cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.an67e9
title: Occurrence Download

description: A dataset containing 56635572 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is one of (Benin, Congo, Democratic Republic of the, South Africa, Kenya, Tanzania, United Republic of, Uganda, Madagascar, Namibia, Morocco, Zimbabwe)"
 ]
}

 The dataset includes 56635572 records from 4029 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023063-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8465wg
title: Occurrence Download

description: A dataset containing 17268 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Pardosa palustris (Linnaeus, 1758), Phylloneta impressa (L.Koch, 1881))",
 "Geometry POLYGON((-9.36035 44.71551,-10.59082 42.81152,-11.82129 36.80928,-7.69043 36.17336,-3.38379 35.88905,3.12012 38.13456,9.18457 38.20366,11.7334 37.16032,12.78809 34.74161,17.09473 33.79741, 23.15918 34.23451,30.10254 32.62087,33.2666 32.99024,35.72754 35.03,33.00293 35.53223,28.69629 35.17381,28.34473 36.31513,25.7959 37.50973,24.91699 39.90974,28.78418 40.6473,30.71777 41.17865,36.87012 45.213,38.18848 46.98025,38.71582 47.7541,39.94629 47.93107,40.20996 49.61071,37.0459 50.34546,35.72754 50.40152,33.79395 52.26816,31.68457 51.94426,31.4209 53.27835,32.56348 53.43572,30.98145 54.77535,30.89355 55.52863,28.43262 56.02295,27.20215 57.65716,27.37793 58.30949,27.90527 59.17593,29.66309 59.88894,27.6416 60.58697,31.50879 62.75473,28.78418 69.83962,30.89355 70.28912,29.22363 71.55274,21.75293 71.30079,14.28223 70.14036,11.38184 67.47492,4.87793 63.66576,-5.53711 71.52491,-10.63477 71.69129,-21.5332 67.74276,-28.56445 65.36684,-24.3457 61.35461,-9.36035 44.71551))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2021"
 ]
}

 The dataset includes 17268 records from 208 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0170023-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.usxg3p
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is China",
 "Country is Hong Kong",
 "Country is Macao",
 "Country is Chinese Taipei"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Cephalotaxus hainanensis H.L.Li",
 "Year 1971-2022"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0255717-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-50158
title: Redemption tidings, vol. 31, nos. 1-26, 7 Jan. - 23 Dec. 1955

description: [description english/roman)] Issued fortnightly (every two weeks). "A full gospel magazine" -- subtitle.
DOI: 10.15468/dl.cdbmu8
title: Occurrence Download

description: A dataset containing 706 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Costa Rica",
 "TaxonKey is Atelopus varius (Lichtenstein & Martens, 1856)"
 ]
}

 The dataset includes 706 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022290-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-50172
title: Redemption tidings, vol. 31, nos. 1-26, 7 Jan. - 23 Dec. 1955

description: [description english/roman)] Issued fortnightly (every two weeks). "A full gospel magazine" -- subtitle.
DOI: 10.15468/dl.vewr4m
title: Occurrence Download

description: A dataset containing 135 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hottentotta tamulus (Fabricius, 1798)"
 ]
}

 The dataset includes 135 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0170028-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g6n5xe
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nannocharax hollyi Fowler, 1936"
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0343827-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tu2yuy
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-90.01699 21.14736,-89.73758 21.23074,-89.7631 21.28549,-89.81833 21.28549,-90.03767 21.18423,-90.01699 21.14736))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Cynoscion arenarius Ginsburg, 1930"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011676-230918134249559/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-50160
title: Redemption tidings, vol. 31, nos. 1-26, 7 Jan. - 23 Dec. 1955

description: [description english/roman)] Issued fortnightly (every two weeks). "A full gospel magazine" -- subtitle.
DOI: 10.15468/dl.58xcnd
title: Occurrence Download

description: A dataset containing 514014 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Glechoma hirsuta Waldst. & Kit., Glechoma hederacea L.)",
 "Geometry POLYGON((-9.36035 44.71551,-10.59082 42.81152,-11.82129 36.80928,-7.69043 36.17336,-3.38379 35.88905,3.12012 38.13456,9.18457 38.20366,11.7334 37.16032,12.78809 34.74161,17.09473 33.79741, 23.15918 34.23451,30.10254 32.62087,33.2666 32.99024,35.72754 35.03,33.00293 35.53223,28.69629 35.17381,28.34473 36.31513,25.7959 37.50973,24.91699 39.90974,28.78418 40.6473,30.71777 41.17865,36.87012 45.213,38.18848 46.98025,38.71582 47.7541,39.94629 47.93107,40.20996 49.61071,37.0459 50.34546,35.72754 50.40152,33.79395 52.26816,31.68457 51.94426,31.4209 53.27835,32.56348 53.43572,30.98145 54.77535,30.89355 55.52863,28.43262 56.02295,27.20215 57.65716,27.37793 58.30949,27.90527 59.17593,29.66309 59.88894,27.6416 60.58697,31.50879 62.75473,28.78418 69.83962,30.89355 70.28912,29.22363 71.55274,21.75293 71.30079,14.28223 70.14036,11.38184 67.47492,4.87793 63.66576,-5.53711 71.52491,-10.63477 71.69129,-21.5332 67.74276,-28.56445 65.36684,-24.3457 61.35461,-9.36035 44.71551))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2021"
 ]
}

