cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23245622
title: Figure 1 and 2 fire susceptibility raw data

description: Raw data for the neighbourhood analysis on fire-susceptibility. Each row represents a pixel extracted in the vicinity of a known ignition source.
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Columns include - 
veg_start_yr/veg_end_yr: the start and end years of the relevant vegetation map before the fire
fire_start_yr/fire_end_yr: the start and end years of hte relevant vegetation map during which the fire occurred
TPI: Topographical postion index
TWI: SAGA wetness index (referred to as SWI in the paper)
slope: slope of the pixel
aspect: aspect in radians
cos_aspect: linearized aspect (see explanation in paper)
forest_distance: distance to nearest forest patch (not used)
grassland_distance: distance to nearest grassland patch (not used)
shrubland_distance: distance to nearest shrubland patch (not used)
vegetation_class: vegetation type before the fire
fire: did it burn? yes = 1; no = 0
centroid_distance: distance to the centroid of the burn area (i.e. exposure to ignition source)
area: area of burn area (not used)
ebal_weights: EBAL weights assigned to balance covariates
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<br>
DOI: 10.6084/m9.figshare.23245622.v1
title: Figure 1 and 2 fire susceptibility raw data

description: Raw data for the neighbourhood analysis on fire-susceptibility. Each row represents a pixel extracted in the vicinity of a known ignition source.
<br>
Columns include - 
veg_start_yr/veg_end_yr: the start and end years of the relevant vegetation map before the fire
fire_start_yr/fire_end_yr: the start and end years of hte relevant vegetation map during which the fire occurred
TPI: Topographical postion index
TWI: SAGA wetness index (referred to as SWI in the paper)
slope: slope of the pixel
aspect: aspect in radians
cos_aspect: linearized aspect (see explanation in paper)
forest_distance: distance to nearest forest patch (not used)
grassland_distance: distance to nearest grassland patch (not used)
shrubland_distance: distance to nearest shrubland patch (not used)
vegetation_class: vegetation type before the fire
fire: did it burn? yes = 1; no = 0
centroid_distance: distance to the centroid of the burn area (i.e. exposure to ignition source)
area: area of burn area (not used)
ebal_weights: EBAL weights assigned to balance covariates
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<br>
<br>
DOI: 10.5281/zenodo.10401361
title: Results from DSC analysis over glass fibre reinforced SMC (50007150 grade by Menzolit supplier)

description: Results from DSC analysis over glass fibre reinforced SMC for automotive applications (50007150 grade provided by Menzolit supplier)
DOI: 10.5281/zenodo.10401362
title: Results from DSC analysis over glass fibre reinforced SMC (50007150 grade by Menzolit supplier)

description: Results from DSC analysis over glass fibre reinforced SMC for automotive applications (50007150 grade provided by Menzolit supplier)
DOI: 10.5281/zenodo.10401845
title: Datasets with typos for testing LEA (https://arxiv.org/abs/2307.02912)

description: Datasets for testing the generalization capacity of LEA in the presence of typos. Link to the paper: https://arxiv.org/abs/2307.02912

Below is a list of raw public datasets and different versions of test splits to which automatically synthetically generated typos have been added by deleting and replacing characters.



Abt-Buy

Amazon-Google

WDC-Computers (small, medium, large and xlarge)

WDC-All (xlarge)

RTE

MRPC


 

Reference:

Almagro, M., Almazán, E., Ortego, D., & Jiménez, D. (2023, August). LEA: Improving Sentence Similarity Robustness to Typos Using Lexical Attention Bias. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 36-46).
DOI: 10.5281/zenodo.10401846
title: Datasets with typos for testing LEA (https://arxiv.org/abs/2307.02912)

description: Datasets for testing the generalization capacity of LEA in the presence of typos. Link to the paper: https://arxiv.org/abs/2307.02912

Below is a list of raw public datasets and different versions of test splits to which automatically synthetically generated typos have been added by deleting and replacing characters.



