cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5063/f1513wnr
title: Tropical Andes Biodiversity Hotspot: A Comprehensive Dataset for the Mira-Mataje Binational Basin

description: We obtained a set of 36 datasets or databases for records of flora and a set of 85 datasets or databases for records of fauna (Tables 1 and 2). The flora datasets included records of vascular plants, while the fauna datasets included records of mammals, birds, reptiles, and amphibians. The datasets were obtained from various sources, including government agencies, non-governmental organizations, and academic institutions. Data was delimited geographically to those records belonging to the Mira-Mataje basin, between Colombia and Ecuador. We systematized and reorganized the information on all the datasets to create a new dataset based on the Darwin Core format. The Darwin Core format is a standard for sharing biodiversity data, which includes a set of terms for describing and organizing information on species occurrences, taxonomy, and related data. We used the Darwin Core Archive (DwC-A) standard, which provides a way to package biodiversity data and metadata in a self-contained and machine-readable format. Data organization was carried out by hand and by a single researcher who applied available filters to the selected data sources and extracted the corresponding data records.Data format: The .rds file extension saves a single object to be read in R and provides better compression, serialization, and integration within the R environment, than simple .csv files.Source publication: TBD
DOI: 10.5281/zenodo.8065002
title: ERCAS Transparency Index (T-index) 2023

description: The Computer Mediated Government Transparency Index (T-index) provides an <strong>assessment of government transparency</strong> based on different kinds of public information services that governments offer to their citizens, encompassing both <em>de jure</em> (signature of treaties and implementation of laws) and <em>de facto</em> (actual availability of data online) aspects of transparency. This index is an important tool for governments and civic actors around the world. Items missing on the index are a guide for transparency advocates. Read more on the T-index Methodology.
DOI: 10.5281/zenodo.8065001
title: ERCAS Transparency Index (T-index) 2023

description: The Computer Mediated Government Transparency Index (T-index) provides an <strong>assessment of government transparency</strong> based on different kinds of public information services that governments offer to their citizens, encompassing both <em>de jure</em> (signature of treaties and implementation of laws) and <em>de facto</em> (actual availability of data online) aspects of transparency. This index is an important tool for governments and civic actors around the world. Items missing on the index are a guide for transparency advocates. Read more on the T-index Methodology.
DOI: 10.5281/zenodo.8063172
title: DIA benchmarking dataset (based on Gotti et al., 2021)

description: Benchmarking dataset of DIA acquisition methods, based on the manuscript "Extensive and Accurate Benchmarking of DIA Acquisition Methods and Software Tools Using a Complex Proteomic Standard" by Gotti et al. (2021), for the purpose of R vignette describing analysis of DIA data using the QFeatures platform. The raw data are publicly available on the ProteomeXchange platform under identifier PXD026600. Subset of files, containing 'overlapped' in the File Name, were searched using the DIA-NN software. Here, pipeline settings, as well as FASTA files and resulting report.tsv file (here labelled as benchmarkingDIA.tsv) are provided.
DOI: 10.5281/zenodo.8063173
title: DIA benchmarking dataset (based on Gotti et al., 2021)

description: Benchmarking dataset of DIA acquisition methods, based on the manuscript "Extensive and Accurate Benchmarking of DIA Acquisition Methods and Software Tools Using a Complex Proteomic Standard" by Gotti et al. (2021), for the purpose of R vignette describing analysis of DIA data using the QFeatures platform. The raw data are publicly available on the ProteomeXchange platform under identifier PXD026600. Subset of files, containing 'overlapped' in the File Name, were searched using the DIA-NN software. Here, pipeline settings, as well as FASTA files and resulting report.tsv file (here labelled as benchmarkingDIA.tsv) are provided.
DOI: 10.6084/m9.figshare.21981842
title: Inventory of landslides triggered by an extreme rainfall event in Marche-Umbria, Italy, on 15 September 2022