 The dataset includes 514014 records from 852 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0162215-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8m8hb7
title: Occurrence Download

description: A dataset containing 1152990 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters 0.0-10000.0",
 {
 "or" : [
 "Issue is Coordinate rounded",
 "Issue is Geodetic datum assumed WGS84",
 "Issue is Coordinate reprojected",
 "Issue is Footprint WKT invalid",
 "Issue is Country coordinate mismatch",
 "Issue is Country invalid",
 "Issue is Country mismatch",
 "Issue is Continent invalid",
 "Issue is Continent derived from coordinates",
 "Issue is Recorded date mismatch",
 "Issue is Recorded date invalid",
 "Issue is Recorded date unlikely",
 "Issue is Taxon match fuzzy",
 "Issue is Taxon match higher rank",
 "Issue is Elevation min max swapped",
 "Issue is Elevation non numeric",
 "Issue is Modified date unlikely",
 "Issue is Identified date unlikely",
 "Issue is Basis of record invalid",
 "Issue is Type status invalid",
 "Issue is Multimedia date invalid",
 "Issue is Multimedia URI invalid",
 "Issue is References URI invalid",
 "Issue is Individual count invalid",
 "Issue is Individual count conflicts with occurrence status",
 "Issue is Occurrence status inferred from individual count",
 "Issue is Occurrence status unparsable",
 "Issue is ccurrence status inferred from basis of record",
 "Issue is Ambiguous institution",
 "Issue is Ambiguous collection",
 "Issue is Institution match none",
 "Issue is Collection match none",
 "Issue is Collection match fuzzy",
 "Issue is Institution match fuzzy",
 "Issue is Institution collection mismatch",
 "Issue is Different owner institution"
 ]
 },
 "TaxonKey is Prunus L."
 ]
}

 The dataset includes 1152990 records from 952 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0343829-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13144/lil-1519
title: Enquête Revenus Fiscaux et Sociaux - 2019

description: L'enquête Revenus Fiscaux et Sociaux (ERFS) de 2019 consiste en un appariement statistique du fichier de l'enquête Emploi en continu (données du 4ème trimestre de l'année 2019) avec les fichiers des déclarations fiscales de l'année 2019 et les données sur les prestations sociales et familiales perçues au cours de l'année 2019. L'enquête ERFS a pour objectif de mesurer un revenu économique : ce dont les ménages ont disposé au cours d'une période pour consommer ou épargner. Elle permet de produire des statistiques et de réaliser des études en matière de revenus, de niveau de vie, de pauvreté monétaire des ménages résidant en France métropolitaine et ce, à l'aide d'indicateurs usuels d'analyse de la distribution des revenus (nombres, quartiles, déciles, moyenne, médiane, etc.) et d'indicateurs de structure et de composition des ménages. C'est la source de référence pour les analyses sur les inégalités et la pauvreté (au niveau national en France métropolitaine). A partir de l'ERFS 2013, un changement méthodologique a été introduit pour calculer l'impôt sur le revenu de l'année N-1 payé en N dans le calcul du revenu disponible du ménage. Outre ce changement de millésime d'impôts, dès l'ERFS 2013, les évolutions introduites dans l'enquête Emploi en continu de 2013 (qui constitue le socle de l'ERFS) ont été prises en compte, visant notamment à mieux cerner certaines populations difficiles à appréhender par l'enquête. Deux changements fiscaux importants ont également été introduits : d'une part, les majorations de pensions de retraite pour avoir élevé trois enfants ou plus ; d'autre part, l'avantage en nature constitué par la participation des employeurs aux contrats collectifs de complémentaire santé est maintenant intégré dans les salaires déclarés à l'administration.Et à partir de l’ERFS 2015, la méthode d’imputation a été revue pour intégrer les améliorations de l’enquête Patrimoine 2014-2015. Les montants d’actifs déclarés par les ménages, désormais recueillis directement et non plus sous forme d’intervalles, sont plus fiables. Cela engendre une estimation des revenus financiers plus proche de la réalité.En 2019, les évolutions des revenus des ménages sont étroitement liées à divers changements dans la législation fiscale, en particulier la mise en place de la prime exceptionnelle de pouvoir d’achat et de l’exonération des heures supplémentaires. Ces mesures sont entrées en vigueur en début d’année en réponse au mouvement des Gilets Jaunes. Des imputations statistiques des revenus de la PEPA et des heures supplémentaires exonérées ont ainsi été introduites dans l’ERFS 2019. Dans les deux cas, l’imputation a été réalisée à l’aide d’un tirage stratifié des éligibles, s’appuyant sur des données issues de sources externes (taux de perception et montant moyen perçu), et sous contrainte d’une cible totale.
DOI: 10.15468/dl.sfcm8p
title: Occurrence Download

