Find research datasets worth reusing

MEaSUREs Greenland Surface Melt Daily 25km EASE-Grid 2.0, Version 1.1.1 (JJA 1980-2022)

This data set offers users a 25 km daily record of surface/near-surface melting on the Greenland Ice Sheet. The presence of melting is determined from brightness temperature data acquired by three satellite-borne microwave radiometers: the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS). Included in this archive are files for the June-July-August (JJA) summer months during 1980-2022, formatted as a separate file for each year. Version 1.1 includes data for 2021-2022 to supplement the original 1980-2020 dataset from version 1. Version 1.1.1 corrects the 2022 file to include data for 2022-08-24 that was missing in version 1.1.

opencc-by-4.0Feb 2023View details →

Supplemental Information to Climate-driven habitat shifts of high-ranked prey species structure Late Upper Paleolithic hunting

The data provided here are the supplemental information accompanying Yaworsky et al, 2023 in the journal Scientific Reports. These data represent the following, which are referenced in the published work at DOI: 10.1038/s41598-023-31085-x. Below is the legend for the Supplementary Information, including how it is referenced within the text of the publication, the file name, and a brief description. More thorough descriptions of the data can be found within the publication in Scientific Reports. Supplementary 1 – UpperPaleoDietV4.html – HTML document of the analyses performed and presented in the paper. This is a Markdown document compiled in R with R code chunks and descriptions. Supplementary 2 – Support Information 2.docx – Word document containing supplementary tables 2 and 3. Supplementary 3 – ArchaeoloigcalDataset_v8.csv – Archaeological data referenced in the Material and Methods. These data are necessary for running the code presented in SI 1. Supplementary 4 – EuroUpperPaleoFaunas_v6.csv – Zooarchaeological data referenced in the Material and Methods. These data are necessary for running the code presented in SI 1. Supplementary 5 – Lupo2016.csv – Data of Arficant fauna weight derived from table in Lupo and Schmitt 2016 (Table 2). These data are necessary for running the code presented in SI 1. Supplementary 6 – PushkinaRaia_FaunaWeights.csv – Data of Pleistocene fauna weights derived from table in Pushkina and Raia 2008 (Table 1). These data are necessary for running the code presented in SI 1. Supplementary 7 – environmental_BG.csv – Data representing background environmental conditions derived from the CHELSA TRaCE21k data. These data are necessary for running the code in SI 1. For more information on the data, methods, and results, please see the main paper.

Dataset for "Magnetic catalysis in the (2+1)-dimensional Gross-Neveu model"

We study the Gross-Neveu model in 2 + 1 dimensions in an external magnetic field B. We first summarize known mean-field results, obtained in the limit of large flavor number N f , before presenting lattice results using the overlap discretization to study one reducible fermion flavor, N f = 1. Our findings indicate that the magnetic catalysis phenomenon, i.e., an increase of the chiral condensate with the magnetic field, persists beyond the mean-field limit for temperatures below the chiral phase transition and that the critical temperature grows with increasing magnetic field. This is in contrast to the situation in QCD, where the broken phase shrinks with increasing B while the condensate exhibits a non-monotonic B-dependence close to the chiral crossover, and we comment on this discrepancy. We do not find any trace of inhomogeneous phases induced by the magnetic field.   If you use this data, please cite the corresponding paper: https://doi.org/10.48550/arXiv.2302.05279 (or better the not-yet-existing published version)

Data from: Coastal upwelling drives ecosystem temporal variability from the surface to the abyssal seafloor.

