cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48677/e722cc0a-6cb3-43e2-aec0-56c199f12328
title: ALS DTM CRS3035RES50000mN2800000E4800000 Höhenraster 1m Stichtag 15.09.2022

description: Das ALS DTM-Höhenraster gehört zur Produktgruppe "Digitales Geländehöhenmodell" (DGM). Das digitale Geländehöhenmodell (DGM) beschreibt die Erdoberfläche (natürlicher Boden, ohne Bewuchs) in Form von Punktmengen, die in einem regelmäßigen Gitter von 1m angeordnet und in Lage und Höhe in den europäischen Referenzsystemen georeferenziert sind. Das ALS DTM Höhenraster wurde mit Airborne Laserscanning Technologien aufgenommen. Das ALS DTM Höhenraster ist ein Kooperationsprodukt der österreichischen behördlichen Geodatenprovider des Bundes und der Länder. Dies ist eine von insgesamt 55 Kacheln, welche zusammen das Gebiet Österreich abdecken.
DOI: 10.48677/16f17620-9da4-437e-a480-b73a2dd1786d
title: ALS DTM CRS3035RES50000mN2850000E4600000 Höhenraster 1m Stichtag 15.09.2022

description: Das ALS DTM-Höhenraster gehört zur Produktgruppe "Digitales Geländehöhenmodell" (DGM). Das digitale Geländehöhenmodell (DGM) beschreibt die Erdoberfläche (natürlicher Boden, ohne Bewuchs) in Form von Punktmengen, die in einem regelmäßigen Gitter von 1m angeordnet und in Lage und Höhe in den europäischen Referenzsystemen georeferenziert sind. Das ALS DTM Höhenraster wurde mit Airborne Laserscanning Technologien aufgenommen. Das ALS DTM Höhenraster ist ein Kooperationsprodukt der österreichischen behördlichen Geodatenprovider des Bundes und der Länder. Dies ist eine von insgesamt 55 Kacheln, welche zusammen das Gebiet Österreich abdecken.
DOI: 10.48677/9ffbb96d-22a4-4622-8299-b9722986ff3e
title: ALS DTM CRS3035RES50000mN2850000E4650000 Höhenraster 1m Stichtag 15.09.2022

description: Das ALS DTM-Höhenraster gehört zur Produktgruppe "Digitales Geländehöhenmodell" (DGM). Das digitale Geländehöhenmodell (DGM) beschreibt die Erdoberfläche (natürlicher Boden, ohne Bewuchs) in Form von Punktmengen, die in einem regelmäßigen Gitter von 1m angeordnet und in Lage und Höhe in den europäischen Referenzsystemen georeferenziert sind. Das ALS DTM Höhenraster wurde mit Airborne Laserscanning Technologien aufgenommen. Das ALS DTM Höhenraster ist ein Kooperationsprodukt der österreichischen behördlichen Geodatenprovider des Bundes und der Länder. Dies ist eine von insgesamt 55 Kacheln, welche zusammen das Gebiet Österreich abdecken.
DOI: 10.25345/c5xs5jt03
title: MassIVE MSV000092333 - GNPS 230703 compounds contained in Burkholderia extract
title: MSMS analysis data of compounds included in the extrtact of some Burkholderia species
DOI: 10.48677/efdfe29b-58bd-4ffd-b508-d653c9698c1e
title: ALS DTM CRS3035RES50000mN2850000E4700000 Höhenraster 1m Stichtag 15.09.2022

description: Das ALS DTM-Höhenraster gehört zur Produktgruppe "Digitales Geländehöhenmodell" (DGM). Das digitale Geländehöhenmodell (DGM) beschreibt die Erdoberfläche (natürlicher Boden, ohne Bewuchs) in Form von Punktmengen, die in einem regelmäßigen Gitter von 1m angeordnet und in Lage und Höhe in den europäischen Referenzsystemen georeferenziert sind. Das ALS DTM Höhenraster wurde mit Airborne Laserscanning Technologien aufgenommen. Das ALS DTM Höhenraster ist ein Kooperationsprodukt der österreichischen behördlichen Geodatenprovider des Bundes und der Länder. Dies ist eine von insgesamt 55 Kacheln, welche zusammen das Gebiet Österreich abdecken.
DOI: 10.48677/4944ac37-7f30-410f-b381-18a59fce4f66
title: ALS DTM CRS3035RES50000mN2850000E4750000 Höhenraster 1m Stichtag 15.09.2022