description: A dataset listing the 32 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-144.56193 60.66553,-146.18834 61.21102,-147.7373 61.2576,-149.13137 60.87354,-150.35118 61.15502,-151.43546 60.8075,-152.30675 59.7322,-151.97759 58.88231,-147.44687 59.74195,-144.91045 60.21661,-144.56193 60.66553))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Apidae"
 ]
}

 The dataset's 32 records were derived from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003005-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.byyc7x
title: Occurrence Download

description: A dataset containing 30181 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pandanus",
 "HasCoordinate is true"
 ]
}

 The dataset includes 30181 records from 283 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055692-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16925323
title: Turn Your Face Into An Attack Surface: Screen Attack Using Facial Reflections in Video Conferencing

description: 1. Introduction

 

This project provides a comprehensive pipeline for processing and analyzing subject screen interaction data, divided into two main components:





Face Segmentation and Reconstruction: This component processes raw screen recording videos. It first segments the video into meaningful clips and extracts frames containing the subject's face. Subsequently, it applies super-resolution techniques (CAMixerSR) to enhance the quality of the extracted facial images.




Screen Content Inference: This component uses a deep learning model to classify the content displayed on the screen. It includes a complete workflow for training a custom two-tier classification model and for testing new data using a suite of provided pre-trained weights.



 

2. Key Features

 





Two-Tier Classification Model: A novel two-stage approach for screen content analysis. A Tier-1 model classifies content into broad categories (e.g., Web Applications, Office Software), and specialized Tier-2 models perform fine-grained classification within each category.




Pre-trained Models: A full set of pre-trained models is provided, allowing for immediate testing and inference on new data without the need for retraining.




Complete Pipeline: Includes scripts for every step of the workflow, from raw video processing to final classification result generation.



 

3. Package Contents

 

This artifact contains the following components:





/code/: All Python scripts for both the "Face Segmentation" and "Screen Content Inference" pipelines. Note on Third-Party Code: This artifact includes the CAMixerSR module (referenced as [29] in the paper), which is an open-source project. The original README and license files for CAMixerSR are retained within its corresponding directory to ensure proper attribution.







/data/: Sample data to demonstrate the functionality of the scripts.




/results/pretrain_models/: The complete set of pre-trained Tier-1 and Tier-2 model weights.




requirements.txt: A list of all Python library dependencies, which can be installed using pip.




README.md: The full instruction file with detailed, step-by-step guidance on installation and usage.



 

4. Basic Usage

 





Create a Python virtual environment and install the dependencies using pip install -r requirements.txt. 




Follow the detailed instructions in the README.md file to run the different parts of the pipeline.


The repository includes a root README.md for the main pipeline (face segmentation & training). IMPORTANT: The super-resolution module (CAMixerSR) requires a SEPARATE ENVIRONMENT due to distinct dependencies. Please explicitly refer to the README.md inside the CAMixerSR folder for its specific setup instructions.
DOI: 10.5281/zenodo.16925324
title: Turn Your Face Into An Attack Surface: Screen Attack Using Facial Reflections in Video Conferencing

description: 1. Introduction

 

This project provides a comprehensive pipeline for processing and analyzing subject screen interaction data, divided into two main components:





Face Segmentation and Reconstruction: This component processes raw screen recording videos. It first segments the video into meaningful clips and extracts frames containing the subject's face. Subsequently, it applies super-resolution techniques (CAMixerSR) to enhance the quality of the extracted facial images.




Screen Content Inference: This component uses a deep learning model to classify the content displayed on the screen. It includes a complete workflow for training a custom two-tier classification model and for testing new data using a suite of provided pre-trained weights.



 

2. Key Features

 





Two-Tier Classification Model: A novel two-stage approach for screen content analysis. A Tier-1 model classifies content into broad categories (e.g., Web Applications, Office Software), and specialized Tier-2 models perform fine-grained classification within each category.




Pre-trained Models: A full set of pre-trained models is provided, allowing for immediate testing and inference on new data without the need for retraining.




Complete Pipeline: Includes scripts for every step of the workflow, from raw video processing to final classification result generation.