Abstract Long-term biological time series that monitor ecosystems across the ocean’s full water column are extremely rare. As a result, classic paradigms have yet to be tested. One such paradigm is that variations in coastal upwelling drive changes in marine ecosystems throughout the water column. We examine this hypothesis by using data from three multi-decadal time series spanning surface (0 m), midwater (200-1000 m), and benthic (~ 4000 m) habitats in the central California Current Upwelling System. Data include microscopic counts of surface plankton, video quantification of midwater animals, and imaging of benthic seafloor invertebrates. Taxon-specific plankton biomass and midwater and benthic animal densities were separately analyzed with principal component analysis. Within each community, the first mode of variability corresponds to most taxa increasing and decreasing over time, capturing seasonal surface blooms and lower-frequency midwater and benthic variability. When compared to local wind-driven upwelling variability, each community correlates to changes in upwelling damped over distinct timescales. This suggests that periods of high upwelling favor increases in organism biomass or density from the surface ocean through the midwater down to the abyssal seafloor. These connections most likely occur directly via changes in primary production and vertical carbon flux, and to a lesser extent indirectly via other oceanic changes. The timescales over which species respond to upwelling are taxon-specific and are likely linked to the longevity of phytoplankton blooms (surface) and of animal life (midwater and benthos), that dictate how long upwelling-driven changes persist within each community.   Data set description This data set includes 3 files, one for each community. The files contain plankton biomass (for the surface community) or animal density (for midwater and benthos communities) as a function of sampling time and taxonomic group.  <ul> <li>surface.csv: autotrophic and heterotrophic surface plankton sampled in Monterey Bay by CTD-rosette and analyzed by epifluorescence microscopy and flow cytometry</li> <li>midwater.csv: midwater animals observed by ROV in the Monterey Bay mesopelagic zone from 200-1000m</li> <li>benthos.csv: benthic animals observed by ROV in a ~ 4000 m abyssal seafloor habitat at the base of the Monterey deep-sea fan</li> </ul> Detailed description (see additional details and references in <a href="https://www.pnas.org/doi/10.1073/pnas.2214567120">Messié et al., 2023</a>): Surface time series: Plankton biomass was estimated from surface plankton counts collected using ship-based CTD-rosette at station M1 in Monterey Bay (122.022°W, 36.747°N). This station is part of a 3-station time series program operating in Monterey Bay since 1989 at 3-4 week intervals. Epifluorescence microscopy was used to enumerate and size auto- and heterotrophic plankton. Starting in 1998, flow cytometry samples provided more precise numbers for Synechococcus and eukaryotic picoplankton (Prochlorococcus was not included as no information is available prior to 1998). Standard geometric equations (e.g., ellipsoid, sphere, cylinder, pennate diatom shape) were used to calculate biovolumes of individual cells, and biomass of each plankton group was assessed using biovolume-based carbon conversions. For picoplankton an average value per cell was used: 82 fgC cell-1 for Synechococcus and 530 fgC cell-1 for eukaryotic picophytoplankton (red fluorescing picoplankton). Diatom biovolumes were converted to biomass using log10(Biomass) = 0.76 log10(Volume) - 0.29 where Biomass is in gC and Volume is in 𝜇m3. The ciliate conversion was Biomass = 0.08 * Volume. For all other plankton we used log10(Biomass) = 0.94 log10(Volume) - 0.6. Midwater time series: Quantitative mesopelagic video transects were conducted at a single station in Monterey Bay (Midwater 1, 36°42′N, 122°02′W). The station is located over the axis of the Monterey Submarine Canyon, where the water column is approximately 1600 m deep. Data were collected using remotely operated vehicles (ROVs). Estimates of animal densities using ROV imaging underestimate some groups (notably fishes), but provide a more complete view of life in the ocean than traditional methods such as nets and acoustics, particularly for gelatinous animals. The ROVs conducted horizontal video transects while moving at about 0.5 m s-1 for 10 min. Data for this paper come from approximately monthly transects made at 100 m intervals between 200 - 1000 m from 1997-2017. These years were chosen because the entire mesopelagic water column was more evenly surveyed than in the years prior. In each transect, the community of animals was annotated by professional annotators using the open-source Video Annotation and Referencing System (VARS) software. Annotators identified organisms in transect video to the lowest taxon possible; in many cases to species. We selected 63 taxonomic groups defined at the highest possible taxonomic resolution; annotations not included represent 31% of the total (84% of which are euphausiids, chaetognaths, and unidentified appendicularians). Calibrated cameras on MBARI ROVs and accurate measurement of ROV speed through water, allow for the calculation of volume for each transect. Animal density was calculated for each taxonomic group and each depth-specific transect as the number of individuals divided by the corresponding transect volume, further averaged over the water column from 200 - 1000 m. Midwater transecting methods and their efficacy are well-documented. Benthic time series: Two comparable methods were used to assess benthic communities at Station M (34°50′N, 123°00′W). From 1989-2005, the identification to the lowest possible taxon, and quantity of benthic animals were recorded from images taken by a camera-sled towed along a horizontal transect above the sea floor at a speed of approximately 1 m s-1, taking a film image every 4-5 seconds (water depth ~ 4,100 m). The developed film was projected by a Beseler model 23C-II enlarger for annotation of identifiable animals in images. From 2006-2018, benthic communities were assessed using ROV video transects recorded from approximately 1.3 m above the sea floor, with a view of approximately 1 m wide, and length typically approximately 1 km. Water depth for these transects was approximately 4,000 m, the lower depth limit of the ROV. Animals visible in the video were identified and annotated using VARS. The 2006 change in sampling method and in time series location and depth was found to have little impact on the megafauna time series.

