cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24380197.v1
title: Supplementary material for "How Perceptions of Voter Control Affect Politicians' Evaluations of Expertise in the News: A Survey Experiment on the Role of Accountability Beliefs"

description: The material contains an Appendix with additionnal information with regard to the article. The author also provides a dataset and an R-Syntax for replication of the main results.
DOI: 10.5281/zenodo.10022632
title: ERA5-Land selected indicators daily aggregates for the Latin America region, 1989

description: This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 1989.
Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.
For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.
Those files were created using the KrigR package.
DOI: 10.5281/zenodo.10022631
title: ERA5-Land selected indicators daily aggregates for the Latin America region, 1989

description: This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 1989.
Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.
For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.
Those files were created using the KrigR package.
DOI: 10.6084/m9.figshare.24381577
title: greenhouse fruit removal metadata

description: This is the metadata describing the data set used in the greenhouse fruit removal experiment.
DOI: 10.6084/m9.figshare.24381577.v1
title: greenhouse fruit removal metadata

description: This is the metadata describing the data set used in the greenhouse fruit removal experiment.
DOI: 10.5281/zenodo.10022641
title: ERA5-Land selected indicators daily aggregates for the Latin America region, 1990

description: This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 1990.
Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.
For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.
Those files were created using the KrigR package.
DOI: 10.5281/zenodo.10022640
title: ERA5-Land selected indicators daily aggregates for the Latin America region, 1990

description: This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 1990.
Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.
For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.
Those files were created using the KrigR package.
DOI: 10.5523/bris.bi3umjin511z28gg91upzvcxx
title: Surface-Area-To-Volume Ratio Determines Surface Tensions in Microscopic, Surfactant-Containing Droplets

description: This data set provides the data underlying the figures in our publication "Surface-Area-To-Volume Ratio Determines Surface Tensions in Microscopic, Surfactant-Containing Droplets", ACS Central Science, 2023.
DOI: 10.5281/zenodo.10019328
title: Extended_CLEF_IP_2011_Dataset

description: Extended CLEF-IP 2011 Dataset: We use the 2011 benchmark dataset of CLEF-IP [23] for visualization type classification. However, it does not cover block and
circuit diagrams, which are an important type of visualization frequently used in patents. We added this category as a tenth class and collected images by querying EPO's publication server (https://data.epo.org/publication-server/). Finally, we manually annotated images depicting block and circuit diagrams. The dataset statistics are provided in Table 1 (left).
Paper title: Classification of Visualization Types and Perspectives in Patents
Paper link: https://link.springer.com/chapter/10.1007/978-3-031-43849-3_16 
Git repository link: https://github.com/TIBHannover/PatentImageClassification
 
Cite as:
Ghauri, J.A., Müller-Budack, E., Ewerth, R. (2023). Classification of Visualization Types and Perspectives in Patents. In: Alonso, O., Cousijn, H., Silvello, G., Marrero, M., Teixeira Lopes, C., Marchesin, S. (eds) Linking Theory and Practice of Digital Libraries. TPDL 2023. Lecture Notes in Computer Science, vol 14241. Springer, Cham. https://doi.org/10.1007/978-3-031-43849-3_16
OR
 
