cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2nb2dh
title: CCDC 2452546: Experimental Crystal Structure Determination

description: Related Article: Schollmeyer, Dieter, Patrick Limberg, Udo Nubbemeyer|2025|CSD Communication|||
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: 10.5517/ccdc.csd.cc2nb55c
title: CCDC 2452632: Experimental Crystal Structure Determination

description: Related Article: Merzweiler, Kurt, Halz, Jan Henrik|2025|CSD Communication|||
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: 10.5517/ccdc.csd.cc2nb7py
title: CCDC 2452710: Experimental Crystal Structure Determination

description: Related Article: Roger Mafua, Titus Jenny and Helen Stoeckli-Evans|2025|CSD Communication|||
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: 10.5517/ccdc.csd.cc2nb93f
title: CCDC 2452754: Experimental Crystal Structure Determination

description: Related Article: Enrico Spoletti, Ting Wen, Fucheng Leng, Nathan Boukioud, Jason Carty Lacey, Shaza Darwish, Oleksii Shemchuk, Koen Robeyns, Tom Leyssens, Matteo Lusi|2025|Cryst.Growth Des.|25|6754|doi:10.1021/acs.cgd.5c00735
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: 10.5517/ccdc.csd.cc2nb94g
title: CCDC 2452755: Experimental Crystal Structure Determination

description: Related Article: Enrico Spoletti, Ting Wen, Fucheng Leng, Nathan Boukioud, Jason Carty Lacey, Shaza Darwish, Oleksii Shemchuk, Koen Robeyns, Tom Leyssens, Matteo Lusi|2025|Cryst.Growth Des.|25|6754|doi:10.1021/acs.cgd.5c00735
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: 10.5517/ccdc.csd.cc2nb95h
title: CCDC 2452756: Experimental Crystal Structure Determination

description: Related Article: Enrico Spoletti, Ting Wen, Fucheng Leng, Nathan Boukioud, Jason Carty Lacey, Shaza Darwish, Oleksii Shemchuk, Koen Robeyns, Tom Leyssens, Matteo Lusi|2025|Cryst.Growth Des.|25|6754|doi:10.1021/acs.cgd.5c00735
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: 10.5517/ccdc.csd.cc2nb98l
title: CCDC 2452759: Experimental Crystal Structure Determination

description: Related Article: Enrico Spoletti, Ting Wen, Fucheng Leng, Nathan Boukioud, Jason Carty Lacey, Shaza Darwish, Oleksii Shemchuk, Koen Robeyns, Tom Leyssens, Matteo Lusi|2025|Cryst.Growth Des.|25|6754|doi:10.1021/acs.cgd.5c00735
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: 10.5517/ccdc.csd.cc2nbmg3
title: CCDC 2453075: Experimental Crystal Structure Determination

description: Related Article: Alayrac, Carole, Witulski, Bernhard, Schollmeyer, Dieter|2025|CSD Communication|||
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: 10.5517/ccdc.csd.cc2nbxhf
title: CCDC 2453355: Experimental Crystal Structure Determination

description: Related Article: Xiaolin Wu, Jiashuo Zhao, Xuzheng Chen, Yu Gu, Xiaolong Luo|2025|J.Mol.Struct.|1348|143489|doi:10.1016/j.molstruc.2025.143489
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: 10.5517/ccdc.csd.cc2nbxjg
title: CCDC 2453356: Experimental Crystal Structure Determination

description: Related Article: Xiaolin Wu, Jiashuo Zhao, Xuzheng Chen, Yu Gu, Xiaolong Luo|2025|J.Mol.Struct.|1348|143489|doi:10.1016/j.molstruc.2025.143489
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: 10.5517/ccdc.csd.cc2nbxkh
title: CCDC 2453357: Experimental Crystal Structure Determination

description: Related Article: Xiaolin Wu, Jiashuo Zhao, Xuzheng Chen, Yu Gu, Xiaolong Luo|2025|J.Mol.Struct.|1348|143489|doi:10.1016/j.molstruc.2025.143489
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: 10.6084/m9.figshare.30002773
title: Quantitative cytoarchitectural phenotyping of deparaffinized human brain tissues: source data