Chemical structures, Cell Painting and transcriptional profiles for compound bioactivity prediction.

This is the related data, both input and produced for the paper <a href="https://doi.org/10.1101/2020.12.15.422887">"Predicting compound activity from phenotypic profiles and chemical structures"</a>. This data can be merged with <a href="https://github.com/CaicedoLab/2023_Moshkov_NatComm">paper's GitHub repository</a> for reproduction. Folders and files and are described below: <pre><code>├── assay_data ├── assay_matrix_discrete_270_assays.csv Assay matrix with hits for assays (270) and compounds (16170). Note that this is the final file that we used to produce splits. ├── assay_metadata.csv Assay metadata ├── broad_ids.txt List of broad ids used in this study. That is an unfiltered list of compounds required by some analysis scripts. ├── smiles.txt Same as broad_ids.txt, but SMILES strings. ├── feature_data (for 16978 compounds, can be masked with ./misc/compounds16978to16170.npy) ├── cp.npz Classical chemical features ├── ge.npz Gene expression features ├── ge_scale.npz Gene expression scaled features ├── mo.npz Morphology features (not batch corrected) ├── mobc.npz Morphology features (batch corrected) ├── misc ├── compound_analysis.npz Compounds in the dataset identified as PAINS ├── compounds16978to16170.npy Used to filter features from the bigger set of compounds to the final one ├── fingerprints.npz Calculated fingerprints of compounds, those were then used to calculate similarity ├── similarity_fingerprints.npz Similarity matrix for compounds (16978) ├── population_normalized.csv.gz Well-level morphological profiles that were used for batch-correction ├── Table for PUMA Excel file with additional data and plots ├── predictions ├── scaffold_median(mean)_AUC.csv Aggregated median(mean) AUC scores over scaffold-based cross-validation splits. In the paper, median results were reported. ├── scaffold_median(mean)_EF.csv Aggregated median(mean) enrichment factor (EF) over scaffold-based cross-validation splits. In the paper, median results were reported. ├── toprank_chemical_cv{}_hitsnorm.csv Those files are needed to create enrichment plots and contain hit rate and top rank hit rate. ├── Each folder here stands for an experiment type, the number in the folder name is a number of the split. Inside each folder there are the following elements: ├── predictions Folder with predictions for each assay-compound pair for each modality ├── 2022_01_evaluation_all_data.csv File with AUC scores for each assay for the test set in the split ├── 2022_01_evaluation_all_data_EF.csv File with enrichment factor (EF) values for each assay for the test set in the split. Those files exist only for *chemical* folders. ├── assay_matrix_discrete_train(test)_old_scaff.csv Training and test subsets of data for the split. The first column contains broad_id. ├── assay_matrix_discrete_train(test)_old_scaff.csv Same, but SMILES strings in the first column. Those files are used as input to ChemProp! Experiments in this folder are the following: - chemical Scaffold-based 5-fold cross-validation splits, the main results in the paper are reported with this series of experiments. - chemical_bal Same splits as in chemical, but training were run with ChemProp built-in data balancing. - chemical_st Same splits as in chemical, but separate models were trained for each assay. - CV Random 5-fold cross-validation splits. - GE 5-fold cross-validation splits based on same-size clustering of gene expression features. - MOBC 5-fold cross-validation splits based on same-size clustering of batch-corrected morphology features. - random 10 random splits, ~80% of compounds in the training set and the rest in the test set. ├── splitting This folder contains numpy files which help to match compounds and features to create training and test sets for a split, which can be reused in the analysis notebook for data preparation. ├── scaffold_based_split.npz Splitting for scaffold-based splits. ├── random_split_{}.npz Random split indices of test set compounds (10 files). ├── cross_validation_indicies.npz Indices for random cross-validation splits ├── GE_clusters_size_constrained.npz Indicies of clusters of same-size clustering for gene-expression features. ├── MOBC_clusters_size_constrained.npz Indices of clusters of same-size clustering for batch-corrected morphology features.</code></pre>

opencc-by-4.0Oct 2022View details →

DisVis-based filtering of contacts from co-evolution data (or other sources)

Dataset described in the manuscript: Improving the Quality of Co-evolution Intermolecular Contact Prediction with DisVisSiri Camee van Keulen, Alexandre M.J.J. Bonvin Details about the data set can be found at:  https://github.com/haddocking/contact-filtering This archive contains in addition all the models generated with HADDOCK.