description: Das ALS DTM-Höhenraster gehört zur Produktgruppe "Digitales Geländehöhenmodell" (DGM). Das digitale Geländehöhenmodell (DGM) beschreibt die Erdoberfläche (natürlicher Boden, ohne Bewuchs) in Form von Punktmengen, die in einem regelmäßigen Gitter von 1m angeordnet und in Lage und Höhe in den europäischen Referenzsystemen georeferenziert sind. Das ALS DTM Höhenraster wurde mit Airborne Laserscanning Technologien aufgenommen. Das ALS DTM Höhenraster ist ein Kooperationsprodukt der österreichischen behördlichen Geodatenprovider des Bundes und der Länder. Dies ist eine von insgesamt 55 Kacheln, welche zusammen das Gebiet Österreich abdecken.
DOI: 10.48677/fe887834-eca4-4a32-ac6d-eacbf141396c
title: ALS DTM CRS3035RES50000mN2850000E4800000 Höhenraster 1m Stichtag 15.09.2022

description: Das ALS DTM-Höhenraster gehört zur Produktgruppe "Digitales Geländehöhenmodell" (DGM). Das digitale Geländehöhenmodell (DGM) beschreibt die Erdoberfläche (natürlicher Boden, ohne Bewuchs) in Form von Punktmengen, die in einem regelmäßigen Gitter von 1m angeordnet und in Lage und Höhe in den europäischen Referenzsystemen georeferenziert sind. Das ALS DTM Höhenraster wurde mit Airborne Laserscanning Technologien aufgenommen. Das ALS DTM Höhenraster ist ein Kooperationsprodukt der österreichischen behördlichen Geodatenprovider des Bundes und der Länder. Dies ist eine von insgesamt 55 Kacheln, welche zusammen das Gebiet Österreich abdecken.
DOI: 10.6084/m9.figshare.23618073
title: The Usefulness of Resistant Maltodextrin and Chitosan Oligosaccharide in Management of Gut Leakage and Microbiota in Chronic Kidney Disease.xlsx

description: Fecal Microbiota Diversity Shannon index and Pielou's evenness were evaluated to assess the diversity and evenness of bacterial communities at week 12 of the experiment. However, no significant differences were observed in Shannon index and Pielou's evenness among the different groups (Figure 3A). In contrast, when examining the control group, beta diversity analysis using Principal Coordinate Analysis (PCoA) of Bray-Curtis Dissimilarity demonstrated no significant difference in diversity between week 0 and week 12. This suggests that the overall composition of bacterial communities in the control group remained relatively stable over the course of the experiment. Regarding specific prebiotic treatments, the inulin group exhibited a significant decrease in beta diversity, similar to the CKD group, indicating a notable change in the bacterial community composition over time. Conversely, the rats treated with RMD did not show a significant change in beta diversity, similar to the control group. This suggests that RMD treatment maintained a relatively stable and consistent bacterial community composition throughout the 12-week period. Interestingly, the COS group displayed a significant increase in beta diversity, suggesting a notable shift in the composition of bacterial communities compared to both the control and CKD groups.
DOI: 10.6084/m9.figshare.23618073.v1
title: The Usefulness of Resistant Maltodextrin and Chitosan Oligosaccharide in Management of Gut Leakage and Microbiota in Chronic Kidney Disease.xlsx

description: Fecal Microbiota Diversity Shannon index and Pielou's evenness were evaluated to assess the diversity and evenness of bacterial communities at week 12 of the experiment. However, no significant differences were observed in Shannon index and Pielou's evenness among the different groups (Figure 3A). In contrast, when examining the control group, beta diversity analysis using Principal Coordinate Analysis (PCoA) of Bray-Curtis Dissimilarity demonstrated no significant difference in diversity between week 0 and week 12. This suggests that the overall composition of bacterial communities in the control group remained relatively stable over the course of the experiment. Regarding specific prebiotic treatments, the inulin group exhibited a significant decrease in beta diversity, similar to the CKD group, indicating a notable change in the bacterial community composition over time. Conversely, the rats treated with RMD did not show a significant change in beta diversity, similar to the control group. This suggests that RMD treatment maintained a relatively stable and consistent bacterial community composition throughout the 12-week period. Interestingly, the COS group displayed a significant increase in beta diversity, suggesting a notable shift in the composition of bacterial communities compared to both the control and CKD groups.
DOI: 10.34740/kaggle/dsv/6072444
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.7526344
title: CEDS-SatEM Gridded - Bulk Emissions