Abt-Buy

Amazon-Google

WDC-Computers (small, medium, large and xlarge)

WDC-All (xlarge)

RTE

MRPC


 

Reference:

Almagro, M., Almazán, E., Ortego, D., & Jiménez, D. (2023, August). LEA: Improving Sentence Similarity Robustness to Typos Using Lexical Attention Bias. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 36-46).
DOI: 10.25773/3t1n-ss33
title: Dataset: The development of strategies for coastal wetland conservation prioritization in Virginia under climate change
DOI: 10.6084/m9.figshare.22006976
title: Visualization of electron density changes along chemical reaction pathways

description: We propose a simple procedure for visualising the electron density changes (EDC) during a chemical reaction, which is based on a mapping of rectangular grid points for a stationary structure into (distorted) positions around atoms of another stationary structure. Specifically, during a small step along the minimum energy pathway (MEP), the displacement of each grid point is obtained as a linear combination of the motion of all atoms, with the contribution from each atom scaled by the corresponding Hirshfeld weight. For several reactions (identity SN2, Claisen rearrangement, Diels-Alder reaction, [3+2] cycloaddition, and phenylethyl mercaptan attack on pericosine A), our EDC plots showed an expected reduction of electron densities around severed bonds (or those with the bond-order lowered), with the opposite observed for newly-formed or enhanced chemical bonds. The EDC plots were also shown for copper triflate catalyzed N2O fragmentation, where the N–O bond weakening initially occurred on a singlet surface, but continued on a triplet surface after reaching the minimum-energy crossing point (MECP) between the two potential energy surfaces.
DOI: 10.6084/m9.figshare.22006976.v1
title: Visualization of electron density changes along chemical reaction pathways

description: We propose a simple procedure for visualising the electron density changes (EDC) during a chemical reaction, which is based on a mapping of rectangular grid points for a stationary structure into (distorted) positions around atoms of another stationary structure. Specifically, during a small step along the minimum energy pathway (MEP), the displacement of each grid point is obtained as a linear combination of the motion of all atoms, with the contribution from each atom scaled by the corresponding Hirshfeld weight. For several reactions (identity SN2, Claisen rearrangement, Diels-Alder reaction, [3+2] cycloaddition, and phenylethyl mercaptan attack on pericosine A), our EDC plots showed an expected reduction of electron densities around severed bonds (or those with the bond-order lowered), with the opposite observed for newly-formed or enhanced chemical bonds. The EDC plots were also shown for copper triflate catalyzed N2O fragmentation, where the N–O bond weakening initially occurred on a singlet surface, but continued on a triplet surface after reaching the minimum-energy crossing point (MECP) between the two potential energy surfaces.
DOI: 10.6084/m9.figshare.24847809.v1
title: ITPA SNP submission data.xlsx

description: After obtaining approval from the ethical committee reference number(IRB-UOLFAHS/565) the HCV patients receiving direct-acting antiviral treatment were invited to participate after taking consent. Blood was drawn in aseptic conditions. DNA extraction was performed ITPA gene was amplified by the following conditions: PCR master mix (Thermo scientific by Thermo Fisher) was used with 5-10 ng/µl DNA. The thermal cycle conditions were: Denaturation at 94°C for 10 min then 35 cycles at 95 °C for 1 min, annealing at 55 °C for 30 sec, extension at 72 °C for 30 sec, and final extension at 72 °C for 10 min. PCR products were visualized under ultraviolet light after running gel electrophoresis. PCR product band size was 213bp. Following PCR, ITPA genotypes were investigated using Restriction fragment length polymorphism (RFLP). For this purpose, PCR products were treated overnight with restriction enzyme Xcel to investigate the presence of rs1127354 polymorphisms. In the case of polymorphism rs1127354, 213bp PCR product was digested into fragments 213bp, 135bp, and 78bp for (CC) homozygous dominant allele, (CA) heterozygous allele, and (AA) for homozygous recessive allele respectively. SNPs conformed to the Hardy-Weinberg equilibrium. For further confirmation of RFLP results, Sanger sequencing was conducted.
DOI: 10.57967/hf/1492
title: 4catac
DOI: 10.6084/m9.figshare.24599958
title: 数据.xlsx

description: This retrospective observational study included 269 CRRT sessions from 129 NS patients treated at Nanfang Hospital, Southern Medical University, from January 2019 to December 2022, it aim to investigate the efficacy and safety of LMWH combined with citrate anticoagulation in patients with NS requiring CRRT therapy, in order to find a better coagulation therapy with lower incidence of venous thrombosis and better filter lifespan.
DOI: 10.6084/m9.figshare.24599958.v1
title: 数据.xlsx

description: This retrospective observational study included 269 CRRT sessions from 129 NS patients treated at Nanfang Hospital, Southern Medical University, from January 2019 to December 2022, it aim to investigate the efficacy and safety of LMWH combined with citrate anticoagulation in patients with NS requiring CRRT therapy, in order to find a better coagulation therapy with lower incidence of venous thrombosis and better filter lifespan.
DOI: 10.5281/zenodo.10401247
title: MCIT storm tracks with updraft radar data for large hail cases in the United States (2013 - 2023)

description: Storm tracking data from 114 hail days in the United States between 2013 and 2023. Cases were selected based on the availability of abundant Storm Prediction Center (SPC) hail reports (either at least 20 reports of hail > 1 cm or five reports > 10 cm) in a range between 20 and 90 km from a NEXRAD radar site. Only cases in a geographic area from 28-48°N and 105-90°E, roughly corresponding to the U.S. Great Plains, were considered. Cells are quality filtered based on these criteria: (1) Cell center within 120 km of the radar range, and (2) continuous tracking for at least 6 timesteps (approx. 30 minutes, depending on radar scan rate). SPC hail data is matched to tracking timesteps. We used this data to train a Random Forest model for hail size nowcasting.

