Find research datasets worth reusing

Atomic-scale environment of niobium in minerals as revealed by X-ray absorption spectroscopy at the Nb K-edge

This data set provides the raw EXAFS and XRD data of the manuscript 'Atomic-scale environment of niobium in minerals as revealed by X-ray absorption spectroscopy at the Nb K-edge' submitted to European Journal of Mineralogy. - EXAFS data files were converted into .txt for more accessibility. Pcl is the abbreviation for pyrochlore. The files are accoridngly labelled. - XRD data files for synthetic Nb-doped compounds start with 'XRD_...'. - The refined crystal structure of hydropyrochlore is 'hydropcl_01.cif' file.

opencc-by-3.0May 2022View details →

Gollum: A Gold Standard for Large Scale\\Multi Source Knowledge Graph Matching

The set of Knowledge Graphs (KGs) generated with automatic and manual approaches is constantly growing. For an integrated view and usage, an alignment between these KGs is necessary on the schema as well as instance level. There are already approaches which try to tackle this multi source knowledge graph matching problem, but large gold standards are missing to evaluate their effectiveness and scalability. In particular, most existing gold standards are fairly small and can be solved by matchers which match exactly two KGs (1:1), which are the majority of existing matching systems. We close this gap by presenting Gollum -- a gold standard for large-scale multi source knowledge graph matching with over 275,000 correspondences between 4,149 different KGs. They originate from knowledge graphs derived by applying the DBpedia extraction framework to a large wiki farm. Three variations of the gold standard are made available: (1) a version with all correspondences for evaluating unsupervised matching approaches, and two versions for evaluating supervised matching: (2) one where each KG is contained both in the train and test set, and (3) one where each KG is exclusively contained in the train or the test set. We plan to extend our KG track at the Ontology Alignment Evaluation Initiative (OAEI) to allow for matching systems  which are specifically designed to solve the multi KG matching problem. As a first step towards this direction, we evaluate multi source matching approaches which reuse two-KG (1:1) matchers from the past OAEI.   Due to the size of the KG files, they are hosted at the institute: <a href="http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/40K.tar">http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/40K.tar</a>    (50,3 GB) <a href="http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/all.tar">http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/all.tar</a>      (74,7 GB) <a href="http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/gold.tar">http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/gold.tar</a>   (25,3 GB)

FIG. 3 in Silica-scaled chrysophytes from the Ukrainian Polissia

FIG. 3. — Mallomonas Perty taxa from the Ukrainian Polissia: A, Mallomonas matvienkoae Asmund & Kristiansen, SEM; B, Mallomonas ouradion K.Harris & D.E.Bradley, SEM; C, Mallomonas cf. pseudomatvienkoae B.Y.Jo, W.Shin, H.S.Kim, Siver & R.A.Andersen, SEM; D, Mallomonas paludosa Fott; E, F, Mallomonas papillosa K.Harris & D.E.Bradley emend. K.Harris, TEM (E) and SEM (F); G, Mallomonas pillula f. valdiviana Dürrschmidt, SEM; H, Mallomonas punctifera Korshikov, SEM; I, Mallomonas pugio D.E.Bradley, SEM; J, Mallomonas rasilis Dürrschmidt, SEM; K, L, Mallomonas schwemmlei Glenk emend. Glenk & Fott, body (K) and apical (L) scales, SEM; M, Mallomonas striata Asmund, SEM; N, Mallomonas teilingii W.Conrad, SEM; O, Mallomonas teres Nemcova & Kapustin, SEM; P, Mallomonas cf. tonsurata Teiling, SEM. Scale bars: A-F, I-M, O, P, 1 µm; G, 0.5 µm; H, N, 2 µm.

FIG. 1 in Silica-scaled chrysophytes from the Ukrainian Polissia

FIG. 1. — Chrysosphaerella Lauterborn, Paraphysomonas De Saedeleer emend. Scoble & Cavalier-Smith, Lepidochromonas Kristiansen and Spiniferomonas E.Takahashi species from the Ukrainian Polissia: A, Chrysosphaerella brevispina Korshikov, spine scale, TEM; B, Stomatocyst of Chrysosphaerella coronacircumspina Wujek & Kristiansen with broken spine scale and plate scales, SEM; C, Paraphysomonas acuminata Scoble & Cavalier-Smith, TEM; D, Paraphysomonas truncata (Preisig & D.J.Hibberd) Scoble & Cavalier-Smith, TEM; E, Paraphysomonas sp., scale with broken spine, SEM; F, Lepidochromonas sp., TEM; G, Spiniferomonas bourrellyi E.Takahashi, spine scale with plate scales attached to it, SEM; H, Spiniferomonas cf. trioralis E.Takahashi, spine scale, SEM; I, Chrysosphaerella longispina J.B.Petersen & J.B.Hansen, SEM. Scale bars: A, D, E, G, H, 1 µm; B, C, 2 µm; F, 0.5 µm; I, 5 µm.

