cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4121/aeb7cf17-1b4a-4f09-a2fb-88d7875c672d.v1
title: Data underlying the publication: Thickness and quality controlled fabrication of fluorescence-targeted frozen-hydrated lamellae

description: IntroductionData underlying the publication: Thickness and quality controlled fabrication of fluorescence-targeted frozen-hydrated lamellae.Publication DOI: https://doi.org/10.1016/j.crmeth.2025.101004Authors: D.B. Boltje, R. Skoupy, C. Taisne, W. H. Evers, A.J. Jakobi, J.P. HoogenboomRepository created by Daan Boltje<br>AbstractCryogenic FIB milling is essential for fabricating thin lamella-shaped samples out of frozen-hydrated cells for high-resolution structure determination. Structural information can only be resolved at high resolution if the lamella thickness is between 100 to 200 nm. While the lamella fabrication workflow has improved significantly since its conception, quantitative, live feedback on lamella thickness, quality, and biological target inclusion remains lacking. Using coincident light microscopy integrated into the FIB-SEM, we present three strategies that enable accurate, live control during lamella fabrication. First, we combine 4D-STEM with FM targeting to determine lamella thickness. Second, with RLM, we screen target sites for ice contamination and monitor lamella thickness and protective Pt coating integrity during FIB milling. Third, we exploit thin-film interference for fine-grained feedback on thickness uniformity below 500 nm. Finally, we present a fluorescence-targeted, quality-controlled workflow for frozen-hydrated lamellae, benchmarked with excellent agreement to EFTEM measurements and tomograms from electron cryo-tomography.<br>General descriptionAll data underlying the manuscript figures is stored in this repository. Experiments have been carried out at the Department of Imaging Physics and Kavli Institute of Nanoscience at Delft University of Technology (Delft, NL) in 2023 and 2024.<br>The repository contains images acquired in a transmission electron microscope, scanning electron microscope, focused ion beam, fluorescence microscope and a reflection light microscope.<br>
DOI: 10.6084/m9.figshare.28682774
title: depression depression

description: depression depression
DOI: 10.4121/e103d825-4662-4659-b238-94effa26d70f
title: Data underlying the publication: Greening Pastures: Ecosystems for sustainable entrepreneurship

description: The dataset for the main analyses in the paper Greening Pastures: Ecosystems for sustainable entrepreneurship. This dataset includes the variables used to answer the research question: <em>What is the influence of the generic and specific elements of sustainable entrepreneurial ecosystems on the presence of sustainable start-ups? </em>The dataset is used for regression analyses to answer this question. The variables for the dataset come from a variety of sources as explained in the paper. The variables are all named the same way as indicated in the regression tables of the paper. In particular it contains: The NUTS_ID of each region, the presence of sustainable start-ups in 2017-2021, the prevalence of sustainable start-ups in 2017-2021, the entrepreneurial ecosystem quality, fellow start-ups in 2015-2017, the Sustainability Resource Endowments, the Sustainability Formal Institutions, the Sustainability Informal Institutions, the Population (per 10,000 inhabitants), the GRP, and the country_code for the country in which the region is located. A more extensive description can be found in the paper.
DOI: 10.4121/e103d825-4662-4659-b238-94effa26d70f.v1
title: Data underlying the publication: Greening Pastures: Ecosystems for sustainable entrepreneurship

description: The dataset for the main analyses in the paper Greening Pastures: Ecosystems for sustainable entrepreneurship. This dataset includes the variables used to answer the research question: <em>What is the influence of the generic and specific elements of sustainable entrepreneurial ecosystems on the presence of sustainable start-ups? </em>The dataset is used for regression analyses to answer this question. The variables for the dataset come from a variety of sources as explained in the paper. The variables are all named the same way as indicated in the regression tables of the paper. In particular it contains: The NUTS_ID of each region, the presence of sustainable start-ups in 2017-2021, the prevalence of sustainable start-ups in 2017-2021, the entrepreneurial ecosystem quality, fellow start-ups in 2015-2017, the Sustainability Resource Endowments, the Sustainability Formal Institutions, the Sustainability Informal Institutions, the Population (per 10,000 inhabitants), the GRP, and the country_code for the country in which the region is located. A more extensive description can be found in the paper.
DOI: 10.6084/m9.figshare.27923973
title: SPTT & SPTEdu-seq dataset

