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Dataset results
6,410 results for “results”
DiahalineBaltic (GETM model results)
<p>This dataset was used for the manuscript "Spatial composition of the diahaline overturning circulation in a fjord-type, non-tidal estuarine system" by Henell et al. (2023, Journal of Geophysical Research Oceans, <a href="https://doi.org/10.1029/2023JC019862" target="_blank" rel="noopener">10.1029/2023JC019862</a>).</p> <p>The data are obtained from a 1 nautical mile simulation of the Baltic Sea with the coastal ocean model GETM, averaged over the years 2014 and 2015.</p>
Dataset with the results and matches of La Liga (2013/14 to 2022/23)
<p>The dataset contains information about soccer matches played in the Spanish professional league, La Liga, from the 2013/2014 season to the current season (2022/2023). The dataset includes 3800 rows, where each row represents a match and contains information such as season, matchday, date, time, home team, away team, result, and referee (referees are only available from the 2014/2015 season onwards). The dataset can be used to analyze and compare the performance of teams over the last 10 years, as well as to investigate the influence of referees and match schedules on match results.</p>
Data and results in "Relationship between crustal structure and plate convergence around the Izu collision zone in central Japan"
<p>“allrfstationlist.dat” contains the list of the used seismic stations. The four columns indicate the name, latitude, longitude, and altitude (m) of each station, respectively.</p> <p>“allrfevent.dat” contains the list of the used teleseismic events. From left to right, the 10 columns indicate the year, month (in number), day, hour, minute, and second of the origin time (Japan Standard Time), and the latitude (from –90 to 90), longitude (from –180 to 180), depth of the hypocenter, and magnitude of each event, respectively.</p> <p>“RFmoho.dat” contains the depth distribution of the Moho determined by our RF analysis. The third column indicates the depth (km) of the Moho at the given latitude (the second column) and longitude (the first column)</p> <p>“Tomo_depth_limited.txt” contains the depth distribution of the lower boundary of a layer with a P-wave velocity of 7.5–7.7 km/s in the model by Ishise et al. (2021), which was assumed as the Moho. The third column indicates its depth (km) at the given latitude (the first column) and longitude (the second column)</p> <p>“crustthickness_tomorf.dat” contains the thickness distribution of the crust of the Philippine Sea Plate determined from the geometry of its upper surface estimated by Hirose et al. (2008a, b) and Nakajima et al. (2009) and the Moho depth distribution shown in RFmoho.dat and Tomo_depth_limited.txt. The third column indicates the thickness (km) of the crust at the given latitude (the second column) and longitude (the first column). “RF” and “tomo” in the fourth column indicate the corresponding thickness determined based on the RF analysis and the model by Ishise et al. (2021), respectively.</p>
Dataset of backprojection and MT inversion results for 2023 Türkiye earthquake sequence
<p>This dataset contains the results of the back-projection analysis of the two Mw 7.7 and 7.6 earthquakes on February 6, 2023 in south-eastern Türkiye and the centroid moment tensor inversion results for one foreshock and 221 aftershocks. All methods and results are described in detail in the publication: Petersen et al., (2023): The 2023 SE Türkiye seismic sequence: Rupture of a complex fault network. The Seismic Record, 3 (2): 134-143. <a href="https://doi.org/10.1785/0320230008">https://doi.org/10.1785/0320230008</a></p>
Digital Elevation Models, orthoimages and lava outlines of the 2021 Fagradalsfjall eruption: Results from near real-time photogrammetric monitoring
<p>This repository contains the data behind the work described in Pedersen et al (in review), specifically the Digital Elevation Models (DEMs), orthoimages and lava outlines created as part of the near-real time monitoring of the Fagradalsfjall 2021 eruption (SW-Iceland).</p> <p>The processing of the data is explained in detail in the Supplement S2 of Pedersen et al (2022).</p> <p>The data derived from Pléiades surveys includes only the DEMs and the lava outlines. The Pléiades-based orthoimages are subject to license. Please contact the authors for further information about this.</p> <p><strong>Convention for file naming:</strong></p> <p>Data: DEM, Ortho, Outline</p> <p>YYYYMMDD_HHMM: Date of acquisition</p> <p>Platform used: Helicopter (HEL), Pléiades (PLE), Hasselblad A6D (A6D)</p> <p>Origin of elevations in DEMs: meters above ellipsoid (zmae)</p> <p>Ground Sampling Distance: 2x2m (DEM) and 30x30cm (Ortho)</p> <p>Cartographic projection: isn93 (see cartographic specifications for further details)</p> <p> </p> <p><strong>Cartographic specifications:</strong></p> <p>Cartographic projection: ISN93/Lambert 1993 (EPSG: 3057, https://epsg.io/3057)</p> <p>Horizontal and vertical reference frame: The surveys after 18 April 2021 are in ISN2016/ISH2004, updated locally around the study area in April 2021 (after pre-eruptive deformations occurred). The rest of the surveys of late March and early April were created using several floating reference systems (see Supplement S3 for details), since no ground surveys were available during the first weeks of the data collection. The surveys of 23 March 2021, 31 March 2021 were re-procesed in Gouhier et al., 2022, using the survey done on 18 May 2021 as reference.</p> <p>Origin of elevations: Ellipsoid WGS84</p> <p>Raster data format: GeoTIFF</p> <p>Raster compression system: ZSTD (http://facebook.github.io/zstd/)</p> <p>Vector data format: GeoPackage (https://www.geopackage.org/)</p>
