized here based on Mayring's (Mayring, 2000) methods.</p>
ARTIMATION Conflict detection and resolution visualisation questionnaire results
<p>This dataset reports the results of the questionnaires (post-condition and post-scenario) that were applied for the human performance assessment of the conflict resolution visualisation tool human in the loop (HITL) simulation of ARTIMATION project.</p>
RRI2SCALE_Monitoring Results_2022.12.19_v1
<p>This dataset contains the results of the monitoring activities that were conducted during the RRI2SCALE project to measure their performance and impact.</p> <p>More Info:</p> <p>Throughout the project, regional authorities and other project partners closely monitored an indicators list and collected the necessary data. Two key data collection methods employed were the following: (i) Regional authorities compiled short reports based on semi-structured templates every four months; and (ii) Feedback collection from participants in key project activities, such as the multistakeholder dialogues and the co-creation workshop. The current dataset provides all the information collected through the above methods. </p>
Data Set from the DMP survey and interviews - intermediate results
<p>This is the data set for a series of interviews in chemistry on data management (plans) and presents interim results. A more detailed analysis and description can be found in the paper "Road to a Chemistry-specific Data Management Plan" submitted to Data Science Journal (2022-12-15).<br> The interview series will continue in 2023 and final results will be published later in 2023.</p> <p>The aim of the conducted interview series is the enrichment of the online survey data from the RDA WG Discipline-specific Guidance for DMP and in a second step the development of a chemistry-specific data management plan template. For this purpose, the current status of data management as well as information about the workflows in the various chemical disciplines were requested in a personal interview with 22 participants so far.</p> <p>All the gathered information and examples will be used to develop a DMP template or guide in line with chemistry-specific requirements. The results provide a comprehensive outlook on the future developments of RDM in chemistry. Possible strategies for implementation are also discussed.</p>
Experimental Results for the study "The Hypervolume Newton Method for Constrained Multi-Objective Optimization Problems"
<p>This repository contains the experiment results (raw data in NPZ and CSV format and Latex tables) for the study "The Hypervolume Newton Method for Constrained Multi-Objective Optimization Problems", which is accepted in <em>Mathematical and Computational Applications</em> journal.</p> <p>The preprint version of the related paper is already online: </p> <p> </p> <p>Wang, H.; Emmerich, M.; Deutz, A.; Hernández, V.A.S.; Schütze, O. The Hypervolume Newton Method for Constrained Multi-objective Optimization Problems. <em>Preprints</em> <strong>2022</strong>, 2022110103 (doi: <a href="http://10.20944/preprints202211.0103.v1">10.20944/preprints202211.0103.v1</a>).</p> <p><strong>Data description:</strong> we benchmarked<strong> </strong>three algorithms: (1) the standalone <strong>Hypervolume Netwon Method</strong> (HVN), (2) NSGA-III, and (3) the <strong>hybridization</strong> of the standalone HVN and NSGA-III on several artificial problems.</p> <ul> <li>For the standalone HVN algorithm, we tested it on three simple artificial test problems - P1, P2, and P3 (proposed in the above paper): <ul> <li>2D-example-50*.tex: problem P1</li> <li>3D-example1*.tex: problem P2</li> <li>3D-example2*.tex: problem P3</li> </ul> </li> <li>For NSGA-III and the hybridization, we tested them on the equality-constrained DTLZ and Inverted DTLZ (IDTLZ) problems: <ul> <li>Eq1DTLZ.*npz: DTLZ problems</li> <li>Eq1IDTLZ*.npz: IDTLZ problems</li> </ul> </li> </ul>
The computation results of coupled hydrological and hydrodynamic modelling application for the Nemunas River watershed – Curonian Lagoon – South-Eastern Baltic Sea continuum
<p>The datasets provided here were used to analyse the cumulative impacts of climate change in a Nemunas River watershed – Curonian Lagoon – South‑Eastern Baltic Sea continuum by applying a state-of-the-art coupled modelling system, which consists of hydrological and hydrodynamic models.</p> <p>Meteorological data used for running the models were acquired from CORDEX (Coordinated Regional Downscaling Experiment) scenarios for Europe from the Rossby Centre high-resolution regional atmospheric climate model (RCA4), which consisted of four sets of simulations (downscaling) driven by four global climate models:</p> <table> <tbody> <tr> <th>Abbreviation in datasets</th> <th>Model</th> <th><strong>Institution</strong></th> </tr> </tbody> <tbody> <tr> <td>ICHEC</td> <td>EC-Earth</td> <td>Irish Centre for High-End Computing</td> </tr> <tr> <td>IPSL</td> <td>IPSL-CM 5A-MR</td> <td>The Institut Pierre-Simon Laplace</td> </tr> <tr> <td>MOHC</td> <td>HadGEM2-ES</td> <td>Met Office Hadley Centre</td> </tr> <tr> <td>MPI</td> <td>MPI-ESM-LR</td> <td>Max Planck Institute for Meteorology</td> </tr> </tbody> </table> <p> </p> <p>Climate change scenarios and periods:</p> <ul> <li>Historical/reference (1970-2005);</li> <li>RCP4.5 (2005-2100);</li> <li>RCP8.5 (2005-2100).</li> </ul> <p>The datasets consist of time series for the parameters of:</p> <ul> <li><strong>Ice thickness</strong> - average ice thickness in the Curonian Lagoon;</li> <li><strong>Meteorological data</strong> - bias-corrected temperature and precipitation data for the marine and terrestrial areas;</li> <li><strong>Nemunas River discharge</strong> - simulated average daily values for the discharge and water temperature;</li> <li><strong>Salinity</strong> - selected points in the south-eastern Baltic Sea and one point next to Juodkrantė (in the Curonian Lagoon);</li> <li><strong>Water fluxes</strong> - through four predefined cross-sections in the Curonian Lagoon;</li> <li><strong>Water level</strong> - in 10 preselected points in the Curonian Lagoon and South-eastern Baltic Sea;</li> <li><strong>Water residence time</strong> - in the total Curonian Lagoon area, as well as its northern and southern parts;</li> <li><strong>Water temperature</strong> - in 10 preselected points in the Curonian Lagoon and South-eastern Baltic Sea.</li> </ul> <p>Some of the datasets (zip files) have additional information (coordinates, data column explanations, units, etc.) in READ_ME.txt files.</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.