FIG. 2 in Silica-scaled chrysophytes from the Ukrainian Polissia

FIG. 2. — Mallomonas Perty taxa from the Ukrainian Polissia: A, Mallomonas acaroides Perty emend. Iwanoff, SEM; B, Stomatocyst of Mallomonas cf. akrokomos Ruttner, SEM; C, Mallomonas annulata (D.E.Bradley) K.Harris, SEM; D, Mallomonas asmundiae (Wujek & van der Veer) K.H.Nicholls, SEM; E, F, Mallomonas calceolus D.E.Bradley, SEM; G, Mallomonas canina Kristiansen, SEM; H, Mallomonas caudata Iwanoff emend. Willi Krieger, SEM; I, Mallomonas corcontica (Kalina) L.Ş.Péterfi & Momeu, SEM; J, Mallomonas costata Dürrschmidt, SEM; K, Mallomonas crassisquama (Asmund) Fott, SEM; L, Mallomonas cratis K.Harris & D.E.Bradley, TEM; M, Mallomonas elongata Reverdin, SEM; N, Mallomonas heterospina J.W.G.Lund, SEM; O, Stomatocyst of Mallomonas mangofera var. foveata (Dürrschmidt) covered with scales, SEM. Scale bars: A, D-F, H-K, 1 µm; B, C, L-O, 2 µm; G, 0.5 µm.

opencc-by-4.0Nov 2022View details →

Maxima of Station-based Rainfall Data over Different Accumulation Durations and Large Scale Covariates

Description These data were used in the study "Non-Stationary Large-Scale Statistics of Precipitation Extremes in Central Europe" (Fauer et al., 2022, https://doi.org/10.21203/rs.3.rs-2542862/v1). Rainfall data were collected from stations by the German Meteorological Service (DWD) and Wupperverband (corrected data). Raw time series data from the German Meteorological Service is publicly available under https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/. Only the annual and monthly precipitation maxima over different durations are published here. For more detailed information on our work and the modeling of extreme rainfall data, see also Fauer et al. (2021, https://doi.org/10.5194/hess-25-6479-2021).   Files <ul> <li>precipMax_and_covariates.csv: This file contains aggregated rainfall data over different durations and for different stations ("xdat"). Also, it contains covariates for the variables "blocking", "NAO", surface air temperature ("tas"), humidty and their polynomials up the the fourth order.</li> <li>precip_meta.csv: This file contains additional information of the different stations such as longitude, latitude, altitude, temporal resolution (m=minutely, h=hourly, d=daily), group. The same group is assigned to stations which have a distance of less than 250 meters and can be treated as one station. The value in "group" corresponds to the value "station" in precipMax_and_covariates.csv.</li> </ul> Abstract of the according study Extreme precipitation shows non-stationary behavior over time, but also with respect to other large-scale variables. While this effect is often neglected, we propose a model including the influence of North Atlantic Oscillation, time, surface temperature and a blocking index. The model features flexibility to use annual maxima as well as seasonal maxima to be fitted in a generalized extreme value setting. To further increase the efficiency of data usage maxima from different accumulation durations are aggregated so that information for extremes on different time scales can be provided. Our model is trained to individual station data with temporal resolutions ranging from one minute to one day across Germany. The models are selected with a stepwise BIC model selection and verified with a cross-validated quantile skill index. The verification shows that the new model performs better than a reference model without large scale information. Also, the new model enables insights into the effect of large scale variables on extreme precipitation. Results suggest that the probability of extreme precipitation increases with time since 1950 in all seasons. High probabilities of extremes are positively correlated with blocking situations in summer and with temperature in winter. However, they are negatively correlated with blocking situations in winter and temperature in summer.   Acknowledgements We would like to thank the German Weather Service (DWD), especially Thomas Junghänel, and the Wupperverband, especially Marc Scheibel, for maintaining the station-based rainfall gauge and providing us with data.   Funding This study is part of the ClimXtreme project (Grant number 01LP1902H) and is sponsored by the Federal Ministry of Education and Research in Germany.   Changelog <ul> <li>added humidity as large-scale covariate</li> <li>large-scale covariates are normalized to mean=0 and standard deviation=1</li> </ul>