@InProceedings{10.1007/978-3-031-43849-3_16,
author="Ghauri, Junaid Ahmed
and M{\"u}ller-Budack, Eric
and Ewerth, Ralph",
editor="Alonso, Omar
and Cousijn, Helena
and Silvello, Gianmaria
and Marrero, M{\'o}nica
and Teixeira Lopes, Carla
and Marchesin, Stefano",
title="Classification of Visualization Types and Perspectives in Patents",
booktitle="Linking Theory and Practice of Digital Libraries",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="182--191",
abstract="Due to the swift growth of patent applications each year, information and multimedia retrieval approaches that facilitate patent exploration and retrieval are of utmost importance. Different types of visualizations (e.g., graphs, technical drawings) and perspectives (e.g., side view, perspective) are used to visualize details of innovations in patents. The classification of these images enables a more efficient search in digital libraries and allows for further analysis. So far, datasets for image type classification miss some important visualization types for patents. Furthermore, related work does not make use of recent deep learning approaches including transformers. In this paper, we adopt state-of-the-art deep learning methods for the classification of visualization types and perspectives in patent images. We extend the CLEF-IP dataset for image type classification in patents to ten classes and provide manual ground truth annotations. In addition, we derive a set of hierarchical classes from a dataset that provides weakly-labeled data for image perspectives. Experimental results have demonstrated the feasibility of the proposed approaches. Source code, models, and datasets are publicly available (https://github.com/TIBHannover/PatentImageClassification).",
isbn="978-3-031-43849-3"
}
DOI: 10.5281/zenodo.10019327
title: Extended_CLEF_IP_2011_Dataset

description: Extended CLEF-IP 2011 Dataset: We use the 2011 benchmark dataset of CLEF-IP [23] for visualization type classification. However, it does not cover block and
circuit diagrams, which are an important type of visualization frequently used in patents. We added this category as a tenth class and collected images by querying EPO's publication server (https://data.epo.org/publication-server/). Finally, we manually annotated images depicting block and circuit diagrams. The dataset statistics are provided in Table 1 (left).
Paper title: Classification of Visualization Types and Perspectives in Patents
Paper link: https://link.springer.com/chapter/10.1007/978-3-031-43849-3_16 
Git repository link: https://github.com/TIBHannover/PatentImageClassification
 
Cite as:
Ghauri, J.A., Müller-Budack, E., Ewerth, R. (2023). Classification of Visualization Types and Perspectives in Patents. In: Alonso, O., Cousijn, H., Silvello, G., Marrero, M., Teixeira Lopes, C., Marchesin, S. (eds) Linking Theory and Practice of Digital Libraries. TPDL 2023. Lecture Notes in Computer Science, vol 14241. Springer, Cham. https://doi.org/10.1007/978-3-031-43849-3_16
OR
 
@InProceedings{10.1007/978-3-031-43849-3_16,
author="Ghauri, Junaid Ahmed
and M{\"u}ller-Budack, Eric
and Ewerth, Ralph",
editor="Alonso, Omar
and Cousijn, Helena
and Silvello, Gianmaria
and Marrero, M{\'o}nica
and Teixeira Lopes, Carla
and Marchesin, Stefano",
title="Classification of Visualization Types and Perspectives in Patents",
booktitle="Linking Theory and Practice of Digital Libraries",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="182--191",
abstract="Due to the swift growth of patent applications each year, information and multimedia retrieval approaches that facilitate patent exploration and retrieval are of utmost importance. Different types of visualizations (e.g., graphs, technical drawings) and perspectives (e.g., side view, perspective) are used to visualize details of innovations in patents. The classification of these images enables a more efficient search in digital libraries and allows for further analysis. So far, datasets for image type classification miss some important visualization types for patents. Furthermore, related work does not make use of recent deep learning approaches including transformers. In this paper, we adopt state-of-the-art deep learning methods for the classification of visualization types and perspectives in patent images. We extend the CLEF-IP dataset for image type classification in patents to ten classes and provide manual ground truth annotations. In addition, we derive a set of hierarchical classes from a dataset that provides weakly-labeled data for image perspectives. Experimental results have demonstrated the feasibility of the proposed approaches. Source code, models, and datasets are publicly available (https://github.com/TIBHannover/PatentImageClassification).",
isbn="978-3-031-43849-3"
}
DOI: 10.5281/zenodo.10019506
title: USPTO_PIP_Dataset

description: USPTO-PIP Dataset: For the perspective classification task, we used the dataset presented by Wei et al. [32] based on patents collected from the USPTO. In
this dataset, meta information including image perspectives has been automatically extracted from captions. We processed the data to extract the most common (more than 1000 samples) perspective labels (e.g., left view, perspective) and identified a class taxonomy (Table 1, right) covering 2, 4, and 7 classes. We
utilize this information to compile the USPTO-PIP dataset for patent image perspective (PIP) classification from images
Paper title: Classification of Visualization Types and Perspectives in Patents
Paper link: https://link.springer.com/chapter/10.1007/978-3-031-43849-3_16 
Git repository link: https://github.com/TIBHannover/PatentImageClassification 
 
