cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10528506
title: Occurrence cubes at species level for European countries

description: This package contains aggregated occurrence data ("occurrence cubes") at species level for European countries. These occurrence cubes were generated by grouping species occurrence data from the Global Biodiversity Information Facility (GBIF) by year (year), 1x1km spatial EEA reference grid cell (eea_cell_code) and species (speciesKey). For each grouping, the number of occurrences found in GBIF (n) and the minimum coordinateUncertaintyInMeters (min_coord_uncertainty) are provided. The provided coordinateUncertaintyInMeters of an occurrence is taken into account when assigning it to a grid cell (see this code). The occurrence cubes can be used as input data for indicators, mapping and species distribution modelling.

The occurrence cubes are built on open science principles and intended to be completely reproducible:



The input data are publicly available on GBIF, with the download DOIs listed in the related identifiers of this package.

The code to process the data to cubes is publicly available on GitHub at https://github.com/trias-project/occ-cube (version 20240124).


Files



Occurrence cubes at species level per country: filename format countrycode_species_cube.csv.

Taxonomic information for species in a cube: filename format countrycode_species_info.csv.


Included countries



Belgium (BE): based on https://doi.org/10.15468/dl.9qx3ba

Italy (IT): based on https://doi.org/10.15468/dl.jghpm5

Lithuania (LT): based on https://doi.org/10.15468/dl.duegx2

Slovenia (SI): based on https://doi.org/10.15468/dl.9eky98

Romania (RO): based on https://doi.org/10.15468/dl.b7z5vw

Portugal (PT): based on https://doi.org/10.15468/dl.b89nr4


Occurrence cubes are added on demand. To include occurrence cubes for other European countries, leave an issue or contact the main author, or generate your own cube using the code in this repository.
description: This version adds the occurrence cube for Portugal and updates the other occurrence cubes with more recent occurrence data, see datasets mentioned above and in Related works.
DOI: 10.5281/zenodo.4299787
title: Occurrence cubes at species level for European countries

description: This package contains aggregated occurrence data ("occurrence cubes") at species level for European countries. These occurrence cubes were generated by grouping species occurrence data from the Global Biodiversity Information Facility (GBIF) by year (<code>year</code>), 1x1km spatial EEA reference grid cell (<code>eea_cell_code</code>) and species (<code>speciesKey</code>). For each grouping, the number of occurrences found in GBIF (<code>n</code>) and the minimum coordinateUncertaintyInMeters (<code>min_coord_uncertainty</code>) are provided. The provided coordinateUncertaintyInMeters of an occurrence is taken into account when assigning it to a grid cell (see this code). The occurrence cubes can be used as input data for indicators, mapping and species distribution modelling. The occurrence cubes are built on open science principles and intended to be completely reproducible: The input data are publicly available on GBIF, with the download DOIs listed in the related identifiers of this package. The code to process the data to cubes is publicly available on GitHub at https://github.com/trias-project/occ-cube (version 20201201). <strong>Files</strong> <strong>Occurrence cubes at species level per country</strong>: filename format <code>countrycode_species_cube.csv</code>. <strong>Taxonomic information for species in a cube</strong>: filename format <code>countrycode_species_info.csv</code>. <strong>Included countries</strong> <strong>Belgium</strong> (BE): based on https://doi.org/10.15468/dl.97jyjt <strong>Italy</strong> (IT): based on https://doi.org/10.15468/dl.as99qq <strong>Lithuania</strong> (LT): based on https://doi.org/10.15468/dl.v5xe9r <strong>Slovenia</strong> (SI): based on https://doi.org/10.15468/dl.49ksep Occurrence cubes are added on demand. To include occurrence cubes for other European countries, leave an issue or contact the main author, or generate your own cube using the code in this repository.
description: This work has been funded under the Belgian Science Policies Brain program (BelSPO BR/165/A1/TrIAS).
DOI: 10.5281/zenodo.6334816
title: Occurrence cubes at species level for European countries