Nuclear Magnetic Resonance values for the Eptachori, Pentalofos and Tsotyli formations in West Macedonia

The data comprises work under the Project Pilot Strategy GA No. 101022664, funded by the European Union.  The work relates to rock samples collected in 2022 in West Macedonia, Greece. For full details, please refer to the following: <ol> <li>Tsotyli formation: <a href="https://app.geosamples.org/sample/igsn/IE5770001">https://app.geosamples.org/sample/igsn/IE5770001</a> - WGS84 Lat : 40.3075, WGS84 Long : 21.3354</li> <li>Pentalofos formation:  <a href="https://app.geosamples.org/sample/igsn/IE5770002">https://app.geosamples.org/sample/igsn/IE5770002</a> - WGS84 Lat : 40.1332, WGS84 Long : 21.1997</li> <li>Eptachori formation: <a href="https://app.geosamples.org/sample/igsn/IE5770003">https://app.geosamples.org/sample/igsn/IE5770003</a> - WGS84 Lat : 40.1332, WGS84 Long : 21.1997</li> </ol> The focus of the work is related to CO2 storage in appropriate saline aquifers in West Macedonia. The bulk samples were shipped to IFP Energies for porosity and permeability laboratory investigation conducted by Nuclear Magnetic Resonance techniques.  Further to the raw data from the NMR, a depiction of the latter is provided in the corresponding figures

opencc-by-4.0Dec 2022View details →

Open Surface Drifter Data - Pirita river

This dataset contains the surface drifter tracks collected in Pirita River (Estonia) to test the open drifter presented in the publication https://doi.org/10.3390/s22249918

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An IoT-Enriched Event Log for Process Mining in Smart Factories

DEPRECATED - current version:  <a href="https://figshare.com/articles/dataset/Dataset_An_IoT-Enriched_Event_Log_for_Process_Mining_in_Smart_Factories/20130794">https://figshare.com/articles/dataset/Dataset_An_IoT-Enriched_Event_Log_for_Process_Mining_in_Smart_Factories/20130794</a>   Modern technologies such as the Internet of Things (IoT) are becoming increasingly important in various domains, including Business Process Management (BPM) research. One main research area in BPM is process mining, which can be used to analyze event logs, e.g., for checking the conformance of running processes. However, there are only a few IoT-based event logs available for research purposes. Some of them are artificially generated, and the problem occurs that they do not always completely reflect the actual physical properties of smart environments. In this paper, we present an IoT-enriched XES event log that is generated by a physical smart factory. For this purpose, we created the DataStream XES extension for representing IoT-data in event logs. Finally, we present some preliminary analysis and properties of the log.

opencc-by-4.0Dec 2021View details →

Global distribution of predicted soil types at 1 km resolution based on the WRB 2022 classification

Global maps at 1 km spatial resolution of the predicted soil types (0–100% probabilities) at 1 km resolution based on the <a href="https://www.fao.org/soils-portal/data-hub/soil-classification/world-reference-base/en/">WRB 2022</a> (World Reference Base the international standard for soil classification) classification system. The training data comes from the following 3 main sources: <ol> <li>WOSIS points available via: <a href="https://www.isric.org/explore/wosis">https://www.isric.org/explore/wosis</a>;</li> <li>HWSD v2 (random draw of cca 20,000 points): <a href="https://iiasa.ac.at/models-tools-data/hwsd">https://iiasa.ac.at/models-tools-data/hwsd</a>;</li> <li>Other national datasets / data from publications and projects.</li> </ol> Predictions are based on using Rando Forest algorithm as implemented in the <a href="https://www.randomforestsrc.org/">randomForestSRC package</a> with cca 190 covariate layers representing soil forming factors (CHELSA Climate, Global Lithological DB GLiM, MODIS EVI and LST long-term derivatives, Digital Terrain model parameters and similar). All TIF files are provided as <a href="https://www.cogeo.org/">COGs</a>, which means that you can open them directly in QGIS or similar. Publication explaining all modeling steps is pending. Update of the predictions takes about 4–5 hrs and will be regularly run provided that new training points are available. Disclaimer: These are initial results with limited accuracy and possible issues with quality of training points, location errors and harmonization issues. Use at own risk. Note: original list of soil types have been subset to classes that appear at least 10 times and at least in 2 countries. If you notice an error or artifact please report via <a href="https://github.com/OpenGeoHub/SoilTypeMapping">the Github repository</a>. Help us improve this dataset by contributing training points.