description: Release of gridded greenhouse gas emissions from 2000-2019 based on the 2021_04_21 CEDS release with direct inclusion of point sources as time series. This dataset contains just the primary emissions species covered by CEDS, both at 0.5° and 0.1° spatial resolution. Data for speciated VOC emissions can be found in the companion dataset, DOI 10.5281/zenodo.7526534.
DOI: 10.5281/zenodo.7958231
title: CEDS-SatEM Gridded - Bulk Emissions

description: Release of gridded greenhouse gas emissions from 2000-2019 based on the 2021_04_21 CEDS release with direct inclusion of point sources as time series. This dataset contains just the primary emissions species covered by CEDS, both at 0.5° and 0.1° spatial resolution. Data for speciated VOC emissions can be found in the companion dataset, DOI 10.5281/zenodo.7526534.
DOI: 10.5281/zenodo.7526533
title: CEDS-SatEM Gridded - Speciated-VOC Emissions

description: Release of gridded greenhouse gas emissions from 2000-2019 based on the 2021_04_21 CEDS release with direct inclusion of point sources as time series. This dataset contains just the speciated VOC emissions covered by CEDS, both at 0.5° and 0.1° spatial resolution. Data for the CEDS primary emission species can be found in the companion dataset, DOI: 10.5281/zenodo.7526345.
DOI: 10.5281/zenodo.7958233
title: CEDS-SatEM Gridded - Speciated-VOC Emissions

description: Release of gridded greenhouse gas emissions from 2000-2019 based on the 2021_04_21 CEDS release with direct inclusion of point sources as time series. This dataset contains just the speciated VOC emissions covered by CEDS, both at 0.5° and 0.1° spatial resolution. Data for the CEDS primary emission species can be found in the companion dataset, DOI: 10.5281/zenodo.7526345.
DOI: 10.48323/p1hjs-j2879
title: MS_Exploris_2023_June
DOI: 10.5281/zenodo.8109226
title: chlorocystis_dangeardii_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.8109225
title: chlorocystis_dangeardii_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.8105688
title: External Digital Appendices of Master's Thesis " MENU CARDS IN LIBRARY COLLECTIONS: AN EXAMINATION OF APPROACHES AND PROBLEMS OF INDEXING"

description: The external digital appendices are two datasets presented in form of tables: "Overview of Menu Collections in Libraries, Archives, and Museums Worldwide" (2023) and "Selection of Digital Collections With Menu-Specific Approaches of Access and Description (2023)". The datasets were produced for the Master’s Thesis "MENU CARDS IN LIBRARY COLLECTIONS: AN EXAMINATION OF APPROACHES AND PROBLEMS OF INDEXING", Humboldt-Universität zu Berlin. This version is only accessible to reviewers, but an open access version will be published afterwards.
DOI: 10.5281/zenodo.8105687
title: External Digital Appendices of Master's Thesis " MENU CARDS IN LIBRARY COLLECTIONS: AN EXAMINATION OF APPROACHES AND PROBLEMS OF INDEXING"

description: The external digital appendices are two datasets presented in form of tables: "Overview of Menu Collections in Libraries, Archives, and Museums Worldwide" (2023) and "Selection of Digital Collections With Menu-Specific Approaches of Access and Description (2023)". The datasets were produced for the Master’s Thesis "MENU CARDS IN LIBRARY COLLECTIONS: AN EXAMINATION OF APPROACHES AND PROBLEMS OF INDEXING", Humboldt-Universität zu Berlin. This version is only accessible to reviewers, but an open access version will be published afterwards.
DOI: 10.3886/e192483
title: Replication files for: Hot Monet Inflows and Bank Risk-Taking: Germany from the 1920s to the Great Depression

description: This paper explores the origins of German banks’ risk-taking in the years preceding the 1931 crisis. The 1920s were marked by a large and prolonged increase in capital flows into Germany, chiefly from the US and the UK. This coincided, at the individual bank level, with a rise in leverage and a fall in liquidity. We examine possible connections between the two phenomena. Our analysis is based on a combination of historiographical work and statistical modelling based on a newly hand-collected bi-monthly dataset on German reporting banks from 1925 to 1935. Bank by bank we examine the effects of foreign inflows on decisions related to leverage, lending and liquidity. The Dawes Plan of 1924 and the relative absence of a Too-Big-to-Fail environment allow us to mitigate endogeneity concerns. We suggest that while capital inflows did not seem to impact banks’ liquidity decisions, their impact on leverage was non-negligeable.
DOI: 10.3886/e192483v1
title: Replication files for: Hot Monet Inflows and Bank Risk-Taking: Germany from the 1920s to the Great Depression