description: This package contains aggregated occurrence data ("occurrence cubes") at species level for European countries. These occurrence cubes were generated by grouping species occurrence data from the Global Biodiversity Information Facility (GBIF) by year (<code>year</code>), 1x1km spatial EEA reference grid cell (<code>eea_cell_code</code>) and species (<code>speciesKey</code>). For each grouping, the number of occurrences found in GBIF (<code>n</code>) and the minimum coordinateUncertaintyInMeters (<code>min_coord_uncertainty</code>) are provided. The provided coordinateUncertaintyInMeters of an occurrence is taken into account when assigning it to a grid cell (see this code). The occurrence cubes can be used as input data for indicators, mapping and species distribution modelling. The occurrence cubes are built on open science principles and intended to be completely reproducible: The input data are publicly available on GBIF, with the download DOIs listed in the related identifiers of this package. The code to process the data to cubes is publicly available on GitHub at https://github.com/trias-project/occ-cube (version 20220308). <strong>Files</strong> <strong>Occurrence cubes at species level per country</strong>: filename format <code>countrycode_species_cube.csv</code>. <strong>Taxonomic information for species in a cube</strong>: filename format <code>countrycode_species_info.csv</code>. <strong>Included countries</strong> <strong>Belgium</strong> (BE): based on https://10.15468/dl.hefbvj <strong>Italy</strong> (IT): based on https://10.15468/dl.8aasr6 <strong>Lithuania</strong> (LT): based on https://doi.org/10.15468/dl.kmh5hs <strong>Slovenia</strong> (SI): based on https://10.15468/dl.b22yu3 Occurrence cubes are added on demand. To include occurrence cubes for other European countries, leave an issue or contact the main author, or generate your own cube using the code in this repository.
description: This work has been funded under the under the Belgian Science Policies Brain program (BelSPO BR/165/A1/TrIAS) and the LIFE program of the European Union (LIFE19 NAT/BE/000953 - LIFE RIPARIAS).
DOI: 10.5281/zenodo.6341688
title: Occurrence cubes at species level for European countries

description: This package contains aggregated occurrence data ("occurrence cubes") at species level for European countries. These occurrence cubes were generated by grouping species occurrence data from the Global Biodiversity Information Facility (GBIF) by year (<code>year</code>), 1x1km spatial EEA reference grid cell (<code>eea_cell_code</code>) and species (<code>speciesKey</code>). For each grouping, the number of occurrences found in GBIF (<code>n</code>) and the minimum coordinateUncertaintyInMeters (<code>min_coord_uncertainty</code>) are provided. The provided coordinateUncertaintyInMeters of an occurrence is taken into account when assigning it to a grid cell (see this code). The occurrence cubes can be used as input data for indicators, mapping and species distribution modelling. The occurrence cubes are built on open science principles and intended to be completely reproducible: The input data are publicly available on GBIF, with the download DOIs listed in the related identifiers of this package. The code to process the data to cubes is publicly available on GitHub at https://github.com/trias-project/occ-cube (version 20220308). <strong>Files</strong> <strong>Occurrence cubes at species level per country</strong>: filename format <code>countrycode_species_cube.csv</code>. <strong>Taxonomic information for species in a cube</strong>: filename format <code>countrycode_species_info.csv</code>. <strong>Included countries</strong> <strong>Belgium</strong> (BE): based on https://10.15468/dl.hefbvj <strong>Italy</strong> (IT): based on https://10.15468/dl.8aasr6 <strong>Lithuania</strong> (LT): based on https://10.15468/dl.kmh5hs <strong>Slovenia</strong> (SI): based on https://10.15468/dl.b22yu3 <strong>Romania </strong>(RO): based on https://10.15468/dl.u7kkvk Occurrence cubes are added on demand. To include occurrence cubes for other European countries, leave an issue or contact the main author, or generate your own cube using the code in this repository.
description: This work has been funded under the under the Belgian Science Policies Brain program (BelSPO BR/165/A1/TrIAS) and the LIFE program of the European Union (LIFE19 NAT/BE/000953 - LIFE RIPARIAS).
DOI: 10.5281/zenodo.7389450
title: Occurrence cubes at species level for European countries