Result dataset for our experimental analysis on multi-cepstral projection representation strategies for dysphonia detection
<p>Database containing the results of the analyzed versions of the framework proposed in our paper submitted to the journal Sensors (Basel) under the title "An experimental analysis on multi-cepstral projection representation strategies for dysphonia detection".</p> <p>In this database, we have the following information:</p> <p>"gender": Gender of individuals referring to the selected voice database. For this field, we have the following possible values: “male” for a selection of male individuals, “female” for a selection of female individuals, and “both” for a selection considering both genders.</p> <p>“Techniques”: Concerns about the techniques for extracting cepstral coefficients that we are analyzing. The identifier “nonceps” refers to the use of non-cepstral features.</p> <p>“vowel”: Vowel considered in the database selection. The following values are possible: “a”, “i” and “u”.</p> <p>“intonation”: Tone used by individuals when pronouncing the analyzed vowel. Possible values are: “h” for high; “l” for low; “n” is normal; and “lhl” for low-high-low.</p> <p>“coordinates”: Number of coordinates that make up the feature vector that represents the voice signal after the dimensionality reduction routines.</p> <p>“scale”: Normalization function used on the feature vector. The possible values of this field are the following: “MinMax” for the min-max scale; “Robust” for the robust scale; “Standard” for the standard scale; and “Unscaled” for the unscaled vector.</p> <p>“ACC”: Accuracy obtained by the analyzed version on the considered voice database clipping.</p> <p>“AUC”: Area under the ROC curve obtained by the analyzed version on the considered voice database clipping.</p> <p>“EER”: Equal Error Rate obtained by the analyzed version on the considered voice database clipping.</p> <p>“F1”: F1-score obtained by the analyzed version on the considered voice database clipping.</p> <p>“EH”: Rate of healthy voice signals classified as pathological on the considered voice database clipping.</p> <p>“EP”: Rate of pathological voice signals classified as healthy on the considered voice database clipping.</p> <p>“KFCV”: Average accuracy score of a 5-fold Cross Validation over the training dataset on the considered voice database clipping.</p> <p>“Balancing”: Indication of the use of balancing technique (SMOTE) by the considered framework version.</p> <p>“Classifier”: Classifier used, being possible the use of Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM).</p> <p>“Multi-Projection”: Multi-projection strategies employed by the evaluated technique.</p> <p>“Features”: Type of feature that defines the feature vector. In this case, the following values are possible in this field: “NonCeps” for non-cepstral features; “Ceps” for cepstral features only; and “Ceps and NonCeps” for features of cepstral and non-cepstral types.<br> .</p> <p>It is worth noting that the symbol “-”, present in some fields, represents the “non-use” of any technique of the type indicated by the field. For example, in the case of the “Balancing” field, the value “-” means that no data balancing technique was used in the evaluated version of the framework.</p>
Data and scripts to reproduce the results shown in "Stability of attractor local dimension estimates in non-Axiom A dynamical systems"
<p>Here we make available all the codes and datasets to reproduce the results of the paper "Stability of attractor local dimension estimates in non-Axiom A dynamical systems" by Flavio Pons, Gabriele Messori and Davide Faranda.</p> <p>The pre-print of the article is available at https://hal.science/hal-04051659/document.</p> <p>Any question/comment can be sent to flavio.pons@gmail.com.</p> <p>License for the codes and simulation/analysis results (*.Rda files): the code is shared under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license, see https://creativecommons.org/licenses/by-nc-sa/4.0/</p> <p>License and terms of use for the ERA5 data (z500_daily_euro.nc): the ERA5 500 hPa geopotential was downloaded from https://climexp.knmi.nl/start.cgi</p>
Reconstructing dust provenance from quartz optically stimulated luminescence (OSL) and electron spin resonance (ESR) signals: Preliminary results on loess from around the world
<p>Dataset for publication</p> <p><strong>Reconstructing dust provenance from quartz optically stimulated luminescence (OSL) and electron spin resonance (ESR) signals: </strong></p> <p><strong>Preliminary results on loess from around the world</strong></p> <p> </p> <p>Quantitative provenance analysis studies are instrumental in understanding the tectonic and climatic processes that shape the earth’s landscape. Although the most abundant mineral in the sedimentary system is quartz, almost all studies in provenance analysis investigate accessory minerals. Quartz crystals contain a vast number of point defects, intrinsic or due to impurities. For a signal to be an accurate indicator of provenance one needs to show that it is either dose independent or reaches a quantifiable steady state characteristic of the source rock. For signals used by trapped charge dating methods (optically stimulated luminescence (OSL) and electron spin resonance (ESR)), the latter option is the feasible one. By using quartz samples collected from the Chinese Loess Plateau (Luochuan loess-paleosol section), we show that the laboratory and natural dose response curves of E`<sub>1</sub> and and peroxy electron spin resonance signals of quartz (as defined later) overlap and reach a steady state for doses over about 1000 Gy. For E’<sub>1</sub> signals we attribute this steady state to reaching an equilibrium state between diamagnetic oxygen vacancies (the oxygen deficiency centre (ODC), Si=Si<em>)</em> and paramagnetic oxygen vacancies (E’<sub>1</sub>). For sedimentary quartz irradiated naturally or artificially in this dose range we show a strong linear relationship with zero intercept between E’<sub>1</sub> and peroxy signals for samples worldwide, supporting the hypothesis that these defects are Frenkel pairs. Further, we show significant correlations between the optically stimulated (OSL) sensitivity and the above two mentioned ESR signals. The very strong correlations (Pearson`s r ˃0.9) between E’<sub>1</sub>, peroxy and OSL sensitivity remain valid after the samples have been heated for 15 min to 350 ˚C for E’<sub>1</sub> to reach its maximum value, believed to be a result of the conversion of diamagnetic oxygen vacancies to E’<sub>1</sub>, clearly suggesting a relationship between OSL sensitivity and oxygen vacancies in general. Samples collected from different loess sites around the world can be distinguished based on both these OSL and ESR properties. An empirical increase in OSL sensitivity as well as oxygen related defect concentrations is observed in areas where the source material has components with older detrital zircon U-Pb ages, inferring a positive correlation between OSL sensitivity, as well as the signal intensity for E<sub>1</sub>` and peroxy defects and the age of the source rocks.</p>
Catalytic Rules and Validation Results for "EzMechanism: An Automated Tool to Propose Catalytic Mechanisms of Enzyme Reactions"
<p>Dataset containing the "Rules of Enzyme Catalysis" as created during the development of EzMechanism and the validation results of the software. For more information see https://www.biorxiv.org/content/10.1101/2022.09.05.506575v1, and the M-CSA website in https://www.ebi.ac.uk/thornton-srv/m-csa/</p>
District Heating Study Results (TIMES Ireland Model) 2023
<p>This is the set of District Heating Results used in a study. The results are available to view on a web-based platform also: https://epmg.netlify.app/Jason_DH/results/ </p> <p>The results are in CSV format. </p>
Datasets, reproducible codes, and results for evaluating differential expression analysis methods on population-level RNA-seq data
<p>This upload contains the necessary R codes and data to reproduce the FDR and Power results described in our correspondence "Neglecting normalization impact in semi-synthetic RNA-seq data simulation generates artificial false positives" to Li Y, Ge X, Peng F, Li W, Li JJ, Exaggerated false positives by popular differential expression methods when analyzing human population samples, <em>Genome Biology</em> 23, 79, 2022, DOI: 10.1186/s13059-022-02648-4.</p>
Supporting Information for "The First GECAM Observation Results on Terrestrial Gamma-ray Flashes and Terrestrial Electron Beams"
<p><strong>Additional Supporting Information</strong></p> <ol> <li>GECAM_TGF_Catalog.xls</li> <li>GECAM_TEB_Catalog.xls</li> <li>Fig1AC_UT2021-07-05T07-45-41.783530_CPD.xls</li> <li>Fig1AC_UT2021-07-05T07-45-41.783530_GRD.xls</li> <li>Fig1AC_UT2021-07-05T07-45-41.783530_Sim.xls</li> <li>Fig1BD_UT2021-04-26T12-16-34.637228_CPD.xls</li> <li>Fig1BD_UT2021-04-26T12-16-34.637228_GRD.xls</li> <li>Fig1BD_UT2021-04-26T12-16-34.637228_Sim.xls</li> <li>Fig4A_UT2021-02-01T02-09-25.691512_CPD.xls</li> <li>Fig4A_UT2021-02-01T02-09-25.691512_GRD.xls</li> <li>Fig4A_UT2021-02-01T02-09-25.691512_Sim.xls</li> <li>Fig4B_UT2021-07-10T21-19-04.519543_CPD.xls</li> <li>Fig4B_UT2021-07-10T21-19-04.519543_GRD.xls</li> <li>Fig4B_UT2021-07-10T21-19-04.519543_Sim.xls</li> <li>Fig4C_UT2022-01-22T22-24-49.664579_CPD.xls</li> <li>Fig4C_UT2022-01-22T22-24-49.664579_GRD.xls</li> <li>Fig4C_UT2022-01-22T22-24-49.664579_Sim.xls</li> <li>Fig4D_UT2021-03-07T19-13-49.995485_CPD.xls</li> <li>Fig4D_UT2021-03-07T19-13-49.995485_GRD.xls</li> <li>Fig4D_UT2021-03-07T19-13-49.995485_Sim.xls</li> <li>Fig4E_UT2021-03-29T06-56-37.831848_CPD.xls</li> <li>Fig4E_UT2021-03-29T06-56-37.831848_GRD.xls</li> <li>Fig4E_UT2021-03-29T06-56-37.831848_Sim.xls</li> <li>Fig4F_UT2021-08-14T09-54-29.177203_CPD.xls</li> <li>Fig4F_UT2021-08-14T09-54-29.177203_GRD.xls</li> <li>Fig4F_UT2021-08-14T09-54-29.177203_Sim.xls</li> <li>Fig4G_UT2021-08-16T17-02-27.908009_CPD.xls</li> <li>Fig4G_UT2021-08-16T17-02-27.908009_GRD.xls</li> <li>Fig4G_UT2021-08-16T17-02-27.908009_Sim.xls</li> <li>Fig4H_UT2022-03-29T08-56-28.599361_CPD.xls</li> <li>Fig4H_UT2022-03-29T08-56-28.599361_GRD.xls</li> <li>Fig4H_UT2022-03-29T08-56-28.599361_Sim.xls</li> <li>Fig5C_UT2021-09-11T18-34-40.551997_CPD.xls</li> <li>Fig5C_UT2021-09-11T18-34-40.551997_GRD.xls</li> <li>Fig5C_UT2021-09-11T18-34-40.551997_Sim.xls</li> <li>Fig5D_UT2021-07-10T01-46-36.709997_CPD.xls</li> <li>Fig5D_UT2021-07-10T01-46-36.709997_GRD.xls</li> <li>Fig5D_UT2021-07-10T01-46-36.709997_Sim.xls</li> <li>Fig5A_UT2021-10-27T22-49-33.082008_CPD.xls</li> <li>Fig5A_UT2021-10-27T22-49-33.082008_GRD.xls</li> <li>Fig5A_UT2021-10-27T22-49-33.082008_Sim.xls</li> <li>Fig5B_UT2022-07-26T00-16-13.728010_CPD.xls</li> <li>Fig5B_UT2022-07-26T00-16-13.728010_GRD.xls</li> <li>Fig5B_UT2022-07-26T00-16-13.728010_Sim.xls</li> <li>Fig5EF_WWLLN_Lightning.txt</li> <li>GLD360data_forTGFUTC2021-02-22T00-17-18.034461.xlsx</li> <li>GLD360data_forTGFUTC2021-03-07T19-13-49.995436.xlsx</li> <li>GLD360data_forTGFUTC2021-03-25T09-48-08.785508.xlsx</li> <li>GLD360data_forTGFUTC2021-03-29T06-56-37.830006.xlsx</li> <li>GLD360data_forTGFUTC2021-04-17T20-10-34.446509.xlsx</li> <li>GLD360data_forTGFUTC2021-04-25T23-07-27.616005.xlsx</li> <li>GLD360data_forTGFUTC2021-04-29T18-12-43.227007.xlsx</li> <li>GLD360data_forTGFUTC2021-05-09T19-50-01.720689.xlsx</li> <li>GLD360data_forTGFUTC2021-05-10T21-38-43.498955.xlsx</li> <li>GLD360data_forTGFUTC2021-05-10T21-43-27.914962.xlsx</li> <li>GLD360data_forTGFUTC2021-05-12T09-58-08.470159.xlsx</li> <li>GLD360data_forTGFUTC2021-05-15T08-38-22.505997.xlsx</li> <li>GLD360data_forTGFUTC2021-05-16T08-43-35.339273.xlsx</li> <li>