description: This repository provides the numerical source data for the <i>Communications Biology</i> publication <i>‘Quantitative cytoarchitectural phenotyping of deparaffinized human brain tissues’ </i>by Di Meo, Sorelli et al.The dataset is organized into the following folders:<b>Figure4</b>: numerical source data for the boxplots and probability density distributions presented in Figure 4 (<i>Quantitative cytoarchitectural analysis of pediatric human brain specimens</i>).<b>LSFM_data</b>: light-sheet fluorescence microscopy (LSFM) data, including maximum intensity projections of multichannel LSFM acquisitions.<b>TPFM_data</b>: two-photon fluorescence microscopy (TPFM) data, further structured into the following subfolders:<b>raw_tpfm</b>: raw tiled TPFM tissue reconstructions<b>pixel_classification</b>: outputs from ilastik’s pixel classification workflow (supervised machine learning–based image enhancement).<b>object_classification</b>: outputs from ilastik’s object classification workflow, including class-wise object masks and quantitative cellular features.
DOI: 10.6084/m9.figshare.30002773.v2
title: Quantitative cytoarchitectural phenotyping of deparaffinized human brain tissues: source data

description: This repository provides the numerical source data for the <i>Communications Biology</i> publication <i>‘Quantitative cytoarchitectural phenotyping of deparaffinized human brain tissues’ </i>by Di Meo, Sorelli et al.The dataset is organized into the following folders:<b>Figure4</b>: numerical source data for the boxplots and probability density distributions presented in Figure 4 (<i>Quantitative cytoarchitectural analysis of pediatric human brain specimens</i>).<b>LSFM_data</b>: light-sheet fluorescence microscopy (LSFM) data, including maximum intensity projections of multichannel LSFM acquisitions.<b>TPFM_data</b>: two-photon fluorescence microscopy (TPFM) data, further structured into the following subfolders:<b>raw_tpfm</b>: raw tiled TPFM tissue reconstructions<b>pixel_classification</b>: outputs from ilastik’s pixel classification workflow (supervised machine learning–based image enhancement).<b>object_classification</b>: outputs from ilastik’s object classification workflow, including class-wise object masks and quantitative cellular features.
DOI: 10.6084/m9.figshare.30002773.v1
title: Quantitative cytoarchitectural phenotyping of deparaffinized human brain tissues: source data

description: This repository provides the numerical source data for the <i>Communications Biology</i> publication <i>‘Quantitative cytoarchitectural phenotyping of deparaffinized human brain tissues’ </i>by Di Meo, Sorelli et al.The dataset is organized into the following folders:<b>Figure4</b>: numerical source data for the boxplots and probability density distributions presented in Figure 4 (<i>Quantitative cytoarchitectural analysis of pediatric human brain specimens</i>).<b>LSFM_data</b>: light-sheet fluorescence microscopy (LSFM) data, including maximum intensity projections of multichannel LSFM acquisitions.<b>TPFM_data</b>: two-photon fluorescence microscopy (TPFM) data, further structured into the following subfolders:<b>raw_tpfm</b>: raw tiled TPFM tissue reconstructions<b>pixel_classification</b>: outputs from ilastik’s pixel classification workflow (supervised machine learning–based image enhancement).<b>object_classification</b>: outputs from ilastik’s object classification workflow, including class-wise object masks and quantitative cellular features.
DOI: 10.5281/zenodo.17037291
title: Data for Changning TRL location
DOI: 10.5281/zenodo.17037292
title: Data for Changning TRL location
DOI: 10.5281/zenodo.17037265
title: Ensemble Habitat Distribution Maps for EUNIS Habitat Type S22

description: This dataset contains the outputs of Use Case 5 (FAIRiCUBE), representing ensemble habitat distribution maps for the EUNIS habitat type S22 (Alpine and subalpine ericoid heath). The maps were generated within a machine learning (ML) modelling framework and are designed to be compared with the official European Nature Information System (EUNIS) habitat mapping method.