description: This paper explores the origins of German banks’ risk-taking in the years preceding the 1931 crisis. The 1920s were marked by a large and prolonged increase in capital flows into Germany, chiefly from the US and the UK. This coincided, at the individual bank level, with a rise in leverage and a fall in liquidity. We examine possible connections between the two phenomena. Our analysis is based on a combination of historiographical work and statistical modelling based on a newly hand-collected bi-monthly dataset on German reporting banks from 1925 to 1935. Bank by bank we examine the effects of foreign inflows on decisions related to leverage, lending and liquidity. The Dawes Plan of 1924 and the relative absence of a Too-Big-to-Fail environment allow us to mitigate endogeneity concerns. We suggest that while capital inflows did not seem to impact banks’ liquidity decisions, their impact on leverage was non-negligeable.
DOI: 10.6069/dmx5-8y05
title: Comparing peptide identifications by FAIMS versus quadrupole gas phase fractionation

description: Gas phase fractionation techniques have become popular in the field of mass spectrometry to improve results without using more sample. High field asymmetric waveform ion mobility spectrometry (FAIMS) coupled to liquid chromatography-mass spectrometry (LC-MS) has been shown to increase peptide and protein detections compared to LC-MS/MS alone. However, FAIMS has not been compared to other methods of gas phase fractionation, such as quadrupole gas phase fractionation, which leaves uncertainty about the mechanisms of improvement. The goal of this work was to assess whether FAIMS improves peptide identifications because 1) gas phase fractionation enables the analysis of less abundant signals by excluding more abundant precursors from filling the ion trap, 2) the use of FAIMS reduces co-isolation of peptides during the MS/MS process resulting in a reduction of chimeric spectra, or 3) a combination of both. To investigate these hypotheses, pooled human brain tissue samples were measured in triplicate using FAIMS gas phase fractionation, quadrupole gas phase fractionation, or no gas phase fractionation. To confirm the results, the experiment was reproduced on another instrument. On both instruments, FAIMS gas phase fractionation reduced co-isolation of peptides and generated fewer chimeric spectra even though it was less efficient at transmitting ions than quadrupole gas phase fractionation. This suggests that improvements can be made to the DDA acquisition process by excluding peptides for MS/MS that have more than one peptide isotope distribution in the isolation window.
DOI: 10.5281/zenodo.8109309
title: dendronotus_rufus_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.8109308
title: dendronotus_rufus_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.7866442
title: VINEyard Piacenza Image Collections - VINEPICs

description: <strong>For a detailed description of this dataset</strong>, based on the <em>Datasheets for Datasets</em> (Gebru, Timnit, et al. "Datasheets for datasets." <em>Communications of the ACM</em> 64.12 (2021): 86-92.), check the <strong>VINEPICs_datasheet.md</strong> file. <strong>For what purpose was the dataset created?</strong><br> VINEPICs was developed specifically for the purpose of detecting grape bunches in RGB images and facilitating tasks such as object detection, semantic segmentation, and instance segmentation. The detection of grape bunches serves as the initial phase in an analysis pipeline designed for vine plant phenotyping. The dataset encompasses a wide range of lighting conditions, camera orientations, plant defoliation levels, species variations, and cultivation methods. Consequently, this dataset presents an opportunity to explore the influence of each source of variability on grape bunch detection<strong>.</strong> <strong>What do the instances that comprise the dataset represent?</strong><br> The dataset consists of RGB images showcasing various species of vine plants. Specifically, the images represent three different Vitis vinifera varieties:<br> - Red Globe, a type of table grape<br> - Cabernet Sauvignon, a red wine grape<br> - Ortrugo, a white wine grape These images have been collected over different years and dates at the vineyard facility of Università Cattolica del Sacro Cuore in Piacenza, Italy. You can find the images stored in the "data/images" directory, organized into subdirectories based on the starting time of data collection, indicating the day (and, if available, the approximate time in minutes). Images collected in 2022 are named using timestamps with nanosecond precision. <strong>Is there a label or target associated with each instance?</strong><br> Each image has undergone manual annotation using the Computer Vision Annotation Tool (CVAT) (https://github.com/opencv/cvat). Grape bunches have been meticulously outlined with polygon annotations. These annotations belong to a single class, "bunch," and have been saved in a JSON file using the COCO Object Detection format, including segmentation masks (https://cocodataset.org/#format-data). <strong>What mechanisms or procedures were used to collect the data?</strong><br> The data was collected using a D435 Intel Realsense camera, which was mounted on a four-wheeled skid-steering robot. The robot was teleoperated during the data collection process. The data was recorded by streaming the camera's feed into rosbag format. Specifically, the camera was connected via a USB 3.0 interface to a PC running Ubuntu 18.04 and ROS Melodic.
DOI: 10.5281/zenodo.7866441
title: VINEyard Piacenza Image Collections - VINEPICs