description: <b>Dataset Overview</b><br>This dataset has been generated using our SPTT and SPTEdu-seq techniques. Below are the details for each file included:FilesTo optimize storage, all datasets are compressed into tar.gz format. Please extract the files after downloading.<b>MOB.tar.gz</b><b>Profiled by:</b> SPTT<b>Contents:</b>Digital expression data (in sparse matrix <code>.mtx</code> format)Metadata including:Spatial barcodesSpatial coordinatesSeurat clusters(and cell type annotations)Other essential information<b>MouseBrain.tar.gz</b><b>Profiled by:</b> SPTT<b>Contents:</b>Digital expression data (in sparse matrix <code>.mtx</code> format)Metadata including:Spatial barcodesSpatial coordinatesSeurat clusters(and cell type annotations)Other essential information<b>IS_MOB.tar.gz</b><b>Profiled by:</b> SPTEdu-seq<b>Contents:</b>Digital expression data (in sparse matrix <code>.mtx</code> format)Metadata including:Spatial barcodesSpatial coordinatesSeurat clustersDesignations of barcodes as either <i>hippocampus region</i> or <i>cortex region</i> for cell type deconvolution and RNA velocity assays<br><br><b>IS_uninjured.tar.gz</b>, <b>IS_3dpi.tar.gz</b>, <b>IS_7dpi.tar.gz</b>, and <b>IS_14dpi.tar.gz</b><b>Profiled by:</b> SPTEdu-seq<b>Contents:</b>Digital expression data (in sparse matrix <code>.mtx</code> format)Metadata including:Spatial barcodesSpatial coordinatesUTI profiling numbersSeurat clustersDesignations of barcodes as <i>injury regions</i> (based on unsupervised clustering and spatial coordinates)<br>
DOI: 10.6084/m9.figshare.27923973.v2
title: SPTT & Trace-SPTT dataset

description: <b>Dataset Overview</b><br>This dataset has been generated using our SPTT and SPTEdu-seq techniques. Below are the details for each file included:FilesTo optimize storage, all datasets are compressed into tar.gz format. Please extract the files after downloading.<b>MOB.tar.gz</b><b>Profiled by:</b> SPTT<b>Contents:</b>Digital expression data (in sparse matrix <code>.mtx</code> format)Metadata including:Spatial barcodesSpatial coordinatesSeurat clusters(and cell type annotations)Other essential information<b>MouseBrain.tar.gz</b><b>Profiled by:</b> SPTT<b>Contents:</b>Digital expression data (in sparse matrix <code>.mtx</code> format)Metadata including:Spatial barcodesSpatial coordinatesSeurat clusters(and cell type annotations)Other essential information<b>IS_MOB.tar.gz</b><b>Profiled by:</b> SPTEdu-seq<b>Contents:</b>Digital expression data (in sparse matrix <code>.mtx</code> format)Metadata including:Spatial barcodesSpatial coordinatesSeurat clustersDesignations of barcodes as either <i>hippocampus region</i> or <i>cortex region</i> for cell type deconvolution and RNA velocity assays<br><br><b>IS_uninjured.tar.gz</b>, <b>IS_3dpi.tar.gz</b>, <b>IS_7dpi.tar.gz</b>, and <b>IS_14dpi.tar.gz</b><b>Profiled by:</b> SPTEdu-seq<b>Contents:</b>Digital expression data (in sparse matrix <code>.mtx</code> format)Metadata including:Spatial barcodesSpatial coordinatesUTI profiling numbersSeurat clustersDesignations of barcodes as <i>injury regions</i> (based on unsupervised clustering and spatial coordinates)<br>
DOI: 10.5281/zenodo.15099742
title: Data for "Spreading of recently introduced North American sand dropseed (Sporobolus crytrandus) threatens European native dry grassland arthropods"
DOI: 10.5281/zenodo.15099743
title: Data for "Spreading of recently introduced North American sand dropseed (Sporobolus crytrandus) threatens European native dry grassland arthropods"
DOI: 10.5281/zenodo.15099527
title: The Azov Sea basin ecosystem long-term annual dataset for 1925–2024 and Russian sturgeon occurrence in 2000–2024

description: Dataset presents long-term annual and spatial occurrence data of Russian sturgeon in the Sea of Azov during the long-term observations. 

1. Long-term annual dataset

Present yearly averaged long-term (1925-2024) Azov Sea benthic ecosystem data.