GLD360data_forTGFUTC2021-06-20T15-37-51.777130.xlsx</li> <li>GLD360data_forTGFUTC2021-06-21T22-38-57.377719.xlsx</li> <li>GLD360data_forTGFUTC2021-07-22T23-38-31.513009.xlsx</li> <li>GLD360data_forTGFUTC2021-08-16T15-11-40.193070.xlsx</li> <li>GLD360data_forTGFUTC2021-09-24T13-55-59.153000.xlsx</li> <li>GLD360data_forTGFUTC2021-10-05T10-16-04.302001.xlsx</li> <li>GLD360data_forTGFUTC2021-11-09T03-10-44.188748.xlsx</li> <li>GLD360data_forTGFUTC2021-12-04T01-37-23.893950.xlsx</li> <li>GLD360data_forTGFUTC2021-12-06T12-15-46.564243.xlsx</li> <li>GLD360data_forTGFUTC2021-12-12T21-41-33.038999.xlsx</li> <li>GLD360data_forTGFUTC2021-12-13T23-34-18.149995.xlsx</li> <li>GLD360data_forTGFUTC2021-12-22T19-36-38.765547.xlsx</li> <li>GLD360data_forTGFUTC2021-12-28T03-16-31.018224.xlsx</li> <li>GLD360data_forTGFUTC2022-02-16T15-26-20.379956.xlsx</li> <li>GLD360data_forTGFUTC2022-03-09T04-37-21.765997.xlsx</li> <li>GLD360data_forTGFUTC2022-03-11T04-56-30.604005.xlsx</li> <li>GLD360data_forTGFUTC2022-03-17T23-01-55.158520.xlsx</li> <li>GLD360data_forTGFUTC2022-03-26T20-48-39.098469.xlsx</li> <li>GLD360data_forTGFUTC2022-03-27T19-13-33.058448.xlsx</li> <li>GLD360data_forTGFUTC2022-03-30T19-35-55.714452.xlsx</li> <li>GLD360data_forTGFUTC2022-04-20T20-47-17.811510.xlsx</li> <li>GLD360data_forTGFUTC2022-05-03T03-38-25.725991.xlsx</li> <li>GLD360data_forTGFUTC2022-05-13T20-32-32.157110.xlsx</li> <li>GLD360data_forTGFUTC2022-05-13T20-36-38.126223.xlsx</li> <li>GLD360data_forTGFUTC2022-06-15T18-06-04.110702.xlsx</li> <li>GLD360data_forTGFUTC2022-06-24T10-42-13.680445.xlsx</li> <li>GLD360data_forTGFUTC2022-06-25T09-09-44.289205.xlsx</li> <li>GLD360data_forTGFUTC2022-07-20T20-59-28.784931.xlsx</li> </ol> <p> </p> <p><strong>Data </strong><strong>D</strong><strong>escription</strong></p> <p>We have uploaded 86 data files. These are:</p> <ol> <li>The list of 147 TGFs observed by GECAM from December 10, 2020 until August 31, 2022. The file includes information about a) the UTC time of observation, b) the longitude, latitude and altitude of the GECAM position, c) the duration calculated by the Bayesian Block algorithm, d) the number of net counts, e) the hardness ratio (energy limitation 200 keV), f) the CPD/GRD counts ratio. These data were used to produce Figure 1 and Figure 2.</li> <li>The list of 2 typical TEBs and 2 TEB-like events observed by GECAM from December 10, 2020 until August 31, 2022. The file includes information about a) the UTC time of observation, b) the longitude, latitude and altitude of the GECAM position, c) the duration calculated by the Bayesian Block algorithm, d) the CPD/GRD counts ratio, e) the longitude and latitude of the northern and sourthern magnetic footpoint. These data were used to produce Figure 1 and Figure 2.</li> <li>The CPD data of a cosmic-ray event. The CPD data include: a) the relative time to reference time (UT 2021-07-05T07:45:41.783530), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure S1A&S1C.</li> <li>The GRD data of a cosmic-ray event. The GRD data include: a) the relative time to reference time (UT 2021-07-05T07:45:41.783530), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure S1A&S1C.</li> <li>The SimEvt data of a cosmic-ray event. The CPD data include: a) the relative time to reference time (UT 2021-07-05T07:45:41.783530), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure S1A&S1C.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-04-26T12:16:34.637228), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure S1B&S1D.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2021-04-26T12:16:34.637228), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure S1B&S1D.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-04-26T12:16:34.637228), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure S1B&S1D.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-02-01T02:09:25.691512), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 3A.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2021-02-01T02:09:25.691512), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 3A.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-02-01T02:09:25.691512), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 3A.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-07-10T21:19:04.519543), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 3B.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2021-07-10T21:19:04.519543), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 3B.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-07-10T21:19:04.519543), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 3B.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2022-01-22T22:24:49.664579), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 3C.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2022-01-22T22:24:49.664579), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 3C.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2022-01-22T22:24:49.664579), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 3C.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-03-07T19:13:49.995485), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 3D.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2021-03-07T19:13:49.995485), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 3D.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-03-07T19:13:49.995485), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 3D.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-03-29T06:56:37.831848), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 3E.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2021-03-29T06:56:37.831848), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 3E.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-03-29T06:56:37.831848), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 3E.