FIG. 4 in Silica-scaled chrysophytes from the Ukrainian Polissia

FIG. 4. — Synura Ehrenb. taxa from the Ukrainian Polissia: A, Synura conopea Kynčlová & Škaloud, TEM; B, Synura echinulata Korshikov, SEM; C, Synura glabra, TEM; D, Synura heteropora Škaloud, Škaloudová & Procházková, TEM; E, Synura korshikovii Kapustin & Gusev, SEM; F, Synura macropora Škaloud & Kynčlová, TEM; G, Synura petersenii Korshikov, TEM; H, Synura petersenii f. columnata Siver, TEM; I, Synura sphagnicola (Korshikov) Korshikov, SEM; J, Synura spinosa f. spinosa Korshikov, SEM; K, Synura spinosa f. longispina J.B.Petersen & J.B.Hansen, TEM; L, Synura uvella Ehrenb. emend. Korshikov, SEM. Scale bars: A, C, D, F-H, K, 2 µm; B, E, I, J, L, 1 µm.

Streamflow drought hazard indicators for monitoring drought hazard for human water supply and river ecosystems at the global scale (WaterGAP 2.2d, WFDEI-GPCC)

1) Streamflow drought hazard indicators (SDHIs) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015: Indicators of drought magnitude: SSI1, SSI12, EP1, RDQI1 Indicators of drought severity: CDQI1-Q50, CDQI1-Q50_f, CDQI1-Q80, CDQI1-Q80_f, CDQI1-Q80-HS, CDQI6-Q80, CDQI6-Q80_f, CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-WUs-EFR_f, CEP1(20%), CEP1(20%)_f, CRDQI1(-50%), CRDQI1(-50%)_f 2) WaterGAP grid cell IDs ("arcid") with longitude and latitude: (WaterGAP_ArcID_lon_lat.txt) 3) Streamflow observations at 220 GRDC gauging stations and list of the 220 GRDC station numbers with related WaterGAP grid cell ID ("arcid"): Observed_monthly_streamflow_km3month_220_calstations_1986_2015.txt GRDC_number_WaterGAP_ArcID_220_stations.txt 4) SDHIs for four GRDC gauging stations: time_series_danube_river.txt, time_series_angara_river.txt, time_series_white_river.txt, time_series_orange_river.txt 5) WaterGAP output: Mean monthly surface water abstractions in km3 per month: Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt

Potential impact of assimilating visible and infrared satellite observations compared to radar reflectivity for convective‐scale NWP

Contains <ul> <li>raw_data <ul> <li>nature run initial conditions for the cases "random" and "warm-bubble"</li> <li>ensemble initial condition sounding profiles</li> </ul> </li> <li>evaluation_metrics <ul> <li>csv files of FSS, RMSE, MAE, ensemble spread, mean difference to reproduce figures of FSS, RMSE and spread in the paper and more. See the README.txt</li> </ul> </li> <li>figures <ul> <li>Map_VIS06 ... Simulated satellite images of visible reflectance for ensemble members and truth</li> <li>Map_VIS06_probability ... Map of ensemble probability for visible reflectance > 0.6 and the nature in red contours</li> </ul> </li> </ul>

opencc-zeroApr 2023View details →

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Data from: Using environmental DNA metabarcoding to monitor fish communities in small rivers and large brooks: Insights on the spatial scale of information

Monitoring fish communities is central to the evaluation of ecological health of rivers. Not only presence/absence of species is important to assess, but also the species composition of local fish assemblages is a crucial parameter. Lotic fish communities are traditionally monitored via electrofishing, characterized by a known limited efficiency and high survey costs. The use of environmental DNA-based analyses could serve as a non-destructive alternative, but this approach requires further insights in practical sampling schemes incorporating transport and dilution of the eDNA fragments; as well as optimization of molecular detection in terms of predictive power and quality assurance. By introducing fifteen species known to occur in Belgian waters via a controlled cage experiment, we aim to extend the knowledge on streamreach of eDNA in small rivers and large brooks, as laid out in the European Water Framework Directive's water typology. Introducing fish communities in two transects of a species poor river characterized by contrasting river discharge rates, we found strong and significant correlations between the eDNA relative abundances and the relative biomass per species in the cage community. Despite a decreasing correlation over distance, the underlying community composition remained stable over a distance of 300 m up to 1 km downstream of the cages, depending on the river discharge rate. Such decrease in similarity between relative source biomass and the corresponding eDNA-based community profile with increasing distance downstream from the source, can partly be attributed to variation in species-specific eDNA persistence. Our findings offer novel insights on eDNA behaviour and characterization of riverine fish communities. We conclude that water sampled from a relatively small river offers an adequate snapshot of the total fish community composition occurring within an upstream perimeter ranging between 300 and 1000 meters. The potential application for other river systems is discussed in this study.