 

3. Package Contents

 

This artifact contains the following components:





/code/: All Python scripts for both the "Face Segmentation" and "Screen Content Inference" pipelines. Note on Third-Party Code: This artifact includes the CAMixerSR module (referenced as [29] in the paper), which is an open-source project. The original README and license files for CAMixerSR are retained within its corresponding directory to ensure proper attribution.







/data/: Sample data to demonstrate the functionality of the scripts.




/results/pretrain_models/: The complete set of pre-trained Tier-1 and Tier-2 model weights.




requirements.txt: A list of all Python library dependencies, which can be installed using pip.




README.md: The full instruction file with detailed, step-by-step guidance on installation and usage.



 

4. Basic Usage

 





Create a Python virtual environment and install the dependencies using pip install -r requirements.txt. 




Follow the detailed instructions in the README.md file to run the different parts of the pipeline.


The repository includes a root README.md for the main pipeline (face segmentation & training). IMPORTANT: The super-resolution module (CAMixerSR) requires a SEPARATE ENVIRONMENT due to distinct dependencies. Please explicitly refer to the README.md inside the CAMixerSR folder for its specific setup instructions.
DOI: 10.17026/ar/xhfrqc
title: PAN-00174376 - iron nail general

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00174376&lt;/p&gt;
description: Modified: 2025-05-20T17:13:47+02:00
DOI: 10.7910/dvn/4gn6qd
title: PDB - 4G3Y: Crystal structure of TNFalpha in complex with Infliximab Fab fragment (310K, 37°C, 200 ns) - random seed #1

description: 4G3Y: Crystal structure of TNFalpha in complex with Infliximab Fab fragment (310K, 37°C, 200 ns) - random seed #1. Contains PDBs obtained every 50 ns and free binding energy calculations.
DOI: 10.15468/dl.pqetdx
title: Occurrence Download

description: A dataset containing 9366 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Thalassoma amblycephalum"
 ]
}

 The dataset includes 9366 records from 126 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055693-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5519/qd.d6njug65
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2826 records)
DOI: 10.17026/ar/poiq72
title: PAN-00173357 - leather pump valve

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00173357&lt;/p&gt;
description: Modified: 2025-09-17T09:33:05+02:00
DOI: 10.15468/dl.jjvxsf
title: Occurrence Download

description: A dataset containing 368083 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tracheophyta",
 "DecimalLatitude is greater than or equal to -40.914722",
 "DecimalLatitude is less than or equal to -35.914722",
 "DecimalLongitude is greater than or equal to 170.0",
 "DecimalLongitude is less than or equal to 175.0",
 "HasCoordinate is TRUE",
 "HasGeospatialIssue is FALSE"
 ]
}

 The dataset includes 368083 records from 115 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055688-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/vne2ip
title: PAN-00062968 - Early Medieval rectangular mount (belt)

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00062968&lt;/p&gt;
description: Modified: 2025-10-27T13:56:35+01:00
DOI: 10.6084/m9.figshare.30834329.v2
title: smBGCs of <i>Streptomyces californicus </i>ADR1

description: <b>Complete genome sequence of </b><b><i>Streptomyces californicus</i></b><b> ADR1 isolated from </b><b><i>Datura metel</i></b>
DOI: 10.21334/npolar.2023.71e9f1e8
title: In situ spectral beam attenuation and absorption coefficients of sea water in the northern Barents Sea: Nansen Legacy Cruise Q2 (2021704)
DOI: 10.17026/dans-z4y-qax3
title: PAN-00121128 - terra sigillata plate type Drag. 18

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00121128&lt;/p&gt;
description: Modified: 2025-09-18T11:29:28+02:00
DOI: 10.15468/dl.f7q2m2
title: Occurrence Download

description: A dataset containing 5199 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Mesoplodon bidens"
 ]
}

 The dataset includes 5199 records from 90 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055695-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14189186
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.21334/npolar.2023.38808452
title: In situ spectral beam attenuation and absorption coefficients of sea water in the northern Barents Sea: Nansen Legacy Cruise Q1 (2021703)
DOI: 10.21227/0bkb-dy37
title: "Your Complete Guide to CSM Training and Certification in India"

description: "In today\u2019s fast-changing business environment, organizations across industries are adopting Agile practices to improve efficiency, speed, and teamwork."
DOI: 10.17026/ar/isrubu
title: PAN-00100149 - closed band-shaped finger ring

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00100149&lt;/p&gt;
description: Modified: 2025-05-26T16:55:25+02:00
DOI: 10.7910/dvn/pim6iy
title: PDB: 2XWT, Crystal structure of TSH receptor in complex with human mAb autoantibody K1-70 (312K, 39°C, 400 ns): random seed #2

description: 2XWT, Crystal structure of TSH receptor in complex with human mAb autoantibody K1-70 (312K, 39°C, 400 ns): random seed #2. Contains PDBs obtained every 50 ns and free binding energy calculations.