Cite as:
Ghauri, J.A., Müller-Budack, E., Ewerth, R. (2023). Classification of Visualization Types and Perspectives in Patents. In: Alonso, O., Cousijn, H., Silvello, G., Marrero, M., Teixeira Lopes, C., Marchesin, S. (eds) Linking Theory and Practice of Digital Libraries. TPDL 2023. Lecture Notes in Computer Science, vol 14241. Springer, Cham. https://doi.org/10.1007/978-3-031-43849-3_16
 
OR
 
@InProceedings{10.1007/978-3-031-43849-3_16,
author="Ghauri, Junaid Ahmed
and M{\"u}ller-Budack, Eric
and Ewerth, Ralph",
editor="Alonso, Omar
and Cousijn, Helena
and Silvello, Gianmaria
and Marrero, M{\'o}nica
and Teixeira Lopes, Carla
and Marchesin, Stefano",
title="Classification of Visualization Types and Perspectives in Patents",
booktitle="Linking Theory and Practice of Digital Libraries",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="182--191",
abstract="Due to the swift growth of patent applications each year, information and multimedia retrieval approaches that facilitate patent exploration and retrieval are of utmost importance. Different types of visualizations (e.g., graphs, technical drawings) and perspectives (e.g., side view, perspective) are used to visualize details of innovations in patents. The classification of these images enables a more efficient search in digital libraries and allows for further analysis. So far, datasets for image type classification miss some important visualization types for patents. Furthermore, related work does not make use of recent deep learning approaches including transformers. In this paper, we adopt state-of-the-art deep learning methods for the classification of visualization types and perspectives in patent images. We extend the CLEF-IP dataset for image type classification in patents to ten classes and provide manual ground truth annotations. In addition, we derive a set of hierarchical classes from a dataset that provides weakly-labeled data for image perspectives. Experimental results have demonstrated the feasibility of the proposed approaches. Source code, models, and datasets are publicly available (https://github.com/TIBHannover/PatentImageClassification).",
isbn="978-3-031-43849-3"
}
DOI: 10.5281/zenodo.10019505
title: USPTO_PIP_Dataset

description: USPTO-PIP Dataset: For the perspective classification task, we used the dataset presented by Wei et al. [32] based on patents collected from the USPTO. In
this dataset, meta information including image perspectives has been automatically extracted from captions. We processed the data to extract the most common (more than 1000 samples) perspective labels (e.g., left view, perspective) and identified a class taxonomy (Table 1, right) covering 2, 4, and 7 classes. We
utilize this information to compile the USPTO-PIP dataset for patent image perspective (PIP) classification from images
Paper title: Classification of Visualization Types and Perspectives in Patents
Paper link: https://link.springer.com/chapter/10.1007/978-3-031-43849-3_16 
Git repository link: https://github.com/TIBHannover/PatentImageClassification 
 
Cite as:
Ghauri, J.A., Müller-Budack, E., Ewerth, R. (2023). Classification of Visualization Types and Perspectives in Patents. In: Alonso, O., Cousijn, H., Silvello, G., Marrero, M., Teixeira Lopes, C., Marchesin, S. (eds) Linking Theory and Practice of Digital Libraries. TPDL 2023. Lecture Notes in Computer Science, vol 14241. Springer, Cham. https://doi.org/10.1007/978-3-031-43849-3_16
 