2. Spatial occurrence dataset 

Present spatial in-situ observation data about Russian sturgeon occurrence during the trawl surveys in the Sea of Azov during 2000-2024.
DOI: 10.5281/zenodo.15099528
title: The Azov Sea basin ecosystem long-term annual dataset for 1925–2024 and Russian sturgeon occurrence in 2000–2024

description: Dataset presents long-term annual and spatial occurrence data of Russian sturgeon in the Sea of Azov during the long-term observations. 

1. Long-term annual dataset

Present yearly averaged long-term (1925-2024) Azov Sea benthic ecosystem data.

2. Spatial occurrence dataset 

Present spatial in-situ observation data about Russian sturgeon occurrence during the trawl surveys in the Sea of Azov during 2000-2024.
DOI: 10.6084/m9.figshare.28682771
title: PRISMA 2020 Check List

description: The data related to the AI in sustainable education
DOI: 10.5281/zenodo.14917825
title: Dataset for the paper "Analytical and experimental solutions for Fourier transform infrared microspectroscopy measurements of microparticles: A case study on Quercus pollen"

description: Comparative study on 12 different analytical and experimental approaches for FTIR measurements of microparticles. The approaches were demonstrated on classification and chemical characterisation of pollen of four oak species.

File description:




Quercus microspectroscopy ZnSe
ZnSe dataset after atmospheric compensation


Quercus microspectroscopy ZnSe ME_EMSC
a3 dataset with parameters


Quercus microspectroscopy ZnSe_DCNN 
a4 dataset with parameters


Quercus microspectroscopy PEP
PEP dataset after atmospheric compensation


Quercus microspectroscopy PEP fringecorrected
a6 dataset


Quercus microspectroscopy PEP_paraffin1sp
a7 dataset


Quercus microspectroscopy PEP_Fringe_paraffin1sp
a8 dataset


Quercus microspectroscopy PEP_paraffinPCA
a9 dataset


Quercus microspectroscopy PEP_Fringe_paraffinPCA
a10 dataset




Code for data processing:



https://github.com/eirikama/sirisp/tree/74c271e2fdc0643ce74d687d4be67562ccc7b481

https://github.com/BioSpecNorway/biospectools/releases/tag/v0.4.0
DOI: 10.5281/zenodo.14917826
title: Dataset for the paper "Analytical and experimental solutions for Fourier transform infrared microspectroscopy measurements of microparticles: A case study on Quercus pollen"

description: Comparative study on 12 different analytical and experimental approaches for FTIR measurements of microparticles. The approaches were demonstrated on classification and chemical characterisation of pollen of four oak species.

File description:




Quercus microspectroscopy ZnSe
ZnSe dataset after atmospheric compensation


Quercus microspectroscopy ZnSe ME_EMSC
a3 dataset with parameters


Quercus microspectroscopy ZnSe_DCNN 
a4 dataset with parameters


Quercus microspectroscopy PEP
PEP dataset after atmospheric compensation


Quercus microspectroscopy PEP fringecorrected
a6 dataset


Quercus microspectroscopy PEP_paraffin1sp
a7 dataset


Quercus microspectroscopy PEP_Fringe_paraffin1sp
a8 dataset


Quercus microspectroscopy PEP_paraffinPCA
a9 dataset


Quercus microspectroscopy PEP_Fringe_paraffinPCA
a10 dataset




Code for data processing:



https://github.com/eirikama/sirisp/tree/74c271e2fdc0643ce74d687d4be67562ccc7b481

https://github.com/BioSpecNorway/biospectools/releases/tag/v0.4.0
DOI: 10.15468/dl.yr8xbc
title: Occurrence Download

description: A dataset containing 63 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is VNM.43_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Insecta"
 ]
}