The modelling approach integrates species occurrence records from vegetation surveys, citizen science initiatives, and museum collections, all retrieved from GBIF, for the eight diagnostic species (including taxonomic synonyms) associated with habitat S22. These records were combined with environmental predictor variables derived from Earth Observation and climatic data sources to produce harmonised, spatially aligned habitat maps individually for each species. Individual maps were then aggregated to final maps representing the Habitat S22 at 1km and 100 m resolutions. 

Area of Interest: Europe (1 km resolution) and the Alps region (100 m resolution).

Diagnostic species (S22) involved: 



 Dryas octopetala (GBIF DOI doi.org/10.15468/dl.pcb2f5)

 Helictochloa versicolor (GBIF DOI doi.org/10.15468/dl.fjpt7s)

Hieracium alpinum (GBIF DOI doi.org/10.15468/dl.et8s3p)

 Homogyne alpina (GBIF DOI doi.org/10.15468/dl.77nej7)

 Loiseleuria procumbens (GBIF DOI doi.org/10.15468/dl.f2uzb6)

 Rhododendron ferrugineum (GBIF DOI doi.org/10.15468/dl.4xqbxx)

 Scorzoneroides helvetica (GBIF DOI doi.org/10.15468/dl.9f2jeu)

 Vaccinium uliginosum (GBIF DOI doi.org/10.15468/dl.4cdnm2)


Environmental variables: 



Aridity Index

Elevation

Slope

HLI

TRI

TWI

Temperature

Seasonal temperature

Precipitation

Seasonal precipitation


All can be found at the UC5 GitHub repository
DOI: 10.5281/zenodo.17037266
title: Ensemble Habitat Distribution Maps for EUNIS Habitat Type S22

description: This dataset contains the outputs of Use Case 5 (FAIRiCUBE), representing ensemble habitat distribution maps for the EUNIS habitat type S22 (Alpine and subalpine ericoid heath). The maps were generated within a machine learning (ML) modelling framework and are designed to be compared with the official European Nature Information System (EUNIS) habitat mapping method.

The modelling approach integrates species occurrence records from vegetation surveys, citizen science initiatives, and museum collections, all retrieved from GBIF, for the eight diagnostic species (including taxonomic synonyms) associated with habitat S22. These records were combined with environmental predictor variables derived from Earth Observation and climatic data sources to produce harmonised, spatially aligned habitat maps individually for each species. Individual maps were then aggregated to final maps representing the Habitat S22 at 1km and 100 m resolutions. 

Area of Interest: Europe (1 km resolution) and the Alps region (100 m resolution).

Diagnostic species (S22) involved: 



 Dryas octopetala (GBIF DOI doi.org/10.15468/dl.pcb2f5)

 Helictochloa versicolor (GBIF DOI doi.org/10.15468/dl.fjpt7s)

Hieracium alpinum (GBIF DOI doi.org/10.15468/dl.et8s3p)

 Homogyne alpina (GBIF DOI doi.org/10.15468/dl.77nej7)

 Loiseleuria procumbens (GBIF DOI doi.org/10.15468/dl.f2uzb6)

 Rhododendron ferrugineum (GBIF DOI doi.org/10.15468/dl.4xqbxx)

 Scorzoneroides helvetica (GBIF DOI doi.org/10.15468/dl.9f2jeu)

 Vaccinium uliginosum (GBIF DOI doi.org/10.15468/dl.4cdnm2)


Environmental variables: 



Aridity Index

Elevation

Slope

HLI

TRI

TWI

Temperature

Seasonal temperature

Precipitation

Seasonal precipitation


All can be found at the UC5 GitHub repository
DOI: 10.5281/zenodo.17010526
title: Machine-learning analysis of solar flare light-curve morphology and implications for stellar CME prediction

description: This repository contains the training and testing datasets, extracted features of solar flares, FGKM-type late-type main-sequence star superflares and Sun-like star superflares, as well as event catalogs, the five trained LR sub-models, and the prediction code.

"train_datasets.zip" and "test_datasets.zip". These files contain the training and testing datasets, respectively. The solar flare events were randomly split into training and testing sets with a ratio of 8:2 using five different random seeds, resulting in five independent subsets. The column names are consistent with the feature extraction descriptions in the paper.

