cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25824/redu/uqonx1
title: Entrevistas sobre roteiros audiovisuais para a tese "A cultura do roteiro no cinema brasileiro: uma perspectiva"

description: Conjuntos de dados associados a cinco entrevistas realizadas em ambiente virtual com cinco roteiristas brasileiros para a tese "A cultura do roteiro no cinema brasileiro: uma perspectiva", sobre temas como ensino do roteiro no Brasil, relações do roteiro com orçamento e autoria de filmes e séries, e cultura do roteiro no audiovisual brasileiro, especialmente no cinema de longa-metragem
DOI: 10.7282/t3gx49m2
title: Innovation in Academic Libraries - Interview F

description: Through a series of structured interviews, university librarians at six institutions provided their perspectives on innovation in academic libraries. The literature on leadership styles and organizational change provides insight into the roles of these leaders in the innovation process. Leadership is cited by many researchers as being a critical factor for organizations to innovate. In this study, university librarians’ perspectives reveal a commitment to innovation, some distinctively non-traditional innovations, and a concern for how to encourage risk-taking behavior. The study also seeks further insight into the innovation process by interpreting the interview data within a larger theoretical context. Although leadership and management can foster innovation in a library, researchers have reported other factors that can influence the ability to innovate, including organizational aspects – size and complexity – and environmental factors. Beyond the organizational aspects, the individual and the norms of the profession create a framework with certain boundaries, some of which may impact the ability to innovate.
DOI: 10.15156/bio/sh0191233.10fu
title: SH0191233.10FU

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/sh0191234.10fu
title: SH0191234.10FU

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/sh0191235.10fu
title: SH0191235.10FU

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/sh0191236.10fu
title: SH0191236.10FU

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: 10.15468/dl.9y6w3x
title: Occurrence Download

description: A dataset containing 381 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is LIFE Fluvial - Invasive Alien Plants Database - Eo River (Spain)"
 ]
}

 The dataset includes 381 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018006-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jtsuqz
title: Occurrence Download

description: A dataset containing 14318 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ondatra zibethicus (Linnaeus, 1766)",
 "Country is France",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 14318 records from 417 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018008-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14174408
title: A Big Data Analysis Algorithm for Massive Sensor Medical Images

description: The smart sensor based big data analysis recommendation system has significant privacy and security concerns when it comes to using sensor medical images for suggestions and monitoring. The danger of security breaches and unauthorized access which might lead to identity theft and privacy violations increases when sending and storing sensitive medical data on the cloud. Insufficient or erroneous patient data can lead to poor treatment decisions, misdiagnoses, and unreliable recommendations. By creating an anomaly detection system based on machine learning specifically for medical image and providing timely treatments and notifications, our effort will improve patient care and well-being. We infer the feature extraction, feature selection, attack detection, and data collection data processing procedures in order to anticipate the anomaly in patient data. We transfer the data, take care of any missing values, and sanitize it using the data pre-processing mechanism. We employed the RFE and DPCA algorithms for feature selection and extraction, respectively. In addition, we applied the AGRNN approach to identify abnormalities. Data arrival rate, resource consumption, propagation delay, transaction epoch, true positive rate, false alarm rate, and RMSE are some of the metrics used to evaluate the proposed task.
DOI: 10.5281/zenodo.14174409
title: A Big Data Analysis Algorithm for Massive Sensor Medical Images

description: The smart sensor based big data analysis recommendation system has significant privacy and security concerns when it comes to using sensor medical images for suggestions and monitoring. The danger of security breaches and unauthorized access which might lead to identity theft and privacy violations increases when sending and storing sensitive medical data on the cloud. Insufficient or erroneous patient data can lead to poor treatment decisions, misdiagnoses, and unreliable recommendations. By creating an anomaly detection system based on machine learning specifically for medical image and providing timely treatments and notifications, our effort will improve patient care and well-being. We infer the feature extraction, feature selection, attack detection, and data collection data processing procedures in order to anticipate the anomaly in patient data. We transfer the data, take care of any missing values, and sanitize it using the data pre-processing mechanism. We employed the RFE and DPCA algorithms for feature selection and extraction, respectively. In addition, we applied the AGRNN approach to identify abnormalities. Data arrival rate, resource consumption, propagation delay, transaction epoch, true positive rate, false alarm rate, and RMSE are some of the metrics used to evaluate the proposed task.
DOI: 10.15468/dl.ch29mx
title: Occurrence Download

description: A dataset containing 2875545 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "DatasetKey is EOD – eBird Observation Dataset",
 "Year 1999-2024"
 ]
}

 The dataset includes 2875545 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018007-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cw7fpn
title: Occurrence Download

description: A dataset containing 5695 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Calliandra houstoniana (Mill.) Standl."
 ]
}

 The dataset includes 5695 records from 150 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018010-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.27791184.v1
title: Dataset for Nature Communications

description: This is dataset for my publications of Nature Communications.
DOI: 10.6084/m9.figshare.25703196
title: The Pacific oyster Crassostrea gigas dataset

description: This study investigates genetic architecture underlying heterotic growth traits of yearling Pacific oysters <i>Crassostrea gigas.</i>
DOI: 10.6084/m9.figshare.25703196.v1
title: The Pacific oyster Crassostrea gigas dataset

description: This study investigates genetic architecture underlying heterotic growth traits of yearling Pacific oysters <i>Crassostrea gigas.</i>
DOI: 10.15468/dl.xvhn5y
title: Occurrence Download

description: A dataset containing 2593837 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is 2023 Contributions of Plant Specimen Data inside China"
 ]
}

 The dataset includes 2593837 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018009-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cszczy
title: Occurrence Download

description: A dataset containing 225 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Calliandra calothyrsus Meisn."
 ]
}