 The dataset includes 63 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004982-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/2mkg-nt67
title: Specular Highlight Semantic Dataset

description: The Semantic Highlight Mask Dataset holds significant importance in the field of computer vision, particularly for tasks such as object detection, image segmentation, and scene understanding. This paper proposes a novel Semantic Highlight Mask Dataset designed to support high-precision semantic segmentation and emphasize key regions in images. The dataset encompasses a diverse range of image categories, with finely annotated highlight masks assigning semantic labels and saliency weights to each pixel. The dataset construction process integrates automated algorithms with manual validation to ensure annotation quality and consistency. Experimental results demonstrate that this dataset significantly enhances the performance of deep learning models in semantic segmentation tasks, particularly when handling complex backgrounds and fine-grained targets. We further explore the dataset’s potential in real-world applications, such as autonomous driving, medical image analysis, and augmented reality, providing valuable resources and benchmarks for future research.
DOI: 10.21227/v1a0-jd82
title: Specular Highlight Semantic Dataset

description: The Semantic Highlight Mask Dataset holds significant importance in the field of computer vision, particularly for tasks such as object detection, image segmentation, and scene understanding. This paper proposes a novel Semantic Highlight Mask Dataset designed to support high-precision semantic segmentation and emphasize key regions in images. The dataset encompasses a diverse range of image categories, with finely annotated highlight masks assigning semantic labels and saliency weights to each pixel. The dataset construction process integrates automated algorithms with manual validation to ensure annotation quality and consistency. Experimental results demonstrate that this dataset significantly enhances the performance of deep learning models in semantic segmentation tasks, particularly when handling complex backgrounds and fine-grained targets. We further explore the dataset’s potential in real-world applications, such as autonomous driving, medical image analysis, and augmented reality, providing valuable resources and benchmarks for future research.
DOI: 10.5281/zenodo.14933947
title: Hybridization-Encoded DNA Tags with Paper-Based Readout for Anti-Forgery Raw Material Tracking

description: This repository includes source data files, the related encoding and decoding software, and raw images used for testing the software presented in the work 'Hybridization-Encoded DNA Tags with Paper-Based Readout for Anti-Forgery Raw Material Tracking' accepted for publication at Nature Communications.
DOI: 10.5281/zenodo.14933948
title: Hybridization-Encoded DNA Tags with Paper-Based Readout for Anti-Forgery Raw Material Tracking

description: This repository includes source data files, the related encoding and decoding software, and raw images used for testing the software presented in the work 'Hybridization-Encoded DNA Tags with Paper-Based Readout for Anti-Forgery Raw Material Tracking' accepted for publication at Nature Communications.
DOI: 10.25442/hku.28623464
title: Supporting data for "First-Generation Undergraduate Students’ Well-being and Academic Readiness in the case of Hong Kong"

description: This qualitative longitudinal study aims to explore the well-being and academic readiness of first-generation undergraduates over time. The study involves two rounds of 1-hour Zoom interviews, conducted at two different time points (e.g., first and second semester of an academic year). The sampling procedure will ensure a diverse and representative sample of first-generation students while maintaining feasibility and ethical considerations.Target population is defined with an inclusion criteria: First-generation undergraduate students (defined as students whose parents/guardians did not complete a bachelor’s degree); and enrolled full-time in a degree program at the participating institution(s). Exclusion criteria includes students who do not identify as first-generation.Sampling strategy is by purposive sampling and snowball sampling. Purposive sampling ensures a diverse sample that reflects the heterogeneity of first-generation students in terms of, gender, ethnicity/race, socioeconomic background, academic major, year of study (e.g., freshmen, sophomores), and student status as local, mainland, or international. Snowball sampling invites participants to refer other first-generation students who meet the inclusion criteria. This will help expand the sample and include students who may not be easily reachable through institutional channels.Since the population of first-generation students is minor in a university, sample size was aimed at 30 participants for the interviews and fifty students were recruited to join round 1 interviews. This sample size is manageable for in-depth qualitative analysis and allows for attrition in the second round. Round 1 interviews were conducted at the beginning of the academic year (e.g., September–October). Round 2 interviews were conducted at the end of the academic year (e.g., April–May). A 10% attrition rate was anticipated between the two rounds due to scheduling conflicts or withdrawal from the study. Forty-five students from round 1 continued the interview in round 2. The data files are transcripts form both round 1 and round 2 interviews.
DOI: 10.15468/dl.gpg2vx
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is CHN.19.1_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Nitraria sphaerocarpa Maxim."
 ]
}

 The dataset includes 9 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004984-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c7skkm
title: Occurrence Download

description: A dataset containing 209 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pterocarpus santalinus L.f."
 ]
}

 The dataset includes 209 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004986-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1175348.10fu
title: SH1175348.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1175351.10fu
title: SH1175351.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1175352.10fu
title: SH1175352.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1175357.10fu
title: SH1175357.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.