Further metadata can be requested from the authors.
DOI: 10.5281/zenodo.10401246
title: MCIT storm tracks with updraft radar data for large hail cases in the United States (2013 - 2023)

description: Storm tracking data from 114 hail days in the United States between 2013 and 2023. Cases were selected based on the availability of abundant Storm Prediction Center (SPC) hail reports (either at least 20 reports of hail > 1 cm or five reports > 10 cm) in a range between 20 and 90 km from a NEXRAD radar site. Only cases in a geographic area from 28-48°N and 105-90°E, roughly corresponding to the U.S. Great Plains, were considered. Cells are quality filtered based on these criteria: (1) Cell center within 120 km of the radar range, and (2) continuous tracking for at least 6 timesteps (approx. 30 minutes, depending on radar scan rate). SPC hail data is matched to tracking timesteps. We used this data to train a Random Forest model for hail size nowcasting.

Further metadata can be requested from the authors.
DOI: 10.6084/m9.figshare.24427375
title: Case report: tularaemia in a white-handed gibbon (<i>Hylobates lar</i>), Germany

description: In 2021, a white-handed gibbon (<i>Hylobates lar</i>) succumbed to illness shortly after transfer from one zoo to another in Germany, due to <i>Francisella tularensis</i> subsp. <i>holarctica</i> infection. To determine the source of infection, whole genome sequencing of the gibbon-derived isolate was performed and wild pest rodents (and captive squirrels) from both zoos were screened for <i>F. tularensis</i>. The <i>F. tularensis</i> whole genome sequence obtained from the gibbon was closely related to previous subclade B.281 sequences obtained from hares from Baden-Wuerttemberg, the same region where the gibbon was first housed. However, <i>F. tularensis</i> DNA was detected in one Norway rat from the receiving zoo. Therefore, neither zoo can be excluded as the source of infection.
DOI: 10.6084/m9.figshare.24427375.v1
title: Case report: tularaemia in a white-handed gibbon (<i>Hylobates lar</i>), Germany

description: In 2021, a white-handed gibbon (<i>Hylobates lar</i>) succumbed to illness shortly after transfer from one zoo to another in Germany, due to <i>Francisella tularensis</i> subsp. <i>holarctica</i> infection. To determine the source of infection, whole genome sequencing of the gibbon-derived isolate was performed and wild pest rodents (and captive squirrels) from both zoos were screened for <i>F. tularensis</i>. The <i>F. tularensis</i> whole genome sequence obtained from the gibbon was closely related to previous subclade B.281 sequences obtained from hares from Baden-Wuerttemberg, the same region where the gibbon was first housed. However, <i>F. tularensis</i> DNA was detected in one Norway rat from the receiving zoo. Therefore, neither zoo can be excluded as the source of infection.
DOI: 10.6084/m9.figshare.23245625
title: Figure 3 fire susceptibility odds ratios

description: Raw dataset for Figure 3.
DOI: 10.6084/m9.figshare.23245625.v1
title: Figure 3 fire susceptibility odds ratios

description: Raw dataset for Figure 3.
DOI: 10.5281/zenodo.10402028
title: King and Welford 2023 data files (submitted to Basin Research).

description: Newfoundland margin crustal thickness estimates, python scripts, and GPlates files used to compute crustal restorations presented in King and Welford 2023 (submitted to Basin Research).
DOI: 10.5281/zenodo.10402027
title: King and Welford 2023 data files (submitted to Basin Research).