OR
 
@InProceedings{10.1007/978-3-031-43849-3_16,
author="Ghauri, Junaid Ahmed
and M{\"u}ller-Budack, Eric
and Ewerth, Ralph",
editor="Alonso, Omar
and Cousijn, Helena
and Silvello, Gianmaria
and Marrero, M{\'o}nica
and Teixeira Lopes, Carla
and Marchesin, Stefano",
title="Classification of Visualization Types and Perspectives in Patents",
booktitle="Linking Theory and Practice of Digital Libraries",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="182--191",
abstract="Due to the swift growth of patent applications each year, information and multimedia retrieval approaches that facilitate patent exploration and retrieval are of utmost importance. Different types of visualizations (e.g., graphs, technical drawings) and perspectives (e.g., side view, perspective) are used to visualize details of innovations in patents. The classification of these images enables a more efficient search in digital libraries and allows for further analysis. So far, datasets for image type classification miss some important visualization types for patents. Furthermore, related work does not make use of recent deep learning approaches including transformers. In this paper, we adopt state-of-the-art deep learning methods for the classification of visualization types and perspectives in patent images. We extend the CLEF-IP dataset for image type classification in patents to ten classes and provide manual ground truth annotations. In addition, we derive a set of hierarchical classes from a dataset that provides weakly-labeled data for image perspectives. Experimental results have demonstrated the feasibility of the proposed approaches. Source code, models, and datasets are publicly available (https://github.com/TIBHannover/PatentImageClassification).",
isbn="978-3-031-43849-3"
}
DOI: 10.6084/m9.figshare.24382018
title: Input_data_Devine_Lynch_2023.xlsx

description: Price curve intercepts, wind capacity factors, and generating units' data
DOI: 10.25921/zbcy-c057
title: NOAA GOES-R Series Advanced Baseline Imager (ABI) Level 2 Land Surface Albedo (LSA)
DOI: 10.5281/zenodo.10022669
title: Validation of the Spanish version of the Nurses' Global Assessment of Suicide Risk Scale (NGASR)
DOI: 10.5281/zenodo.10022668
title: Validation of the Spanish version of the Nurses' Global Assessment of Suicide Risk Scale (NGASR)
DOI: 10.5281/zenodo.10022678
title: Process Metrics Collected using Commit Guru

description: This data contains file-level process metrics collected using the commit guru tool.
DOI: 10.5281/zenodo.10022677
title: Process Metrics Collected using Commit Guru

description: This data contains file-level process metrics collected using the commit guru tool.
DOI: 10.6084/m9.figshare.24299866
title: Data for Nazari et al 2023.zip in Cells: doi: 10.3390/cells12202474

description: Data underlying the manuscript "Long prehensile protrusions can facilitate cancer cell invasion through the basement membrane" in the journal <i>Cells</i> by Nazari et al. 2023: doi: 10.3390/cells12202474
DOI: 10.6084/m9.figshare.24299866.v1
title: Data for Nazari et al 2023.zip in Cells: doi: 10.3390/cells12202474

description: Data underlying the manuscript "Long prehensile protrusions can facilitate cancer cell invasion through the basement membrane" in the journal <i>Cells</i> by Nazari et al. 2023: doi: 10.3390/cells12202474
DOI: 10.5281/zenodo.10017457
title: Data and software for article: Taylor-West, J. J., Balmforth, N. J., and Hogg, A. J. (2023) Lava delta formation: Mathematical modelling and laboratory experiments

description: Experimental and numerical data and scripts required to reproduce the results of Taylor-West, Balmforth, & Hogg 2023 "Lava delta formation: Mathematical modelling and laboratory experiments". Submitted to JGR: Earth Surfaces.
DOI: 10.25921/mwe8-7e12
title: NOAA Climate Data Record (CDR) of IR Sounder Upper Tropospheric Humidity BT, Version 4.0
DOI: 10.6084/m9.figshare.22360540
title: Survival Outcome of Local versus Radical Resection for Jejunoileal Gastrointestinal Stromal Tumors: A Propensity Score-Matched Population-Based Analysis