description: This package contains aggregated occurrence data ("occurrence cubes") at species level for European countries. These occurrence cubes were generated by grouping species occurrence data from the Global Biodiversity Information Facility (GBIF) by year (<code>year</code>), 1x1km spatial EEA reference grid cell (<code>eea_cell_code</code>) and species (<code>speciesKey</code>). For each grouping, the number of occurrences found in GBIF (<code>n</code>) and the minimum coordinateUncertaintyInMeters (<code>min_coord_uncertainty</code>) are provided. The provided coordinateUncertaintyInMeters of an occurrence is taken into account when assigning it to a grid cell (see this code). The occurrence cubes can be used as input data for indicators, mapping and species distribution modelling. The occurrence cubes are built on open science principles and intended to be completely reproducible: The input data are publicly available on GBIF, with the download DOIs listed in the related identifiers of this package. The code to process the data to cubes is publicly available on GitHub at https://github.com/trias-project/occ-cube (version 20221202). <strong>Files</strong> <strong>Occurrence cubes at species level per country</strong>: filename format <code>countrycode_species_cube.csv</code>. <strong>Taxonomic information for species in a cube</strong>: filename format <code>countrycode_species_info.csv</code>. <strong>Included countries</strong> <strong>Belgium</strong> (BE): based on https://doi.org/10.15468/dl.q2pz3n <strong>Italy</strong> (IT): based on https://10.15468/dl.8aasr6 <strong>Lithuania</strong> (LT): based on https://10.15468/dl.kmh5hs <strong>Slovenia</strong> (SI): based on https://10.15468/dl.b22yu3 <strong>Romania </strong>(RO): based on https://10.15468/dl.u7kkvk Occurrence cubes are added on demand. To include occurrence cubes for other European countries, leave an issue or contact the main author, or generate your own cube using the code in this repository.
description: This work has been funded under the under the Belgian Science Policies Brain program (BelSPO BR/165/A1/TrIAS) and the LIFE program of the European Union (LIFE19 NAT/BE/000953 - LIFE RIPARIAS).
DOI: 10.34740/kaggle/dsv/7471733
title: MoviesDatasets
DOI: 10.5281/zenodo.10195162
title: Prognostic Value of Gut Microbe-Generated Metabolite Phenylacetylglutamine in Patients with Heart Failure

description: Excel file containing source data for "Prognostic Value of Gut Microbe-Generated Metabolite Phenylacetylglutamine in Patients with Heart Failure", by Tang et al, published in European Journal of Heart Failure.
DOI: 10.5281/zenodo.10195163
title: Prognostic Value of Gut Microbe-Generated Metabolite Phenylacetylglutamine in Patients with Heart Failure

description: Excel file containing source data for "Prognostic Value of Gut Microbe-Generated Metabolite Phenylacetylglutamine in Patients with Heart Failure", by Tang et al, published in European Journal of Heart Failure.
DOI: 10.34740/kaggle/dsv/7471739
title: Mahanagar Yatayat Transport Dataset
DOI: 10.6084/m9.figshare.23907963
title: Stormwater pond water monitoring results

description: Results of water quality monitoring at 21 stormwater ponds in Brampton, Ontario, from May to July 2022.
DOI: 10.6084/m9.figshare.23907963.v1
title: Stormwater pond water monitoring results

description: Results of water quality monitoring at 21 stormwater ponds in Brampton, Ontario, from May to July 2022.
DOI: 10.25822/40pm-wv54
title: Sleep Health in Infancy and Early Childhood
DOI: 10.5281/zenodo.10563002
title: 3D representation of meso-voids location and morphology within the fibrous pore space of three carbon fabrics

description: Segmented meso-voids from a tomography scan of preforms impregnated under capillary dominated conditions, combined to 3D scans of the initial prefrom dry state.
DOI: 10.5281/zenodo.7712835
title: 3D representation of meso-voids location and morphology within the fibrous pore space of three carbon fabrics

description: Segmented meso-voids from a tomography scan of preforms impregnated under capillary dominated conditions, combined to 3D scans of the initial prefrom dry state.
DOI: 10.5281/zenodo.7712836
title: 3D representation of meso-voids location and morphology within the fibrous pore space of three carbon fabrics

description: Segmented meso-voids from a tomography scan of preforms impregnated under capillary dominated conditions, combined to 3D scans of the initial prefrom dry state.
DOI: 10.26036/cnp0004043
title: Split complementation of base editors to minimize off-target edits