description: Newfoundland margin crustal thickness estimates, python scripts, and GPlates files used to compute crustal restorations presented in King and Welford 2023 (submitted to Basin Research).
DOI: 10.6084/m9.figshare.23245628
title: Figure 6 random survival forest variable importance

description: The importances of variables explaining post-fire recovery rates. The importances were derived from a random survival forest (RSF) model.
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DOI: 10.5281/zenodo.10373140
title: Dataset:  Bivalvia species checklist from the Gulf of Mexico

description: Here we update the bivalve based on Turgeon et al. (2009) checklist, and using the same definition of GoM, we created a species list containing records from three main sources: 1) an extensive search of publications (scientific articles, books and government agency reports) from 2006, the most recent year included in the chapter on bivalves in the book of Tunnell et al. (2009), to December 2020 within the area; 2) the online datasets GBIF and Invert-E-base (retrieved on October 2023) and 3) all the bivalve records in the Colección de Moluscos de la Península de Yucatán (CMPY), from Facultad de Ciencias, Sisal UNAM.. We list a total of 640 species of Bivalvia.
DOI: 10.5281/zenodo.10393659
title: Dataset:  Bivalvia species checklist from the Gulf of Mexico

description: Here we update the bivalve based on Turgeon et al. (2009) checklist, and using the same definition of GoM, we created a species list containing records from three main sources: 1) an extensive search of publications (scientific articles, books and government agency reports) from 2006, the most recent year included in the chapter on bivalves in the book of Tunnell et al. (2009), to December 2020 within the area; 2) the online datasets GBIF and Invert-E-base (retrieved on October 2023) and 3) all the bivalve records in the Colección de Moluscos de la Península de Yucatán (CMPY), from Facultad de Ciencias, Sisal UNAM. We list a total of 640 species of Bivalvia.
DOI: 10.5281/zenodo.7863881
title: Dataset:  Bivalvia species checklist from the Gulf of Mexico

description: Here we update the bivalve based on Turgeon et al. (2009) checklist, and using the same definition of GoM, we created a species list containing records from three main sources: 1) an extensive search of publications (scientific articles, books and government agency reports) from 2006, the most recent year included in the chapter on bivalves in the book of Tunnell et al. (2009), to December 2020 within the area; 2) the online datasets GBIF and Invert-E-base (retrieved on October 2023) and 3) all the bivalve records in the Colección de Moluscos de la Península de Yucatán (CMPY), from Facultad de Ciencias, Sisal UNAM. We list a total of 640 species of Bivalvia.
DOI: 10.5281/zenodo.7863882
title: Dataset: Bivalvia species checklist from the Gulf of Mexico

description: Here we update the bivalve checklist of Turgeon et al. (2009) using recent literature, describe species distributions across two biogeographical regions. We list a total of 551 species of Bivalvia that belong to 295 genus, 76 families, and 18 orders. The compilation came from 53 peer-reviewed papers published between 2009 and 2020.
DOI: 10.6084/m9.figshare.23245637
title: Figure 6 and 7 grassland fire recovery times

description: Post-fire recovery data after grassland fires.
<br>
Columns - 
grass_surv_time: the time taken for grasslands to transition to the next successional stage after fires
censored: 0=did not transition to the next stage throughout study period; 1=transitioned to next stage in study period
TWI: SAGA wetness index (SWI in paper)
TPI: Topographic postion index
slope: slope
aspect: aspect in radians
cos_aspect: linearized aspect (see explanation in paper)
RBR: burn severity measured in ts-RBR
prev_fire_freq: how many times it burnt (not used)
fire_freq_per_year: how many times it burnt per year on average (not used)
veg_class_before_fire: 1=forest; 2=grassland; 4=shurbland
forest_distance: distance from nearest forest patch
DOI: 10.6084/m9.figshare.23245637.v1
title: Figure 6 and 7 grassland fire recovery times

description: Post-fire recovery data after grassland fires.
<br>
Columns - 
grass_surv_time: the time taken for grasslands to transition to the next successional stage after fires
censored: 0=did not transition to the next stage throughout study period; 1=transitioned to next stage in study period
TWI: SAGA wetness index (SWI in paper)
TPI: Topographic postion index
slope: slope
aspect: aspect in radians
cos_aspect: linearized aspect (see explanation in paper)
RBR: burn severity measured in ts-RBR
prev_fire_freq: how many times it burnt (not used)
fire_freq_per_year: how many times it burnt per year on average (not used)
veg_class_before_fire: 1=forest; 2=grassland; 4=shurbland
forest_distance: distance from nearest forest patch
DOI: 10.6084/m9.figshare.24851067.v1
title: Prediction of intravenous methylprednisolone therapeutic response in first diagnosed multiple sclerosis using risk factor categories.zip

description: Data of "<b>Prediction of intravenous methylprednisolone</b><b> </b><b>therapeutic response in </b><b>first </b><b>diagnosed multiple sclerosis</b><b> using risk factor categories</b>"
DOI: 10.6084/m9.figshare.24851067
title: Prediction of intravenous methylprednisolone therapeutic response in first diagnosed multiple sclerosis using risk factor categories.zip