description: Data of patients with jejunoileal gastrointestinal stromal tumors (JI GISTs) were extracted from Surveillance, Epidemiology, and End Results (SEER) database. Predictor variables (age, sex, race, marital status, year of diagnosis, income, area, tumor size, T stage, N stage, M stage, grade, mitotic rate, tumor site, chemotherapy, lymphadenectomy and mode of surgery), survival time and outcome variable (all-cause death and disease-specific death) used in this study were recoded. Propensity score matching (PSM) was performed to reduce bias between local resection (LR) and radical resection (RR) groups.The article using these datasets is published at International Journal of Colorectal Disease.
DOI: 10.6084/m9.figshare.22360540.v2
title: Survival Outcome of Local versus Radical Resection for Jejunoileal Gastrointestinal Stromal Tumors: A Propensity Score-Matched Population-Based Analysis

description: Data of patients with jejunoileal gastrointestinal stromal tumors (JI GISTs) were extracted from Surveillance, Epidemiology, and End Results (SEER) database. Predictor variables (age, sex, race, marital status, year of diagnosis, income, area, tumor size, T stage, N stage, M stage, grade, mitotic rate, tumor site, chemotherapy, lymphadenectomy and mode of surgery), survival time and outcome variable (all-cause death and disease-specific death) used in this study were recoded. Propensity score matching (PSM) was performed to reduce bias between local resection (LR) and radical resection (RR) groups.The article using these datasets is published at International Journal of Colorectal Disease.
DOI: 10.6084/m9.figshare.22360540.v1
title: Survival Outcome of Local versus Radical Resection for Jejunoileal Gastrointestinal Stromal Tumors: A Propensity Score-Matched Population-Based Analysis

description: Data of patients with jejunoileal gastrointestinal stromal tumors (JI GISTs) were extracted from Surveillance, Epidemiology, and End Results (SEER) database. Predictor variables (age, sex, race, marital status, year of diagnosis, income, area, tumor size, T stage, N stage, M stage, grade, mitotic rate, tumor site, chemotherapy, lymphadenectomy and mode of surgery), survival time and outcome variable (disease-specific death) used in this study were recoded. Propensity score matching (PSM) was performed to reduce bias between local resection (LR) and radical resection (RR) groups.
DOI: 10.5066/p9pxoalq
title: Land Cover Basin Characteristics Rasters from NLCD 2019 for South Carolina StreamStats

description: In cooperation with the South Carolina Department of Transportation, the U.S. Geological Survey calculated four land cover basin characteristics rasters from the National Land Cover Database (NLCD) 2019 as part of updating the South Carolina StreamStats application. These datasets are raster representations of impervious surface, developed, forested, and storage land cover attributes within the South Carolina StreamStats study area, and will be served in the South Carolina StreamStats application (https://www.usgs.gov/streamstats) to describe delineated watersheds. The StreamStats application provides access to spatial analytical tools that are useful for water-resources planning and management, and for engineering and design purposes. The map-based user interface can be used to delineate watershed areas, get basin characteristics and estimates of flow statistics, and more.
DOI: 10.5281/zenodo.7686210
title: µCT scan data: stack of DICOM images

description: MicroCT or µCT scan data: stack of DICOM images of the crab Secretanella sp. (ALMNH:Paleo:6522) from an­­ upper Campanian methane seep limestone in Pennington County, South Dakota.
DOI: 10.5281/zenodo.7686211
title: µCT scan data: stack of DICOM images

description: MicroCT or µCT scan data: stack of DICOM images of the crab Secretanella sp. (ALMNH:Paleo:6522) from an­­ upper Campanian methane seep limestone in Pennington County, South Dakota.