Interactions between bats and agricultural insect pests worlwide

This database illustrates the interactions between bats and agricultural insect pests detected conducting a systematic review in October 2022, entitled "Pest suppression by bats and management strategies to favour it: a global review", to be published in the journal Biological Reviews. Methodology applied: We compiled a comprehensive list of agricultural insect pests occurring in temperate and tropical regions. Since no more recent public documents or published lists were available, we extracted the main agricultural insect pests cited in Hill (1983, 1987). Note that species might be considered pests in certain regions while not in others, meaning that this comprehensive list will need careful review by entomologists and local or regional experts for use in agricultural management. We assembled a first list of 1,237 insect pest species or genera extracted from Hill (1987, 1983). We then conducted a literature search in the ISI Web of Science using the R package wosr. We searched for any indexed document containing the following terms in the topic field: "pest species name" AND "bat*", where ‘pest species name’ refers to each of the 1237  species. After the first check of the articles found, we added 562 new pest species to the first list, which were not included in Hill (1987, 1983), but were mentioned in the papers found. Thus, the updated list consisting of 1799 insect pest species was used again to perform the same literature search with the R package wosr. In addition, we also performed three literature searches including the following terms: (i) "bat" or "bats", "diet*", and "insect*"; (ii) "bat" or "bats", "predat*", and "insect*"; (iii) "bat" or "bats", "diet*", and "arthropod*". We identified a total of 1125 articles, of which we retained only those that identified bat prey at the genus or species level (N = 95). Predator - prey interactions were extracted from the articles reviewed and added in this data set, showing each bat species with the insect pest species it consumed, as well as the method used to confirm predation.

CoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil and biofuel CO2 emission inventories

CoCO2-MOSAIC 1.0 is a global mosaic of regional bottom-up inventories of anthropogenic CO2 emissions developed in the framework of the CoCO2 project (<a href="https://coco2-project.eu/">https://coco2-project.eu/</a>). CoCO2-MOSAIC 1.0 provides gridded (0.1˚×0.1˚) monthly emissions fluxes of CO2 fossil fuel (CO2ff, long cycle) and CO2 biofuel (CO2bf, short cycle) for the years 2015 to 2018 disaggregated in seven sectors: energy_s (super-emitting sources above 7.9e-6 kg/m2/s), energy_a (average emitters), manufacturing, settlements, transport, aviation land/take-off (LTO) and other. The regional inventories included are CAMS-GHG-REG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (South-East Asia) and VULCAN 3.0 (USA). EDGAR 6.0 and CAMS-GLOB-SHIP 3.1 are used for gap-filling missing sectors and regions. CAMS-GLOB-TEMPO 3.1 is used for temporal disaggregation of inventories providing annual emissions. Aviation emissions from climb, descent, and cruise are not covered by regional inventories and are provided as a separate file. Note that 2015 is the only year when all regional inventories are simultaneously available.   Compared to global inventories, CoCO2-MOSAIC 1.0 includes all the regional information available without the limitation of providing spatially consistent emissions. Therefore, CoCO2-MOSAIC 1.0 can be used as a global baseline inventory due to the higher level of detail, higher spatial resolution, and country-specific information included by regional inventories.  For further details see Urraca et al. 2023 (ESSD submitted). The paper (i) describes the CoCO2-MOSAIC methodology and (ii) uses the mosaic to inter-compare the most widely used global inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0/7.0, ODIAC v2020b, and CEDS v2020_04_24.