description: Data of "<b>Prediction of intravenous methylprednisolone</b><b> </b><b>therapeutic response in </b><b>first </b><b>diagnosed multiple sclerosis</b><b> using risk factor categories</b>"
DOI: 10.6084/m9.figshare.23245643
title: Figure 6 and 7 shrubland fire recovery times

description: Post-fire recovery data after shrubland fires.
<br>
Columns - 
shrub_surv_time: the time taken for shrublands to transition to the next successional stage after fires
censored: 0=did not transition to the next stage throughout study period; 1=transitioned to next stage in study period
TWI: SAGA wetness index (SWI in paper)
TPI: Topographic postion index
slope: slope
aspect: aspect in radians
cos_aspect: linearized aspect (see explanation in paper)
RBR: burn severity measured in ts-RBR
prev_fire_freq: how many times it burnt (not used)
fire_freq_per_year: how many times it burnt per year on average (not used)
veg_class_before_fire: 1=forest; 2=grassland; 4=shurbland
forest_distance: distance from nearest forest patch
DOI: 10.6084/m9.figshare.23245643.v1
title: Figure 6 and 7 shrubland fire recovery times

description: Post-fire recovery data after shrubland fires.
<br>
Columns - 
shrub_surv_time: the time taken for shrublands to transition to the next successional stage after fires
censored: 0=did not transition to the next stage throughout study period; 1=transitioned to next stage in study period
TWI: SAGA wetness index (SWI in paper)
TPI: Topographic postion index
slope: slope
aspect: aspect in radians
cos_aspect: linearized aspect (see explanation in paper)
RBR: burn severity measured in ts-RBR
prev_fire_freq: how many times it burnt (not used)
fire_freq_per_year: how many times it burnt per year on average (not used)
veg_class_before_fire: 1=forest; 2=grassland; 4=shurbland
forest_distance: distance from nearest forest patch
DOI: 10.5281/zenodo.10068977
title: Review of redshift values of bright AGNs with hard spectra in 4LAC catalog v3

description: Review of redshift values of bright AGNs with hard spectra in 4LAC catalog


FITS table and description file (pdf)
DOI: 10.5281/zenodo.5512660
title: Review of redshift values of bright AGNs with hard spectra in 4LAC catalog

description: Review of redshift values of bright AGNs with hard spectra in 4LAC catalog FITS table and description file (pdf)
DOI: 10.5281/zenodo.4721385
title: Review of redshift values of bright AGNs with hard spectra in 4LAC catalog v3

description: Review of redshift values of bright AGNs with hard spectra in 4LAC catalog


FITS table and description file (pdf)
DOI: 10.5281/zenodo.4721386
title: Review of redshift values of bright AGNs with hard spectra in 4LAC catalog

description: Review of redshift values of bright AGNs with hard spectra in 4LAC catalog FITS table and description file (pdf)
DOI: 10.6084/m9.figshare.23245640
title: Figure 5 dataframe

description: The dataframe used the generate the area plot in Figure 3.
<br>
Columns - 
time: time after fire
g: proportion of pixels being grasslands
s: proportion of pixels being shrublands
sfg: proportion of pixels being shrublands that developed from burnt grasslands
f: proportion of pixels being forests
ffg: proportion of pixels being forests that developed from burnt grasslands
ffs: proportion of pixels being forests that developed from burnt shrublands
DOI: 10.6084/m9.figshare.23245640.v1
title: Figure 5 dataframe

description: The dataframe used the generate the area plot in Figure 3.
<br>
Columns - 
time: time after fire
g: proportion of pixels being grasslands
s: proportion of pixels being shrublands
sfg: proportion of pixels being shrublands that developed from burnt grasslands
f: proportion of pixels being forests
ffg: proportion of pixels being forests that developed from burnt grasslands
ffs: proportion of pixels being forests that developed from burnt shrublands
DOI: 10.5281/zenodo.10401144
title: Building footprints Oldenburg derived from aerial imagery

description: This data set contains 78038 georeferenced polygons representing all building footprints within the administrative boundaries of the city of Oldenburg, Lower Saxony, Germany. These geometries were created by a deep learning-based image segmentation. The model for this was trained at the State Office of Lower Saxony for Geoinformation and Surveying (LGLN).

We publish this data under the CC0 license. You can do whatever you want with it. There are no restrictions.If you do something great with the data set, we'd love to hear about it: ki-gebaeudeerkennung@geolabs.atlassian.netIf you use this dataset, you are welcome to reference it - but you don't have to.Would you like builiding footprints for another area? We'd love to hear about it.