description: This paper describes the event landslide inventory map produced to record the ground effects of the extreme rainfall event that hit Umbria-Marche, central Italy, on 15th September 2022. The rainfall event hit an area of ~5,000 km<sup>2</sup>, with peak rainfall intensities of 419 mm in 9 hours, an exceptionally intense rainfall for this area. The event generated widespread flooding also outside the area affected by rainfall. The rainfall event produced widespread landslides across an area of nearly 1,000 km<sup>2</sup>. As a direct consequence of floods and landslides, many roads were interrupted and extensive damages were recorded to structures and infrastructures. The landslide inventory presented is the result of an extensive reconnaissance field survey covering a large neighbourhood of the area affected by the highest rainfall intensity. The inventory covers an area of 550 km<sup>2</sup> and includes 1,687 landslides, corresponding to an average density of about 3.1 landslides per square kilometre. Landslide size (Landslide Area, A<sub>L</sub> in m<sup>2</sup>) is in the range ~1 m<sup>2</sup> &lt; A<sub>L</sub> &lt; 5.7 × 10<sup>4</sup> m<sup>2</sup>. Overall, landslides cover an area of 1.1 km<sup>2</sup>,<sup> </sup>which represents 0.2% of the Area of Interest (AOI). In the final inventory, a total of 1,243 landslides were mapped as polygons directly in the field, whereas the remaining 444 landslides were mapped as points (i.e. without information on landslide area). After post-processing activities, 512 polygons were transformed into points because their area was below the threshold of 225 m<sup>2</sup>. Therefore, in the final map, 731 landslides are represented as polygons and 956 as points, ~54% of which kept the value of the original area. This repository stores: A polygon shapefile of the AOI A polygon shapefile of the raw inventory A point shapefile of the raw inventory A polygon shapefile of the final inventory A point shapefile of the final inventory This dataset consistently supports the scale of 1:15 000. As a consequence, all polygons smaller than 225 m<sup>2</sup> (i.e. a square of 1 mm per side in the map) in the raw data polygon layer were transformed into points because their size was too small to be represented as polygons at 1:15000 scale. <br> <br>
DOI: 10.5063/f11834zv
title: The Tropical Andes Biodiversity Hotspot: A Comprehensive Dataset for the Mira-Mataje Binational Basins

description: We present a flora and fauna dataset for the Mira-Mataje binational basins. This is an area shared between southwestern Colombia and northwestern Ecuador, where both the Chocó and Tropical Andes biodiversity hotspots converge. Information from 110 sources was systematized in the Darwin Core Archive (DwC-A) standard and geospatial vector data format for geographic information systems (GIS) (shapefiles). Sources included natural history museums, published literature, and citizen science repositories across 18 countries. The resulting database has 248,372 records from 5,281 species, of which 1,083 are endemic and 680 threatened. The diversity represented in the dataset is equivalent to 10\% of the total plant species and 26\% of the total terrestrial vertebrate species in the hotspots and corresponds to 0.07\% of their total area. The dataset can be used to estimate and compare biodiversity patterns in relation to environmental parameters and provide value to ecosystems, ecoregions, and protected areas. The dataset is a baseline for future assessments of biodiversity in the face of environmental degradation, climate change, and accelerated extinction processes.Data format: The .rds file extension saves a single object to be read in R and provides better compression, serialization, and integration within the R environment, than simple .csv files.Source publication: TBD
DOI: 10.5281/zenodo.8065149
title: clypeosectus_delectus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8065148
title: clypeosectus_delectus_spades_01.fasta.gz

description: .
DOI: 10.5517/ccdc.csd.cc275lmn
title: CCDC 2060961: Experimental Crystal Structure Determination

description: Related Article: Kazuki Ishizaki, Ryota Sugimoto, Yuki Hagiwara, Hideko Koshima, Takuya Taniguchi, Toru Asahi|2021|CrystEngComm|23|5839|doi:10.1039/D1CE00208B
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: 10.5281/zenodo.8063712
title: Single Particle Investigation of Triolein Digestion using Optical Manipulation, Polarized Video Microscopy, and SAXS