79 Block--63k or 50k scale Maps of South Asia

189 maps of the Survey of India covering block 79. In the time since this first batch of maps (see the list below) was posted additional maps of Block 79 have become available.  These additional maps are posted on a separate page, and can be reached by clicking the link(s) below.  Included in the final set of additional maps is an Index Map showing the total 63k/50k holdings in Block 79. <table> <tbody> <tr> <td><a href="../records/10460656">63k/50k Maps of South Asia--Block 79--additional 1--20240105</a></td> </tr> </tbody> </table>

NsCircle datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"

Datasets with the simulations of the incompressible flow around an elliptical as described by the incompressible Navier-Stokes equations. These simulations were used to train and test the MuS-GNN models in the paper:     Multi-scale rotation-equivariant graph neural networks for     unsteady Eulerian fluid dynamics (https://doi.org/10.1063/5.0097679) The datasets are:   - train/NsEllipse   - test/NsEllipseLowRe   - test/NsEllipseHighRe   - test/NsEllipseThin   - test/NsEllipseThick   - test/NsEllipseNarrow   - test/NsEllipseWide   - test/NsEllipseAoA     To cite these datasets, use the following reference: Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022). @article{lino2022multi,     author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris},     title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}},     journal = {Physics of Fluids},     volume = {34},     year = {2022},     url = {https://doi.org/10.1063/5.0097679}, }

Simulation data -- Electron-scale current sheet for southward IMF KHI event (Part 1)

The data from a VPIC simulation of the electron-scale current sheet at a KH vortex for the southward IMF. Simulation is performed using the VPIC code (githab.com/lanl/vpic). This part includes data for the magnetic field (bx, by, bz), the electric field (ez), the electron velocity (uex, uey, uez), the ratio between parallel and perpendicular electron temperature (tepar_teper) and the energy conversion measure J.E' (JEp). Also included is the info file of the simulation. Further we included the orbit-files containing the data structures from the virtual spacecraft orbits set in the simulation. The data structure is as follows: it (time number=timestep/705), i (grid number in x), k (grid number in z), time (in 1/Omega_i~1s for 10nT), x in de, z in de, bx, by, bz, ex, ey, ez, uix, uiy, uiz, uex, uey, uez, pi, pe, ni, ne, e_para, e_perp, JE', Te_para, Te_perp The normalization of the data is the ones for the raw VPIC data.

NsEllipse datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"

Datasets with simulations of the incompressible flow around an elliptical cylinder as described by the incompressible Navier-Stokes equations. These simulations were used to train and test the MuS-GNN models in the paper:     "Multi-scale rotation-equivariant graph neural networks for     unsteady Eulerian fluid dynamics" (https://doi.org/10.1063/5.0097679) The datasets are:   - train/NsEllipse   - test/NsEllipseLowRe   - test/NsEllipseHighRe   - test/NsEllipseThin   - test/NsEllipseThick   - test/NsEllipseNarrow   - test/NsEllipseWide   - test/NsEllipseAoA   To cite these datasets, use the following reference: Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022). @article{lino2022multi,     author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris},     title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}},     journal = {Physics of Fluids},     volume = {34},     year = {2022},     url = {https://doi.org/10.1063/5.0097679}, }

Local response and emerging nonlinear elastic length scale in biopolymer matrices

Dataset corresponding to the underlying numerical and experimental data of the research article "Local response and emerging nonlinear elastic length scale in biopolymer matrices".  This repository contains four categories of data, each contained in a folder:  - Fiber Network Simulations  - Finite Elements Simulations  - Optical Tweezer Experiments - Traction Force Microscopy In each folder, a README.txt document provides a detailed description of the content. We would like to acknowledge the support from the NIH (1R01GM140108), MathWorks, and the Jeptha H. and Emily V. Wade Award at the Massachusetts Institute of Technology. H.Y. acknowledges the MathWorks Mechanical Engineering Fellowship. M.G. acknowledges the Sloan Research Fellowship. This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 891217 and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project-ID 201269156 - SFB 1032 (Project B12) (E.B. and C.P.B.). P.R. is supported by France 2030, the French National Research Agency (ANR-16-CONV-0001), and the Excellence Initiative of Aix- Marseille University—A*MIDEX.