Marine magnetic anomaly data from high resolution surveys off the SW Portuguese coast

This dataset contains magnetic anomaly grids that result from the full processing of marine magnetic data collected off the SW Portuguese coast between 2014 and 2019. A total area of ~4400 km2 was surveyed with average line spacing of 1 nautic mile. Surveys covered the continental shelf and in some regions reaching up to 2500 m bathymetric levels. Total magnetic field data were acquired with a G882 Cesium vapor marine magnetometer towed, towed at sea surface. Full processing of magnetic data included: layback correction; noise removal; IGRF subtraction; base station correction; line leveling; minimum curvature gridding. The resulting sea level magnetic anomaly grid was further processed for upward continuation and reduction to the pole, providing additional outputs.  The following grids are provided in georeferenced geotiff format: <ul> <li>Magnetic anomaly (sealevel)</li> <li>Magnetic anomaly reduced to the pole (sealevel)</li> <li>Magnetic anomaly upward continued to 200 m height </li> <li>Magnetic anomaly upward continued to 200 m height, reduced to the pole</li> <li>Magnetic anomaly upward continued to 3000 m height </li> <li>Magnetic anomaly upward continued to 3000 m height, reduced to the pole</li> </ul> Published in: Neres, M., P. Terrinha, J. Noiva, P. Brito, M. Rosa, L. Batista, C. Ribeiro (2023). New Late Cretaceous and CAMP magmatic sources off West Iberia, from high-resolution magnetic surveys on the continental shelf. Tectonics. doi: 10.1029/2022TC007637

Sparse observations induce large biases in estimates of the global ocean CO2 sink: an ocean model subsampling experiment

Dataset underlying the analysis in Hauck et al., 2023: Sparse observations induce large biases in estimates of the global ocean CO2 sink - an ocean model subsampling experiment, Philosophical Transactions A Surface ocean partial pressure of CO2 (pCO2) and air-sea CO2 flux reconstructions, using two mapping methods (MPI-SOM-FFN, CarboScope) three different sampling masks: SOCAT, SOCAT+SOCCOM, IDEAL (based on bgcArgo, Roemmich et al., 2019). Also, all FESOM-REcoM output fields that were used in the reconstructions are provided. We further provide the three masks that were used for subsampling: SOCAT, SOCAT+SOCCOM, IDEAL (bgcArgo).

GoNEXUS SEF application: Andalusia practical case study

Data for the figures in the journal article: Gonzalez-Rosell A, Arfa I and Blanco M (2023) Introducing GoNEXUS SEF: a solutions evaluation framework for the joint governance of water, energy, and food resources. Sustainability Science. https://doi.org/10.1007/s11625-023-01324-1 Figure 3. Generation of new quantitative evidence: Percentage of variation of indicators between the water price scenario (WP) and baseline scenario (BS) for the year 2030. Source: Own elaboration based on the participatory SDM results (González-Rosell et al., 2020). Figure 4. Cross-impact matrix of the indicator system and solution from Table 2 for the water pricing in the Andalusia case study. Source: Own elaboration. Figure 5. Analysis of the degree of distribution of the network system based on the cross-impact matrix in Figure 4. Source: Own elaboration. Figure 6. (a) Full network graph: links between 16 indicators and WP solution based on the cross-impact matrix. (b) Tree network graph of the total influence of the WP solution at the first and second order based on the cross-impact matrix. Colour scale as in Figure 8. Synergies (green) and trade-off (red) of the WP solution on policy objectives and nexus objectives in Andalusia. Source: Own elaboration.

Imaging Temperature and Thickness of Thin Planar Liquid Water Jets in Vacuum - data

Data set pertaining to the article "Imaging Temperature and Thickness of Thin Planar Liquid Water Jets in Vacuum", published in Struct. Dyn. 10, 034901 (2023), <a href="https://doi.org/10.1063/4.0000188" target="_blank" rel="noopener">https://doi.org/10.1063/4.0000188 </a>. The following data are provided: <table> <tbody> <tr> <td>(zip-)file/Folder</td> <td>Description</td> <td>Format</td> <td>Extension</td> </tr> <tr> <td>IR_images/calibration_data/vacuum</td> <td> Snapshots from a thermographic movie of our flat jet running in vacuum, at thirty different background temperature. (A snapshot shown in Fig. 3a, rhs.) </td> <td> temperature values per camera pixel (°C), 640 row * 480 columns, semicolon-separated ascii data </td> <td>.asc</td> </tr> <tr> <td>IR_images/calibration_data/1atm</td> <td>As above, for our flat jet running in atmosphere. (Three snapshots shown in Fig. 2a.)</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_images/calibration_data/chipnozzle</td> <td>As above, for a flat jet produced from a chip nozzle, and running in atmosphere.</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_images/raw_data</td> <td>As above, for various conditions of the flat jet environment as detailed in table exp_settings.csv.</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_video</td> <td>Two thermographic movies recorded of our flat jet at varied conditions of the jet environment detailed in table chamber_pressure.pdf.</td> <td>Radiographic image stream, suitable for opening with free software Optris Pix Connect.</td> <td>.ravi</td> </tr> <tr> <td>FJ_cooling_2D.mph</td> <td>Input file for 2D finite element simulation of our flat jet.</td> <td>Input file suitable for Comsol software, proprietary format.</td> <td>.mph</td> </tr> <tr> <td>Y_Z_Temp_Comsol.txt</td> <td>Ascii representation of our simulated temperature profile (Fig. S7 (SI)).</td> <td>List of (y,z,T) tupels, with (y,z) in m and T in °C.</td> <td>.txt</td> </tr> </tbody> </table>   In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .

Global Meteor Network observations of Crew-5 Dragon trunk re-entry 2023-04-27

This dataset contains video observations by some stations of the Global Meteor Network of the re-entry of the Crew-5 dragon trunk above Arizona on 2023-04-27 around 08:52 UTC. There are several types of files: <ul> <li>FF files: these are 10.24 second videos compressed in the four-frame format. They are just FITS files with four frames, containing per pixel 1) the maximum value over 256 frames 2) the frame nr (between 0 and 255) where the maximum occurred 3) the mean value of all 256 frames and 4) the RMS of the 256 values.</li> <li>FR files: compressed video recordings of detected fireballs. These can be read with the RMS software.</li> <li>MP4 files: rendered movies of combined FF and FR files for one station (more can be made with FR_binviewer from RMS software).</li> <li>Platepar-files: these contain astrometry corresponding to the FITS files. These can be interpreted by the RMS software.</li> <li>ECSV files: these contain manually picked points (with SkyFit2.py from RMS) along the track of the reentry. For each point, time and apparent coordinates are recorded. These files can be interpreted by the WesternMeteorPyLib trajectory solver.</li> <li>trajectory-points.txt: solutions from the trajectory solver.</li> <li>reentry-map-v4.png: a rendered map of the trajectory (made in QGIS).</li> <li>compilation.png: rendered version of the FF-files of most stations.</li> </ul> The files can be processed with the software in https://github.com/CroatianMeteorNetwork/RMS and https://github.com/wmpg/WesternMeteorPyLib.

Murreviikko: an Annotated and Normalized Corpus of Dialectal Finnish Tweets

Murreviikko (literally 'Dialect week') is a campaign founded in the University of Eastern Finland to promote the use of Finnish dialects in social media. It started in 2020 and takes place mid-October. The original data was collected from Twitter with the search word murreviikko ('dialect week') and hashtag #murreviikko separately for 2020, 2021 and 2022. The current dataset combines all the original collections. The tweets are dialectologically annotated on two levels: following the East-West division of Finnish dialects, and following a seven-way division of Finnish dialects (South-West, Häme, Southern Ostrobothnia, Central and Northern Ostrobothnia, Far North, Savo, and South-East), appended with the Helsinki slang. There is also a class for dialectal tweets, which are not discernible (NA) because of contrasting or scarce dialectal features. The original tweets are normalized to a phonetic standard, but word order is not altered, or grammar rules of standard Finnish followed otherwise. This means that for instance standard Finnish possessive suffixes (minun kirja-ni 'my book-my') are not added if they are not present in the original tweet (minun kirja). Likewise, dialect words are not corrected to the standard alternative, even if such words would exist (pruukata > pruukata instead of standard tavata). Following the rules of the Twitter API, this repository only includes the tweet id's, dialect annotations and normalizations. The original tweets are available for scientific use by request, as granted by the European Union’s Digital Single Market directive (2019/790).

Calculated moisture sources for the Yangtse River Valley for past, present and future climate using a Lagrangian moisture source diagnostic

This dataset contains calculated moisture sources for the Yangtse River Valley (110–122°E and 27–33°N, eastern China) for past, present and future climate using a Lagrangian moisture source diagnostic. The dataset comprises gridded monthly moisture source data files and monthly time series files for a Last Glacial Maximum (LGM) simulation and a Pre-Industrial reference simulation (PRE) with CAM5.1 using prescribed sea surface temperatures, and a control simulation (CTL, 2001-2010) and a climate scenario run with representative concentration pathway 6 (RCP, 2061-2070) with the coupled NorESM-1M model. Each file covers a 10-year time period, computed with the Lagrangian moisture source diagnostic WaterSip (Sodemann et al., 2008).