description: Hypothesis: Understanding how soft colloids, such as food emulsion droplets, transform based on their environment is critical for various applications, including drug and nutrient delivery and biotechnology. However, the mechanisms behind colloidal transformations within individual oil droplets still need to be better understood. Experiments: This study employs optical micromanipulation with microfluidics and polarized optical video microscopy to investigate the pancreatic lipase- and pH-triggered colloidal transformations in a single triolein droplet. Small-angle X-ray scattering (SAXS) provides complementary statistical insights and allows for detailed structural assignment. Findings: Optical video microscopy recorded the transformation of individual triolein emulsion droplets, with the smooth surface of these spherical particles becoming rough and the entire volume eventually being affected. The polarized microscopy revealed the coexistence of at least two distinct structures in a single particle during digestion, with their ratio and distribution altered by pH. The SAXS analysis assigned the optical anisotropy to emulsified inverse hexagonal- and multilamellar phases, coexisting with isotropic structures such as the micellar cubic phase. These results can help understand the liquid-liquid crystalline phase transformations inside an emulsion droplet and guide the design of advanced food emulsions.
DOI: 10.5281/zenodo.8063711
title: Single Particle Investigation of Triolein Digestion using Optical Manipulation, Polarized Video Microscopy, and SAXS

description: Hypothesis: Understanding how soft colloids, such as food emulsion droplets, transform based on their environment is critical for various applications, including drug and nutrient delivery and biotechnology. However, the mechanisms behind colloidal transformations within individual oil droplets still need to be better understood. Experiments: This study employs optical micromanipulation with microfluidics and polarized optical video microscopy to investigate the pancreatic lipase- and pH-triggered colloidal transformations in a single triolein droplet. Small-angle X-ray scattering (SAXS) provides complementary statistical insights and allows for detailed structural assignment. Findings: Optical video microscopy recorded the transformation of individual triolein emulsion droplets, with the smooth surface of these spherical particles becoming rough and the entire volume eventually being affected. The polarized microscopy revealed the coexistence of at least two distinct structures in a single particle during digestion, with their ratio and distribution altered by pH. The SAXS analysis assigned the optical anisotropy to emulsified inverse hexagonal- and multilamellar phases, coexisting with isotropic structures such as the micellar cubic phase. These results can help understand the liquid-liquid crystalline phase transformations inside an emulsion droplet and guide the design of advanced food emulsions.
DOI: 10.5281/zenodo.8065223
title: lepetodrilus_pustulosus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8065222
title: lepetodrilus_pustulosus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8065174
title: LyNSeC: Lymphoma Nuclear Segmentation and Classification

description: Over the last years, there has been large progress in automated segmentation and classification methods in histological whole slide images (WSIs) stained with hematoxylin and eosin (H&E). Current state-of-the-art techniques are based on diverse datasets of H&E-stained WSIs of different types of predominantly solid cancer. However, there is a lack of publicly available annotated datasets of lymphoma, which is why we generated a labeled diffuse large B-cell lymphoma dataset and denoted it LyNSeC (lymphoma nuclear segmentation and classification). LyNSeC comprises three subsets: LyNSeC 1 consists of 379 IHC images of size 512 x 512 pixels at 40x magnification. In the images, we annotated the contours of each cell nuclei and the cell class: marker-positive or marker-negative. In total, LyNSeC 1 contains 87,316 annotated cell nuclei of four different cases, with 48,171 of them assigned the class negative and 39,145 positive. We included three markers in this dataset showing visually different staining patterns: cluster of differentiation 3 (CD3), Ki67 as a marker of proliferation, and erythroblast transformation-specific (EST)-related gene (ERG). LyNSeC 2 and 3 contain H&E-stained images of 70 different patients. LyNSeC 2 consists of 280 images and LyNSeC 3 of 40 images of size 512 x 512 pixels at 40x magnification. 65,479 and 8,452 nuclei were annotated in LyNSeC 2 and 3, respectively. In LyNSeC 3, the nuclei were also assigned a class label (tumor and non-tumor). 3,747 nuclei were identified as tumors and 4,705 as non-tumors. In the annotation procedure, the contours of the H&E images (LyNSeC 2 and LyNSeC 3) were annotated by two pathologists and by two students (trained by the pathologists). Annotation of the cell classes in LyNSeC 3 was done by the pathologists only. LyNSeC 1 was annotated by the two students who were additionally trained to annotate the contours and to distinguish marker-positive and marker-negative cells. The pathologists inspected and (if necessary) adjusted the LyNSeC 3 annotations.<br> <br> The files are uploaded in '.npy' format. The files of LyNSeC 1 (x_l1.npy) and LyNSeC 3 (x_l3.npy) contain five channels, respectively: the first three are the RGB channels of the images, channel 4 contains the instance maps, and channel 5 the class type maps (for LyNSeC 1 a pixel value of 1 corresponds to the class negative and 2 to the class positive, whereas in LyNSeC 3 1 corresponds to the class non-tumor and 2 to the class tumor). The files of LyNSeC 2 (x_l2.npy) have 4 channels (without the class type map).<br> <br> Additionally, we also make our HoVer-Net-based pre-trained nuclei segmentation and classification models available (he.tar for H&E images and ihc.tar for IHC images).
DOI: 10.5281/zenodo.8065173
title: LyNSeC: Lymphoma Nuclear Segmentation and Classification