description: <strong>For a detailed description of this dataset</strong>, based on the <em>Datasheets for Datasets</em> (Gebru, Timnit, et al. "Datasheets for datasets." <em>Communications of the ACM</em> 64.12 (2021): 86-92.), check the <strong>VINEPICs_datasheet.md</strong> file. <strong>For what purpose was the dataset created?</strong><br> VINEPICs was developed specifically for the purpose of detecting grape bunches in RGB images and facilitating tasks such as object detection, semantic segmentation, and instance segmentation. The detection of grape bunches serves as the initial phase in an analysis pipeline designed for vine plant phenotyping. The dataset encompasses a wide range of lighting conditions, camera orientations, plant defoliation levels, species variations, and cultivation methods. Consequently, this dataset presents an opportunity to explore the influence of each source of variability on grape bunch detection<strong>.</strong> <strong>What do the instances that comprise the dataset represent?</strong><br> The dataset consists of RGB images showcasing various species of vine plants. Specifically, the images represent three different Vitis vinifera varieties:<br> - Red Globe, a type of table grape<br> - Cabernet Sauvignon, a red wine grape<br> - Ortrugo, a white wine grape These images have been collected over different years and dates at the vineyard facility of Università Cattolica del Sacro Cuore in Piacenza, Italy. You can find the images stored in the "data/images" directory, organized into subdirectories based on the starting time of data collection, indicating the day (and, if available, the approximate time in minutes). Images collected in 2022 are named using timestamps with nanosecond precision. <strong>Is there a label or target associated with each instance?</strong><br> Each image has undergone manual annotation using the Computer Vision Annotation Tool (CVAT) (https://github.com/opencv/cvat). Grape bunches have been meticulously outlined with polygon annotations. These annotations belong to a single class, "bunch," and have been saved in a JSON file using the COCO Object Detection format, including segmentation masks (https://cocodataset.org/#format-data). <strong>What mechanisms or procedures were used to collect the data?</strong><br> The data was collected using a D435 Intel Realsense camera, which was mounted on a four-wheeled skid-steering robot. The robot was teleoperated during the data collection process. The data was recorded by streaming the camera's feed into rosbag format. Specifically, the camera was connected via a USB 3.0 interface to a PC running Ubuntu 18.04 and ROS Melodic.
DOI: 10.5281/zenodo.8109381
title: devaleraea_mollis_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.8109380
title: devaleraea_mollis_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.8109261
title: Evaluation of Agriculture Open Data Ecosystem Maturity

description: This dataset represents an evaluation of the key elements of the agricultural open data ecosystem in Croatia: Stakeholders, Infrastructure, Data, Policy/Governance. The evaluation was made by experts. For each element, an evaluation was made according to certain criteria. The experts involved in the research were representatives of each stakeholder group, namely: Management and Support Organizations, Agriculture Producers/Farmers, Suppliers, Researchers and Scientists, Consumers/Consumer Organizations and Other Stakeholders.
DOI: 10.5281/zenodo.8109260
title: Evaluation of Agriculture Open Data Ecosystem Maturity

description: This dataset represents an evaluation of the key elements of the agricultural open data ecosystem in Croatia: Stakeholders, Infrastructure, Data, Policy/Governance. The evaluation was made by experts. For each element, an evaluation was made according to certain criteria. The experts involved in the research were representatives of each stakeholder group, namely: Management and Support Organizations, Agriculture Producers/Farmers, Suppliers, Researchers and Scientists, Consumers/Consumer Organizations and Other Stakeholders.