"solar_feature.csv", "FGKM_late_main_sequence_stars_feature.csv" and "sun_like_stars_feature.csv". These files contain the extracted features of solar flares, FGKM-type late-type main-sequence star superflares, and Sun-like star superflares, respectively.

"solar_flare_event_catalog.csv" and "stellar_superflare_catalog.csv". These files represent the catalogs of solar flare events and stellar superflare events. "solar_flare_event_catalog.csv"— SDO/EVE-ESP 0.1-7nm broadband solar flare samples containing 1,156 events. Columns: No. — Event serial number; Start_Time(UTC), Peak_Time(UTC), End_Time(UTC) — Event start, peak, and end times in UTC; Class_Type — Flare class; CME_Association — 1 = eruptive flare (associated with CME), 0 = confined flare (no CME). "stellar_superflare_catalog.csv" — After quality selection, the final Kepler 30-minutes cadence white-light stellar flare sample contains 17,717 events. Columns: No. — Event serial number; Star_Name — Host star identifier; Start_Time, End_Time — Flare start and end times (BJD_TDB − 2,454,833; days); ED — Equivalent duration (s); Energy — Bolometric energy (erg); CME_Association — 1 = with CME, 0 = without CME; Reference — Source of flare.

"logic_models.zip". This file contains the five trained LR sub-models.

"prediction_code.zip". This file contains the prediction code.
DOI: 10.15468/dl.by2cqx
title: Occurrence Download

description: A dataset containing 73371 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Coordinate rounded, Geodetic datum invalid, Geodetic datum assumed WGS84, Coordinate reprojected, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate accuracy invalid, Coordinate precision/uncertainty mismatch, Footprint SRS invalid, Footprint WKT invalid, Country mismatch, Country invalid, Country derived from coordinates, Continent coordinate mismatch, Continent country mismatch, Continent invalid, Continent derived from country, Continent derived from coordinates, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Recorded date unlikely, Taxon match aggregate, The scientific name ID was not used in backbone matching, The taxon concept ID was not used in backbone matching, The taxon ID was not used in backbone matching, The scientific name ID was not found in the checklist, The taxon concept ID was not found in the checklist, The taxon ID was not found in the checklist, The scientific name differs on the record from that found in the checklist, Taxon match none, The backbone match found using the taxon IDs differs from what would be found if using the scientific names, Depth not metric, Depth unlikely, Depth min max swapped, Depth non numeric, Elevation unlikely, Elevation min max swapped, Elevation not metric, Elevation non numeric, Modified date invalid, Modified date unlikely, Identified date unlikely, Identified date invalid, Basis of record invalid, Type status invalid, Multimedia date invalid, Multimedia URI invalid, References URI invalid, Error during interpretation, Individual count invalid, Individual count conflicts with occurrence status, Occurrence status unparsable, Occurrence status inferred from individual count, Occurrence status inferred from basis of record, Georeferenced date unlikely, Georeferenced date invalid, Ambiguous institution, Ambiguous collection, Institution match none, Collection match none, Institution match fuzzy, Collection match fuzzy, Institution collection mismatch, Possibly on loan, Different owner institution)",
 "OccurrenceStatus is Present",
 "TaxonKey is Aerodramus germani"
 ]
}

 The dataset includes 73371 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019157-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q7x5hj
title: Occurrence Download