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-08-14T09:54:29.177203), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 3F.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2021-08-14T09:54:29.177203), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 3F.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-08-14T09:54:29.177203), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 3F.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-08-16T17:02:27.908009), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 3G.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2021-08-16T17:02:27.908009), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 3G.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-08-16T17:02:27.908009), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 3G.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2022-03-29T08:56:28.599361), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 3H.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2022-03-29T08:56:28.599361), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 3H.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2022-03-29T08:56:28.599361), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 3H.</li> <li>The CPD data of a TEB-like event. The CPD data include: a) the relative time to reference time (UT 2021-09-11T18:34:40.551997), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 4C.</li> <li>The GRD data of a TEB-like event. The GRD data include: a) the relative time to reference time (UT 2021-09-11T18:34:40.551997), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 4C.</li> <li>The SimEvt data of a TEB-like event. The CPD data include: a) the relative time to reference time (UT 2021-09-11T18:34:40.551997), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 4C.</li> <li>The CPD data of a TEB-like event. The CPD data include: a) the relative time to reference time (UT 2021-07-10T01:46:36.709997), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 4D.</li> <li>The GRD data of a TEB-like event. The GRD data include: a) the relative time to reference time (UT 2021-07-10T01:46:36.709997), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 4D.</li> <li>The SimEvt data of a TEB-like event. The CPD data include: a) the relative time to reference time (UT 2021-07-10T01:46:36.709997), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 4D.</li> <li>The CPD data of a typical TEB event. The CPD data include: a) the relative time to reference time (UT 2021-10-27T22:49:33.082008), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 4A.</li> <li>The GRD data of a typical TEB event. The GRD data include: a) the relative time to reference time (UT 2021-10-27T22:49:33.082008), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 4A.</li> <li>The SimEvt data of a typical TEB event. The CPD data include: a) the relative time to reference time (UT 2021-10-27T22:49:33.082008), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 4A.</li> <li>The CPD data of a typical TEB event. The CPD data include: a) the relative time to reference time (UT 2022-07-26T00:16:13.728010), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 4B.</li> <li>The GRD data of a typical TEB event. The GRD data include: a) the relative time to reference time (UT 2022-07-26T00:16:13.728010), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 4B.</li> <li>The SimEvt data of a typical TEB event. The CPD data include: a) the relative time to reference time (UT 2022-07-26T00:16:13.728010), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 4B.</li> <li>The specific WWLLN data of the TEB-like event UT 2021-09-11T18:34:40.551997. The WWLLN data include: a) WWLLN Lighning UT Time, b) WWLLN Lighning UNIX Time, c) WWLLN Lighning Longitude (deg) , d) WWLLN Lighning Latitude (deg) , e) WWLLN Lighning Energy (J) , f) WWLLN Lighning Energy Error (J). Data are used in Figure 4E&4F.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-02-22T00:17:18.034461 +/- 1 minute. The GECAM-B nadir (129.7E, 10.9N) of this TGF is located in the east Asia region (EAR, 77E-138E, 13S-30N). The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg) , c) Lighning Peak Current (kA) , d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-03-07T19:13:49.995436 +/- 1 minute. The GECAM-B nadir (92.2E, 4.7N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-03-25T09:48:08.785508 +/- 1 minute. The GECAM-B nadir (101.4E, 3.5N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-03-29T06:56:37.830006 +/- 1 minute. The GECAM-B nadir (105.0E, 2.4S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-04-17T20:10:34.446509 +/- 1 minute. The GECAM-B nadir (131.0E, 2.4N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>51) The specific GLD360 data near GECAM TGF UT 2021-04-25T23:07:27.616005 +/- 1 minute. The GECAM-B nadir (117.1E, 29.0N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>52) The specific GLD360 data near GECAM TGF UT 2021-04-29T18:12:43.227007 +/- 1 minute. The GECAM-B nadir (77.9E, 5.3N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-05-09T19:50:01.720689 +/- 1 minute. The GECAM-B nadir (119.4E, 15.5N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-05-10T21:38:43.498955 +/- 1 minute. The GECAM-B nadir (105.2E, 5.1N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-05-10T21:43:27.914962 +/- 1 minute. The GECAM-B nadir (119.5E, 3.5S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-05-12T09:58:08.470159 +/- 1 minute. The GECAM-B nadir (122.8E, 12.9N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-05-15T08:38:22.505997 +/- 1 minute. The GECAM-B nadir (115.3E, 10.1N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-05-16T08:43:35.339273 +/- 1 minute. The GECAM-B nadir (103.6E, 8.5N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-06-20T15:37:51.777130 +/- 1 minute. The GECAM-B nadir (128.0E, 21.1N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-06-21T22:38:57.377719 +/- 1 minute. The GECAM-B nadir (124.4E, 13.3N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-07-22T23:38:31.513009 +/- 1 minute. The GECAM-B nadir (117.2E, 16.3N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-08-16T15:11:40.193070 +/- 1 minute. The GECAM-B nadir (126.9E, 28.8N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-09-24T13:55:59.153000 +/- 1 minute. The GECAM-B nadir (131.1E, 5.2N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-10-05T10:16:04.302001 +/- 1 minute. The GECAM-B nadir (115.3E, 10.1N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-11-09T03:10:44.188748 +/- 1 minute. The GECAM-B nadir (114.8E, 6.6N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-12-04T01:37:23.893950 +/- 1 minute. The GECAM-B nadir (126.7E, 10.5S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-12-06T12:15:46.564243 +/- 1 minute. The GECAM-B nadir (128.9E, 10.6N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-12-12T21:41:33.038999 +/- 1 minute. The GECAM-B nadir (119.0E, 11.2S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-12-13T23:34:18.149995 +/- 1 minute. The GECAM-B nadir (117.0E, 7.2N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-12-22T19:36:38.765547 +/- 1 minute. The GECAM-B nadir (104.2E, 3.4N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-12-28T03:16:31.018224 +/- 1 minute. The GECAM-B nadir (117.7E, 3.4N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-02-16T15:26:20.379956 +/- 1 minute. The GECAM-B nadir (102.0E, 3.6S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-03-09T04:37:21.765997 +/- 1 minute. The GECAM-B nadir (109.5E, 5.3S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-03-11T04:56:30.604005 +/- 1 minute. The GECAM-B nadir (114.3E, 8.1N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-03-17T23:01:55.158520 +/- 1 minute. The GECAM-B nadir (120.4E, 9.7S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-03-26T20:48:39.098469 +/- 1 minute. The GECAM-B nadir (111.5E, 3.1N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-03-27T19:13:33.058448 +/- 1 minute. The GECAM-B nadir (113.7E, 5.0S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-03-30T19:35:55.714452 +/- 1 minute. The GECAM-B nadir (102.1E, 4.0N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-04-20T20:47:17.811510 +/- 1 minute. The GECAM-B nadir (104.9E, 1.8S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-05-03T03:38:25.725991 +/- 1 minute. The GECAM-B nadir (109.0E, 11.1N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>81) The specific GLD360 data near GECAM TGF UT 2022-05-13T20:32:32.157110 +/- 1 minute. The GECAM-B nadir (115.8E, 0.1N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-05-13T20:36:38.126223 +/- 1 minute. The GECAM-B nadir (128.2E, 7.4N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-06-15T18:06:04.110702 +/- 1 minute. The GECAM-B nadir (109.7E, 10.9S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-06-24T10:42:13.680445 +/- 1 minute. The GECAM-B nadir (116.8E, 10.4N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-06-25T09:09:44.289205 +/- 1 minute. The GECAM-B nadir (127.3E, 13.2N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-07-20T20:59:28.784931 +/- 1 minute. The GECAM-B nadir (97.4E, 22.8N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> </ol>
Project "Public services management system to improve the quality and accessibility of services" (01.2.2-LMT-K-718-03-0019) literature review screening results
<p>The results of the keyword query in Scopus search with the abstracts were screened using <i>abstractr </i>platform at <a href="http://abstrackr.cebm.brown.edu">http://abstrackr.cebm.brown.edu</a>. Four reviewers reviewed intersecting subsets of the overall list of publications in separate reviews, therefore duplicate records in the file are possible. The results from four reviews were combined into one file using functionality of <i>abstractr </i>platform. The reviews were finalized in February, 2022. Majority of the publications from the Scopus query results were automatically assigned low relevance scores thanks to the active learning algorithm used by <i>abstractr</i> and therefore were not reviewed manually.</p><p>Notes on the columns of the dataset:</p><ul><li>(internal) id - internal id added by <i>abstractr.</i></li><li>(source) id - Scopus document id followed by underscore and '1' (if publication has DOI) or 'n' (if publication has no DOI).</li><li>keywords - authors' keywords and Scopus keywords concatenated from the Scopus query results.</li><li>abstract - an abstract of a publication from the Scopus query results.</li><li>title - title of a publication from the Scopus query results.</li><li>journal - journal of a publication from the Scopus query results.</li><li>authors - authors of a publlication from the Scopus query results.</li><li>consensus - for publications reviewed by multiple reviewers the consensus decision is signified by '1', no consensus - by 'x' and unable to asses consensus by 'o'. These codes are generated by <i>abstrackr.</i></li><li>eg - the first reviewer id. Code '1' means the publication was selected based on title and abstract, '0' - unsure, '-1' rejected.</li><li>dj - the second reviewer id. Code '1' means the publication was selected based on title and abstract, '0' - unsure, '-1' rejected.</li><li>rp - the third reviewer id. Code '1' means the publication was selected based on title and abstract, '0' - unsure, '-1' rejected.</li><li>mp - the fourth reviewer id. Code '1' means the publication was selected based on title and abstract, '0' - unsure, '-1' rejected.</li><li>count (+1) - integer, the number of reviewers selecting the publication for fulltext reading. Calculated from 'eg ','dj ','rp' and 'mp' columns.</li><li>count (0) - integer, the number of reviewers not sure of selecting the publication for fulltext reading. Calculated from 'eg ','dj ','rp' and 'mp' columns.</li><li>count (-1) - integer, the number of reviewers not selecting the publication for fulltext reading. Calculated from 'eg ','dj ','rp' and 'mp' columns.</li><li>at leat once selected - binary integer, representing the final decision rule to select publications for fulltext reading and further analysis.</li></ul><p>The data were collected as a part of the project "Public services management system to improve the quality and accessibility of services" ("Viešųjų paslaugų vadybos sistema paslaugų kokybei ir prieinamumui gerinti"), grant no. 01.2.2-LMT-K-718-03-0019, funded by the Lithuanian research council.</p>