description: Over the last years, there has been large progress in automated segmentation and classification methods in histological whole slide images (WSIs) stained with hematoxylin and eosin (H&E). Current state-of-the-art techniques are based on diverse datasets of H&E-stained WSIs of different types of predominantly solid cancer. However, there is a lack of publicly available annotated datasets of lymphoma, which is why we generated a labeled diffuse large B-cell lymphoma dataset and denoted it LyNSeC (lymphoma nuclear segmentation and classification). LyNSeC comprises three subsets: LyNSeC 1 consists of 379 IHC images of size 512 x 512 pixels at 40x magnification. In the images, we annotated the contours of each cell nuclei and the cell class: marker-positive or marker-negative. In total, LyNSeC 1 contains 87,316 annotated cell nuclei of four different cases, with 48,171 of them assigned the class negative and 39,145 positive. We included three markers in this dataset showing visually different staining patterns: cluster of differentiation 3 (CD3), Ki67 as a marker of proliferation, and erythroblast transformation-specific (EST)-related gene (ERG). LyNSeC 2 and 3 contain H&E-stained images of 70 different patients. LyNSeC 2 consists of 280 images and LyNSeC 3 of 40 images of size 512 x 512 pixels at 40x magnification. 65,479 and 8,452 nuclei were annotated in LyNSeC 2 and 3, respectively. In LyNSeC 3, the nuclei were also assigned a class label (tumor and non-tumor). 3,747 nuclei were identified as tumors and 4,705 as non-tumors. In the annotation procedure, the contours of the H&E images (LyNSeC 2 and LyNSeC 3) were annotated by two pathologists and by two students (trained by the pathologists). Annotation of the cell classes in LyNSeC 3 was done by the pathologists only. LyNSeC 1 was annotated by the two students who were additionally trained to annotate the contours and to distinguish marker-positive and marker-negative cells. The pathologists inspected and (if necessary) adjusted the LyNSeC 3 annotations.<br> <br> The files are uploaded in '.npy' format. The files of LyNSeC 1 (x_l1.npy) and LyNSeC 3 (x_l3.npy) contain five channels, respectively: the first three are the RGB channels of the images, channel 4 contains the instance maps, and channel 5 the class type maps (for LyNSeC 1 a pixel value of 1 corresponds to the class negative and 2 to the class positive, whereas in LyNSeC 3 1 corresponds to the class non-tumor and 2 to the class tumor). The files of LyNSeC 2 (x_l2.npy) have 4 channels (without the class type map).<br> <br> Additionally, we also make our HoVer-Net-based pre-trained nuclei segmentation and classification models available (he.tar for H&E images and ihc.tar for IHC images).
DOI: 10.5517/ccdc.csd.cc2b9ngs
title: CCDC 2154235: Experimental Crystal Structure Determination