description: Base editors (BEs) empower the efficient installation of beneficial or corrective point mutations in crop and human genomes. However, conventional BEs can induce unpredictable guide RNA (gRNA)-independent off-target edits in the genome and transcriptome due to spurious activities of BE-enclosing deaminases, and current improvements mostly rely on deaminase-specific mutagenesis or exogenous regulators. Here we developed a split deaminase for safe editing (SAFE) system applicable to BEs containing distinct cytidine or adenosine deaminases, with no need of external regulators. In SAFE, a BE was properly split at a deaminase domain embedded inside a Cas9 nickase, simultaneously fragmenting and deactivating both the deaminase and the Cas9 nickase. The gRNA-conditioned BE reassembly conferred robust on-target editing in plant, human and yeast cells, while minimizing both gRNA-independent and gRNA-dependent off-target DNA/RNA edits. SAFE also substantially increased product purity by eliminating indels. Altogether, SAFE provides a generalizable solution for BEs to suppress off-target editing and improve on-target performance.
DOI: 10.15784/601762
title: East Antarctic Seismicity from different Automated Event Detection Algorithms

description: As seismic data availability increases, the necessity for automated processing techniques has become increasingly evident. Expanded geophysical datasets collected over the past several decades across Antarctica provide excellent resources to evaluate different event detection approaches. We have used the traditional Short-Term Average/Long-Term Average (STA/LTA) algorithm to catalogue seismic data recorded by 19 stations in East Antarctica between 2012 and 2015. However, the complexities of the East Antarctic dataset, including low magnitude events and phenomena such as icequakes, warrant more advanced automated detection techniques. Therefore, we have also applied template matching as well as several deep learning algorithms, including Generalized Phase Detection (GPD), PhaseNet, BasicPhaseAE, and EQTransformer (EQT), to identify seismic phases within our dataset. Our goal was not only to increase the volume of detectable seismic events but also to gain insights into the effectiveness of these different automated approaches. Our assessment evaluated the completeness of the newly generated catalogs, the precision of identified event locations, and the quality of the picks. The final events corresponding to each of our three catalogs (based on STA/LTA, template matching, and machine learning, respectively) are listed in the provided files.
DOI: 10.25625/xwkw1r/kdf5ca
title: changelog.schema.json

description: :unav
DOI: 10.25625/xwkw1r/r9c8gg
title: changelog.schema.json

description: :unav
DOI: 10.25625/xwkw1r
title: JSON Schema for the changelog PID

description: This is the JSON schema of the value in a changelog PID. This schema allows users to describe the changes the made to metadata of a PID.
DOI: 10.25625/xwkw1r/8gxwq9
title: changelog.schema.json

description: :unav
DOI: 10.25625/xwkw1r/daboty
title: changelog.schema.json

description: :unav
DOI: 10.25625/xwkw1r/ktzmpf
title: changelog.schema.json

description: :unav
DOI: 10.25625/xwkw1r/m9qyit
title: changelog.schema.json

description: :unav
DOI: 10.5281/zenodo.10210061
title: Dataset for manuscript titled "Exploring SureChEMBL from a drug discovery perspective".

description: This is the data directory for running the code available on the GitHub repository for the manuscript titled "Exploring SureChEMBL from a drug discovery perspective". The GitHub repository is available at https://github.com/Fraunhofer-ITMP/patent-clinical-candidate-characteristics.
DOI: 10.5281/zenodo.10210062
title: Dataset for manuscript titled "Exploring SureChEMBL from a drug discovery perspective".

description: This is the data directory for running the code available on the GitHub repository for the manuscript titled "Exploring SureChEMBL from a drug discovery perspective". The GitHub repository is available at https://github.com/Fraunhofer-ITMP/patent-clinical-candidate-characteristics.
DOI: 10.15784/601763
title: Full Waveform Ambient Noise Tomography for East Antarctica

description: Recent investigations in polar environments have examined solid-Earth-ice-sheet feedbacks and have emphasized that glacial isostatic adjustment, tectonic, and geothermal forcings exert first-order control on the physical conditions at and below the ice-bed interface and must be taken into account when evaluating ice-sheet evolution. However, the solid-Earth structure beneath much of Antarctica is still poorly constrained given the sparse distribution of seismic stations across the continent and the generally low seismicity rate. One region of particular interest is the Wilkes Subglacial Basin (WSB) in East Antarctica. During the mid-Pliocene warm period, the WSB may have contributed 3-4 m to the estimated 20 m rise in sea-level, indicating that this region could also play an important role in future warming scenarios. However, the WSB may have experienced notable bedrock uplift since the Pliocene; therefore, past geologic inferences of instability may not serve as a simple analogue for the future.