 The dataset includes 225 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018011-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4c8zra
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Aspidistra bogneri Tillich)"
 ]
}

 The dataset includes 16 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018012-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qs3qwy
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-74.25139 11.11565,-74.09545 11.11565,-74.09545 11.21908,-74.25139 11.21908,-74.25139 11.11565))",
 "TaxonKey is Elasmobranchii"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018013-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jphzqj
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Aspidistra bogneri Tillich)"
 ]
}

 The dataset includes 16 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018016-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26409736
title: The codes and data for "Lane Extraction from Trajectories at Road Intersections Based on Graph Transformer Network"

description: <b>Lane Extraction from Trajectories at Road Intersections Based on Graph Transformer Network</b><br>The data and codes that support the findings of our study "Lane Extraction from Trajectories at Road Intersections Based on Graph Transformer Network".DataThe following data are provided in the 'Data' folder:`traj.csv`: The trajectory data used for model training and testing. Each point consist of 'trajectory_id', 'timestamp', 'longitude(x)', 'latitude(y)', 'azimuth', and 'inter_id' attributes.`trueLane.csv`: The ground truth lanes for model training and testing. Each lane includes 'geometry' and 'inter_id' attributes.CodesThis repository contains the following Python codes：`data_processing.py`: Contains the implementation of data processing and feature extraction. It includes functions related to trajectory data processing, trajectory feature extraction, and the calculation of graph node and edge features.`run_process.py`: Contains the code for executing data processing and feature extraction.`GTN.py`: Contains the implementation of Graph Transformer Network for lane extraction. It includes classes and functions related to the architecture of the Graph Transformer Network and the set-based lane extraction loss calculation.`train_GTN.py`: Contains the code for Graph Transformer Network model training.`test_GTN.py`: Contains the code for model inference and lane extraction.Running the Code<b>Data processing and feature extraction</b><pre>python run_process.py</pre>This step processes trajectory data, extracts graph node features and edge features, and saves them as CSV files in the `processed_data` folder.<b>Model training</b><code>python train_GTN.py</code>This step trains the GTN model. The trained model is saved in `model/GTN_model.pth`.<b>Model inference and lane extraction</b><code>python test_process.py</code>This step performs model inference and extracts lanes for the test intersections. The lane extraction result is saved in `result/predicted_lane.csv`.RequirementsThe codes use the following dependencies with Python 3.11networkx==3.2.1pytorch==2.0.1torch-geometric==2.5.3geopandas==1.0.1<br>
DOI: 10.15468/dl.6ynzdm
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-74.25139 11.11565,-74.09545 11.11565,-74.09545 11.21908,-74.25139 11.21908,-74.25139 11.11565))",
 "TaxonKey is one of (Oreochromis niloticus (Linnaeus, 1758), Hoplias malabaricus (Bloch, 1794), Pimelodus clarias Geoffroy Saint-Hilaire, 1809, Prochilodus magdalenae Steindachner, 1879, Ctenolucius Gill, 1861, Pseudoplatystoma magdaleniatum Buitrago-Suárez & Burr, 2007, Elasmobranchii)"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018018-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p9kf6a
title: Occurrence Download

description: A dataset containing 59753 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Melanerpes chrysogenys (Vigors, 1839)"
 ]
}

 The dataset includes 59753 records from 69 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018017-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gu696n
title: Occurrence Download

description: A dataset containing 2229 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Some findings of common, protected, and invasive species in Ukraine recorded in 2016-2023"
 ]
}

 The dataset includes 2229 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018019-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fq94e9
title: Occurrence Download

description: A dataset containing 142 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is France",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Pardosa saltans Töpfer-Hofmann, 2000",
 "Year 2016-2016"
 ]
}

 The dataset includes 142 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018020-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nv5s3j
title: Occurrence Download

description: A dataset containing 32723 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Melanerpes pygmaeus (Ridgway, 1885)"
 ]
}

 The dataset includes 32723 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018021-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2cbzwa
title: Occurrence Download

description: A dataset containing 11475 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Canada",
 "DatasetKey is one of (EOD – eBird Observation Dataset, iNaturalist Research-grade Observations)",
 "TaxonKey is Charadrius melodus Ord, 1824",
 "Year 2012-2024"
 ]
}

 The dataset includes 11475 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018022-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rm8ed4
title: Occurrence Download

description: A dataset containing 11475 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Canada",
 "DatasetKey is one of (EOD – eBird Observation Dataset, iNaturalist Research-grade Observations)",
 "TaxonKey is Charadrius melodus Ord, 1824",
 "Year 2012-2024"
 ]
}

 The dataset includes 11475 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018023-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc2l5mgw
title: CCDC 2388688: Experimental Crystal Structure Determination

description: Related Article: Weikang Wu, Thayalan Rajeshkumar, Dongjing Hong, Shan Zhu, Zeming Huang, Fuxiang Chai, Weigang Wang, Qingbing Yuan, Yun Wei, Zuowei Xie, Laurent Maron, * and Shaowu Wang*|2024|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: 10.15468/dl.khesjq
title: Occurrence Download

description: A dataset containing 215 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-74.25139 11.11565,-74.09545 11.11565,-74.09545 11.21908,-74.25139 11.21908,-74.25139 11.11565))",
 "TaxonKey is Testudines"
 ]
}

 The dataset includes 215 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018024-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d7q62r
title: Occurrence Download

description: A dataset containing 2557 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Occurrences from the Alien CSI Bioblitz: citizen science to engage society in invasive species monitoring"
 ]
}

 The dataset includes 2557 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018025-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.476sdc
title: Occurrence Download

description: A dataset containing 942 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Calliandra californica Benth."
 ]
}

 The dataset includes 942 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018026-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.