description: A dataset containing 78117 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aves",
 "Year is greater than or equal to 1900",
 "Year is less than or equal to 1990",
 "HasCoordinate is true",
 "Geometry MULTIPOLYGON (((-41 -19, -41 -18, -42 -18, -42 -19, -41 -19)), ((-66 -18, -66 -17, -67 -17, -67 -18, -66 -18)), ((-56 -20, -56 -19, -57 -19, -57 -20, -56 -20)), ((-72 -14, -72 -13, -73 -13, -74 -13, -74 -14, -73 -14, -72 -14)), ((-63 -15, -63 -14, -64 -14, -64 -15, -63 -15)), ((-44 -17, -44 -16, -45 -16, -46 -16, -46 -18, -45 -18, -45 -17, -44 -17)), ((-80 -5, -80 -6, -79 -6, -79 -4, -80 -4, -81 -4, -81 -5, -80 -5)), ((-78 -1, -78 9.998757e-11, -79 9.998757e-11, -79 -1, -78 -1)), ((-81 -1, -81 -3, -80 -3, -80 -1, -81 -1)), ((-77 -9, -77 -8, -78 -8, -78 -9, -77 -9)), ((-47 -7, -48 -7, -48 -9, -49 -9, -49 -10, -48 -10, -48 -12, -49 -12, -49 -13, -48 -13, -47 -13, -47 -15, -46 -15, -45 -15, -45 -14, -44 -14, -44 -16, -43 -16, -43 -15, -42 -15, -42 -12, -41 -12, -41 -11, -40 -11, -40 -10, -39 -10, -38 -10, -37 -10, -37 -9, -36 -9, -36 -7, -35 -7, -35 -5, -36 -5, -37 -5, -38 -5, -38 -4, -39 -4, -39 -3, -40 -3, -41 -3, -42 -3, -42 -4, -43 -4, -43 -5, -44 -5, -45 -5, -46 -5, -46 -6, -47 -6, -47 -7), (-46 -13, -45 -13, -45 -14, -46 -14, -46 -13)), ((-78 -7, -78 -6, -79 -6, -79 -7, -78 -7)), ((-57 -34, -57 -33, -56 -33, -55 -33, -54 -33, -54 -34, -53 -34, -53 -33, -52 -33, -52 -31, -51 -31, -50 -31, -50 -30, -51 -30, -51 -28, -52 -28, -53 -28, -54 -28, -54 -29, -55 -29, -55 -28, -56 -28, -57 -28, -57 -25, -58 -25, -58 -22, -57 -22, -57 -20, -58 -20, -58 -18, -59 -18, -60 -18, -60 -16, -61 -16, -61 -18, -62 -18, -62 -17, -63 -17, -63 -19, -64 -19, -64 -18, -65 -18, -65 -22, -64 -22, -64 -24, -65 -24, -65 -25, -66 -25, -66 -28, -65 -28, -65 -27, -64 -27, -64 -30, -65 -30, -65 -32, -66 -32, -66 -33, -65 -33, -64 -33, -64 -32, -63 -32, -63 -31, -62 -31, -62 -33, -61 -33, -61 -34, -60 -34, -60 -35, -59 -35, -58 -35, -58 -34, -57 -34), (-58 -30, -57 -30, -57 -31, -58 -31, -58 -30), (-52 -30, -53 -30, -53 -29, -52 -29, -52 -30), (-58 -34, -59 -34, -59 -33, -58 -33, -58 -34)), ((-54 -35, -54 -34, -55 -34, -56 -34, -57 -34, -57 -35, -56 -35, -55 -35, -54 -35)))"
 ]
}

 The dataset includes 78117 records from 64 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019153-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17036809
title: Decoration of gold nanoparticles with glycopeptides leads to a lower cellular uptake and liver retention

description: Unspecific uptake by the liver is one of the main drawbacks of the translation of nanomaterials into clinics, preventing their delivery into diseased tissues. Here, we synthesized gold nanoparticles (GNPs) decorated with a sialic acid-displaying glycopeptide to enhance their specific targeting properties by reducing theiruptake inside hepatic cells. We demonstrated the biocompatibility of the glycopeptide-coated GNPs with two different nanomaterial shapes (spherical and rod-like GNPs) and the targeting properties of the glycopeptide were retained in serum-free and protein-rich media. We found that the glycopeptide reduces nanomaterial interaction with hepatic cells by 1.96 times. In the liver, Kupffer cells (KCs) and liver sinusoidal endothelial cells (LSECs) were the only cells that interacted with the GNPs, increasing the expression of sialic acid-binding receptors such as Siglec-1. This work provides potential new strategies to overcome off-target nanomaterial accumulation by manipulating nanomaterial functionalisation with glycans to alter hepatic cell interactions.