Simulation result from "Simulating Bark Beetle Outbreak Dynamics and their Influence on Carbon Balance Estimates with ORCHIDEE r7791"
<p>Eight locations were selected which represent the range of climatic conditions within the distribution area of spruce in Europe (<em>Picea Abies</em> Karst L.) as shown in Table 4. Half-hourly weather data from the FLUXNET database <a href="https://www.zotero.org/google-docs/?ibBw15">(Pastorello et al., 2020)</a> for these locations were used to drive ORCHIDEE. Some of these locations (FON, SOR, HES, COL, WET) are not populated with spruce but all are located within the species distribution. For each location, a pure spruce stand was simulated and the available FLUXNET data was looped to simulate a 100-year period. The study did not investigate the effect of species mixture in the simulation experiments. Other inputs, including soil texture, pH and soil color were obtained from the USDA map derived from <a href="https://www.zotero.org/google-docs/?aaWPI6">Eswaran et al. (2003</a>), for the corresponding pixel.</p> <p>The amount of fresh breeding woody substrate inputs used by the bark beetles to breed was controlled by modifying the maximum wind speed of a windthrow event in ORCHIDEE. Seven wind speeds ranging between 19 m/s and 40 m/s were selected (Table 3). This range is justified by the observation that mean wind speeds below 19 m/s could not trigger a windthrow event in ORCHIDEE <a href="https://www.zotero.org/google-docs/?jEqNDm">(Chen et al., 2018)</a> while for wind speeds exceeding 40 m/s, more than 60% of the trees are uprooted, leaving too few living trees to trigger a bark beetle outbreak within the same pixel. </p> <p>To investigate the impact of windthrow intensity and background climate on bark beetle outbreaks, the study conducted a total of 56 [8 sites x 7 wind speed intensities] simulations as given in table 3. The same 56 simulations were also used to analyze the sensitivity of the carbon balance of spruce forests to windthrow intensity and background climate.</p> <p>Where most land surface models use a turnover time to simulate continuous mortality <a href="https://www.zotero.org/google-docs/?5jGfXF">(Thurner et al., 2014; Pugh et al., 2019)</a>, ecological reality is better described by abrupt mortality events. An idealized simulation experiment was used to qualify the impact of abrupt mortality on net biome productivity by changing from a framework in which mortality is approximated by a constant background mortality to a framework in which mortality occurs in abrupt, discrete events. To test the impact of a change in mortality framework two versions of ORCHIDEE were compared to create an idealized simulation experiment: (1) a version simulating mortality as a continuous process, labeled ”the continuous version”, and (2) the version capable of simulating abrupt mortality from windthrow and subsequent bark beetle outbreaks, labeled ”the abrupt version”. The effect of simulating abrupt mortality was evaluated over 20-, 50-, and 100-year time horizons.</p> <p>The effect of changing the framework of simulating mortality from continuous to abrupt was qualified on the basis of 112 simulations (8 sites x 7 wind speeds x 2 model versions) of 100 years each. The simulations with abrupt mortality were run first. Subsequently, the number of trees killed was quantified and used as a reference value for the continuous mortality set-up. This approach resulted in the same quantities of dead trees at the end of the simulation for both frameworks, which then differed only in the timing of the simulated mortality. This precaution is necessary to avoid comparing two different mortality regimes where the result would mainly be explained by the intensity of the mortality rather than by its underlying mechanisms. </p>
List of articles resulting from the Google Scholar search "graph based author name disambiguation" published after 1/1/2021
<p>This dataset contains the list of articles resulting from the Google Scholar search “graph based author name disambiguation” published after 1/1/2021. The list is provided for reproducibility of the survey article “Graph-based Methods for Author Name Disambiguation: A Survey” and it was obtained using the following Python script available at <a href="https://github.com/WittmannF/sort-google-scholar">https://github.com/WittmannF/sort-google-scholar</a>:</p> <blockquote> <p>$ python sortgs.py --kw “graph based author name disambiguation” --startyear 2021</p> </blockquote> <p>The command returned the CSV file that contains the first 94 publications matching the query (articles with corrupted metadata have been excluded), each with metadata about Title, Number of Citations, and Rank. The CSV contains a column that specified which articles have been eventually selected for the survey.</p>
Results of the numerical experiments of the HyPaD algorithm