SCALE-SPR19 MLD and Euphotic Depth

The file includes the mixed layer depth (MLD; m) calculated as: 1. Depth at which density changes by 0.03 kg m-3 from the value at 10m 2. Depth of the maximum Brunt-Vaissala Frequency   The file includes the euphotic depth (m) calculated as the depth at which iPAR is 1% of surface iPAR. Please note that not all stations have a value due to either being sampled at night or the depth of the CTD cast was deeper then depth rating of the sensor (1500 m). Included is the diffuse attenuation coefficient (m-1) calculated as the slope of the depth vs log(iPAR).

SCALE-WIN19 MLD and Euphotic Depth

The file includes the mixed layer depth (MLD; m) calculated as: 1. Depth at which density changes by 0.03 kg m-3 from the value at 10m 2. Depth of the maximum Brunt-Vaissala Frequency   The file includes the euphotic depth (m) calculated as the depth at which iPAR is 1% of surface iPAR. Please note that not all stations have a value due to either being sampled at night or the depth of the CTD cast was deeper then depth rating of the sensor (1500 m). Included is the diffuse attenuation coefficient (m-1) calculated as the slope of the depth vs log(iPAR).

opencc-zeroMay 2023View details →

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Non-random mating within an island rookery of Hawaiian hawksbill turtles: demographic discontinuity at a small coastline scale

Hawksbill sea turtles (Eretmochelys imbricata) from the Hawaiian archipelago form a small and genetically isolated population, consisting of only a few tens of individuals breeding annually. Most females nest on the island of Hawai'i, but little is known about the demographics of this rookery. This study used genetic relatedness, inferred from 135 microhaplotype markers, to determine breeding sex-ratios, estimate female nesting frequency, and assess relationships between individuals nesting on different beaches. Samples were collected during the 2017 nesting season and final data included 13 nesting females and 1,002 unhatched embryos, salvaged from 41 nests, of which 13 had no observed mother. Results show that most females used a single nesting beach laying 1–5 nests each. From female and offspring alleles the paternal genotypes of 12 breeding males were reconstructed and many showed high relatedness to their mates. Pairwise relatedness of offspring revealed one instance of polygyny but otherwise suggest a 1:1 breeding-sex ratio. Relatedness analysis and spatial-autocorrelation of genotypes indicate non-random mating among complexes of nesting beaches, for both sexes, suggesting strong natal philopatry. Nesting complexes also showed unique patterns of inbreeding across loci, further indicating that Hawaiian hawksbill turtles have demographically discontinuous nesting populations on a fine spatial scale.

Raw output data for "Chemical design of electronic and magnetic energy scales of tetravalent praseodymium materials", DOI: 10.1038/s41467-023-38431-7

This repository contains output data from the first-principles calculations reported in the following article: Authors: Arun Ramanathan, Jensen Kaplan, Dumitru-Claudiu Sergentu, Jacob A. Branson, Mykhaylo Ozerov, Alexander I. Kolesnikov, Stefan G. Minasian, Jochen Autschbach, John W. Freeland, Zhigang Jiang, Martin Mourigal, Henry S. La Pierre Title: Chemical Design of Electronic and Magnetic Energy Scales in Tetravalent Praseodymium Published in Nature Communications at: https://doi.org/10.1038/s41467-023-38431-7 Preprint on arXiv at: https://doi.org/10.48550/arXiv.2212.10401 Description: The archive contains raw output files generated from quantum chemistry calculations conducted using the OpenMolcas program. OpenMolcas is a freely available program that can be accessed at <a href="https://gitlab.com/Molcas/OpenMolcas">https://gitlab.com/Molcas/OpenMolcas</a>. For specific details about the content of the archive, please refer to the '00README' file included in the archive.

Supporting data for the publication "Emission ensemble approach to improve the development of multi-scale emission inventories"

This dataset includes the source IDL code as well as the three emission inventory aggregated emission datasets necessary to perform the analysis presented in the publication: "Emission ensemble approach to improve the development of multi-scale emission inventories (GMD)"