description: Related Article: Cody F. Dickinson, Glenn P. A. Yap, Marcus A. Tius|2023|Org.Biomol.Chem.|21|5014|doi:10.1039/D3OB00735A
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.cc2b9nht
title: CCDC 2154236: Experimental Crystal Structure Determination

description: Related Article: Cody F. Dickinson, Glenn P. A. Yap, Marcus A. Tius|2023|Org.Biomol.Chem.|21|5014|doi:10.1039/D3OB00735A
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.cc2btfk5
title: CCDC 2169397: Experimental Crystal Structure Determination

description: Related Article: Tong-Tong Wang, Li-Ming Zhao|2022|Chem.Commun.|58|11099|doi:10.1039/D2CC03418B
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.cc2dgr2q
title: CCDC 2218703: Experimental Crystal Structure Determination

description: Related Article: Qiu-Yan Jin, Yuan-Yuan Liang, Zhi-Hui Zhang, Liao Meng, Jun-Shan Geng, Kong-Qiu Hu, Ji-Pan Yu, Zhi-Fang Chai, Lei Mei, Wei-Qun Shi|2023|Chemical Science|14|6330|doi:10.1039/D3SC01343J
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.cc2dgr3r
title: CCDC 2218704: Experimental Crystal Structure Determination

description: Related Article: Qiu-Yan Jin, Yuan-Yuan Liang, Zhi-Hui Zhang, Liao Meng, Jun-Shan Geng, Kong-Qiu Hu, Ji-Pan Yu, Zhi-Fang Chai, Lei Mei, Wei-Qun Shi|2023|Chemical Science|14|6330|doi:10.1039/D3SC01343J
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: 10.34740/kaggle/dsv/5988587
title: TS-EAMCET-LAST-RANK
DOI: 10.6084/m9.figshare.23553006.v1
title: <strong>Seamless seafloor topography determination from shallow to deep waters over island areas using airborne gravimetry</strong>

description: Herewith, we provide ICESat-2 bathymetric data, satellite-derived bathymetry (SDB) estimated from Sentinel-2 imagery, and the bathymetry models derived from airborne gravimetry. The folder with the name of " ICESat-2 data " provides ICESat-2 training and validation data over three shallow water areas (i.e., Areas 1–3). The folder with the name of "satellite-derived bathymetry" provides SDB estimated from Sentinel-2 imagery over three shallow water areas (i.e., Areas 1–3). The folder with the name of "gravity-derived bathymetry" provides bathymetry models derived from airborne gravimetry over three shallow water areas (i.e., Areas 1–3) and one deep water area (i.e., Area 4).
DOI: 10.5281/zenodo.8065750
title: pachydermia_laevis_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8065749
title: pachydermia_laevis_spades_01.fasta.gz

description: .
DOI: 10.15151/esrf-es-1244456965
title: iNEXT Discovery: Structural biology for translational research and discovery.

description: The aim of this proposal is to provide user access to Online crystallography and fragment screening pipelines based on the combination of EMBL’s HTX Lab services and the CrystalDirect Technology and automated data collection at ESRF’s MX beamlines, supported through the collaborative EMBL-ESRF Joint Structural Biology Group (JSBG). This will include three different user access modalities: 1) Large-Scale Fragment Screening, 2) Structural characterization of ligand-target complexes and 3) Online Crystallography (automated protein-to-structure pipeline). This work will be carried out in the context of international collaborative projects. Access to these modalities will require competitive application and peer-review through projects like the E.C. funded iNEXT Discovery or the recently awarded ISIDORe.
DOI: 10.5517/ccdc.csd.cc2dgr4s
title: CCDC 2218705: Experimental Crystal Structure Determination

description: Related Article: Qiu-Yan Jin, Yuan-Yuan Liang, Zhi-Hui Zhang, Liao Meng, Jun-Shan Geng, Kong-Qiu Hu, Ji-Pan Yu, Zhi-Fang Chai, Lei Mei, Wei-Qun Shi|2023|Chemical Science|14|6330|doi:10.1039/D3SC01343J
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.