Using records of ambient seismic noise recorded by both temporary and long-term seismic networks, along with a full-waveform tomographic inversion technique, we have developed improved images of the lithospheric structure beneath East Antarctica, including the WSB. Empirical Green’s Functions with periods between 40 and 340 s have been extracted using a frequency-time normalization technique, and a finite-difference approach with a spherical grid has been employed to numerically model synthetic seismograms. Associated sensitivity kernels have also been constructed using a scattering integral method. Our results suggest the WSB is underlain by slow seismic velocities, with faster seismic structure beneath the adjacent Transantarctic Mountains and the Belgica Subglacial Highlands. This may indicate that the WSB is associated with a region of thinner lithosphere, possibly associated with prior continental rifting. The seismic heterogeneity highlighted in our model could have significant implications for understanding the geodynamic origin of WSB topography and its influence on ice-sheet behavior.

The model file and associated plotting scripts are provided.
DOI: 10.17028/rd.lboro.23709972
title: Fourier Transform Infrared Microspectroscopy and cancer cells in blood

description: FTIR data are presented for multiple cells within each population and from multiple cell types as listed in column A. Wavenumbers are provided across the data, presented in Row 1, with absorbances provided in the matrix.
This dataset a number of spectra obtained for cell lines (A549, CALU and PBMC; Sheet 1) and those from cancer cells (in all other 'Map' tabs). 
The data are presented here in support of the publication, "Fourier Transform Infrared Microspectroscopy identifies single cancer cells in blood. A feasibility study towards liquid biopsy."
DOI: 10.17028/rd.lboro.23709972.v1
title: Fourier Transform Infrared Microspectroscopy and cancer cells in blood

description: FTIR data are presented for multiple cells within each population and from multiple cell types as listed in column A. Wavenumbers are provided across the data, presented in Row 1, with absorbances provided in the matrix.
This dataset a number of spectra obtained for cell lines (A549, CALU and PBMC; Sheet 1) and those from cancer cells (in all other 'Map' tabs). 
The data are presented here in support of the publication, "Fourier Transform Infrared Microspectroscopy identifies single cancer cells in blood. A feasibility study towards liquid biopsy."
DOI: 10.5517/ccdc.csd.cc29xygn
title: CCDC 2142982: Experimental Crystal Structure Determination

description: Related Article: Clément Falaise, Soumaya Khlifi, Pierre Bauduin, Philipp Schmid, Jéril Degrouard, Amélie Leforestier, William Shepard, Jérôme Marrot, Mohamed Haouas, David Landy, Caroline Mellot-Draznieks, Emmanuel Cadot|2024|J.Am.Chem.Soc.|146|1501|doi:10.1021/jacs.3c11004
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.3886/e195462v1
title: ECIN Replication Package for "Social Learning about Climate Risks"

description: Replication code and data for Social Learning about Climate Risk.<br><br>Abstract: This paper examines whether social learning facilitates climate risk perception updates to inform climate adaptation. We estimate a network difference-in-differences (NDID) model with a social network adjacency matrix constructed from the Facebook Social Connectedness Index (SCI). We find that regional flooding brought by Hurricanes Harvey and Irma increased flood insurance policies nationwide to the extent of each county’s social network proximity to the flooded areas. Such social learning induced 250,000 more policies in flooded counties and 81,000 more policies in unflooded counties in three years. We find evidence of the salience effect but no support for adverse selection or over-insurance.
DOI: 10.3886/e195462v2
title: ECIN Replication Package for "Social Learning about Climate Risks"

description: Replication code and data for Social Learning about Climate Risk.<br><br>Abstract: This paper
examines whether social learning facilitates climate risk perception updates to
inform climate adaptation. We estimate a network difference-in-differences
(NDID) model with a social network adjacency matrix constructed from the
Facebook Social Connectedness Index (SCI). We find that regional flooding
brought by Hurricanes Harvey and Irma increased flood insurance policies nationwide
to the extent of each county’s social network proximity to the flooded areas. Such
social learning induced 250,000 more policies in flooded counties and 81,000
more policies in unflooded counties in three years. We find evidence of the salience
effect but no support for adverse selection or over-insurance.