<p>This dataset is supplementary material for [1]. It provides the data obtained for the numerical experiments of the Hybrid Patch Decomposition (HyPaD) algorithm [2] that are presented in that paper. The following data is contained in the different zip-archives:</p> <ul> <li><strong>SNIA - full enumeration:</strong> A collection of .mat files with the output data for each of the 35 test instances using the full enumeration approach to realize the SNIA procedure.</li> <li><strong>SNIA - dynamic boxes:</strong> A collection of .mat files with the output data for each of the 35 test instances using the dynamic boxes approach to realize the SNIA procedure.</li> <li><strong>SNIA - fixed boxes [4]:</strong> A collection of .mat files with the output data for each of the 35 test instances using the fixed boxes approach with b=4 branching steps (i.e., 16 boxes in total) to realize the SNIA procedure.</li> <li><strong>MOMIX:</strong> A collection of folders that contain the .mat files with the results for the MOMIX (and MOMIX light) algorithm [3] which have been used as a reference point in the publication.</li> <li><strong>Figures:</strong> The .fig files used in [1].</li> </ul> <p> </p> <p><strong>References:</strong></p> <ol> <li>Gabriele Eichfelder and Leo Warnow, <strong>On implementation details and numerical experiments for the HyPaD algorithm to solve multi-objective mixed-integer convex optimization problems</strong>, <a href="http://www.optimization-online.org/DB_HTML/2021/08/8538.html">Optimization Online</a>, 2021.</li> <li>Gabriele Eichfelder and Leo Warnow, <strong>A hybrid patch decomposition approach to compute an enclosure for multi-objective mixed-integer convex optimization problems</strong>, <a href="https://link.springer.com/article/10.1007/s00186-023-00828-x">Mathematical Methods of Operations Research</a>, 2021.</li> <li>Marianna De Santis, Gabriele Eichfelder, Julia Niebling and Stefan Rocktäschel, <strong>Solving Multiobjective Mixed Integer Convex Optimization Problems</strong>, <a href="https://epubs.siam.org/doi/10.1137/19M1264709">SIAM Journal on Optimization</a>, 2020.</li> </ol>
TRINITY DC_3.5.1 final demonstration results
<p>This data set represents final demonstration results for the Demo Case 3.5.1 "Regional Adequacy Assessment (RAA) in the SEE region" of the TRINITY project. It contains following files:</p> <p>- PG.csv contains values of unit cost for engagement of particular generation types for different areas that are part of optimization process;</p> <p>- PT.csv contains values of unit cost for transaction on borders;</p> <p>- RAA_algorithm_results-Test1-CB_settings.xlsx contains results of RAA algorithm using CB settings;</p> <p>- RAA_algorithm_results-Test2-TY_settings.xlsx contains results of RAA algorithm using TY settings.</p> <p>Last two files contain information about values: ERIC [MW], TIC [MW], TIF [MW], Loop flow [%], Cost [€], Cost/TIC [€/MW], Optimal adequacy transaction [MW] and Net adequacy exchange [MW] for 9 areas (AL, BA, BG, GR, ME, MK, HR, RO and RS) and 331 timestamps (of Week 9 and Week 10 of 2021). </p> <p>All information about RAA algorithm and terms used in this data set could be found in document <em>D4.4 T-SENTINEL TOOLSET v2</em>, while more conclusions about DC 3.5.1 final demonstration could be found in document <em>D7.4 TRINITY demonstration activities</em><em>.</em></p>
Paired differential gene expression and splicing analyses results of 199 baseline vs. case comparisons across 100 datasets
<p>This dataset contains results from paired differential expression and differential splicing analyses as well as gene-set over-representation analysis results for 199 baseline vs. case comparisons across 100 randomly curated datasets with accompanying metadata (<a href="https://doi.org/10.1186/s12915-023-01724-w" target="_blank" rel="noopener">article</a>).<br>All results were computed using the R package <a href="https://github.com/shdam/pairedGSEA">pairedGSEA</a>, which utilized DESeq2 (Love et al., 2014), DEXSeq (Anders et al., 2012), and fgsea (Korotkevich et al., 2019).<br>See limma results here: <a href="https://doi.org/10.5281/zenodo.8162214">https://doi.org/10.5281/zenodo.8162214</a><br><br>Each .RDS file contains a list with four objects: A 'metadata' object with the metadata of the respective raw data, a 'genes' object with gene-level differential splicing and expression results, a 'gene_set' object with over-representation results, and 'experiment' with the experiment title.<br><br>The filenames follow this pattern: "[dataset ID]_[GEO accession number]_[Manually assigned comparison title].RDS".<br><br>All datasets were obtained from a local copy of the ARCHS4 v11 database of transcript counts (Lachmann et al., 2018).</p>
SI2: How circular is an extractive economy? South Africa's export orientation results in low circularity and insufficient societal stocks for service-provisioning
<p>Supporting information SI2 for the manuscript under review:</p> <p>How circular is an extractive economy? South Africa’s export orientation results in low circularity and insufficient societal stocks for service-provisioning </p> <p> </p> <p>It provides the data used and the basic mass balanced calculation for a circularity assessment.</p>
Result data related to "Tröndle et al (2023): Public preferences for phasing-out fossil fuels in the German building and transport sectors"
<p>Parameter estimations from the conjoint experiments performed in "Tröndle et al (2023): Public preferences for phasing-out fossil fuels in the German building and transport sectors". Parameter estimations are given for different:</p> <p>* estimands: average marginal component effects (amce) or marginal means,</p> <p>* variables: choice and rating,</p> <p>* sectors: buildings (heat) and transport sector,</p> <p>* subgroups: by-<subgroupname>.</p> <p>Filenames accordingly are: <estimand>-<variable>-<sector>.csv or <estimand>-<variable>-<sector>-by-<subgroup>.csv</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.