cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.29149
title: A multiyear time series (2012–2022) of river plankton dynamics in a subtropical urbanizing watershed
title: A multiyear time series (2012–2022) of river plankton dynamics in a subtropical urbanizing watershed

description: Abstract: Urbanization exerts profound impacts on freshwater ecosystems, triggering shifts in plankton communities as environmental conditions change. Therefore, we present an extensive 11-year dataset (2012-2022) showcasing a distinct rural-to-urban gradient in the Houxi watershed, in subtropical China. Our systematic sampling captured multiple plankton communities: phytoplankton analyzed bi-annually (summer and winter) through microscopy, and bacterioplankton and eukaryotic plankton sampled seasonally through amplicon sequencing of 16S rRNA gene (V3-V4) and 18S rRNA gene (V9) regions, respectively. Synchronized collection of water quality and nutrient data provides comprehensive environmental context. The BEPE (Bacterioplankton, Eukaryotic plankton, Phytoplankton and Environmental variables) dataset showed spatiotemporal trends during the study period and enhances our understanding of urbanization and land-use impact on river-reservoir ecosystems with an emphasis on plankton communities, offering a valuable mitigating source for future ecological research and conservation efforts.
description: Abstract: Urbanization exerts profound impacts on freshwater ecosystems, triggering shifts in plankton communities as environmental conditions change. Therefore, we present an extensive 11-year dataset (2012-2022) showcasing a distinct rural-to-urban gradient in the Houxi watershed, in subtropical China. Our systematic sampling captured multiple plankton communities: phytoplankton analyzed bi-annually (summer and winter) through microscopy, and bacterioplankton and eukaryotic plankton sampled seasonally through amplicon sequencing of 16S rRNA gene (V3-V4) and 18S rRNA gene (V9) regions, respectively. Synchronized collection of water quality and nutrient data provides comprehensive environmental context. The BEPE (Bacterioplankton, Eukaryotic plankton, Phytoplankton and Environmental variables) dataset showed spatiotemporal trends during the study period and enhances our understanding of urbanization and land-use impact on river-reservoir ecosystems with an emphasis on plankton communities, offering a valuable mitigating source for future ecological research and conservation efforts.
DOI: 10.5281/zenodo.18491291
title: AKTIN - Daten zur Aufenthaltsdauer in Notaufnahmen

description: Im Datensatz "AKTIN – Daten zur Aufenthaltsdauer in Notaufnahmen" werden aggregierte Daten der Routinedokumentation aus einer Auswahl deutscher Notaufnahmen aus dem AKTIN-Notaufnahmeregister bereitgestellt. Anhand der Aufenthaltsdauer kann die Belastungssituation von Notaufnahmen abgeschätzt  werden. Dies ermöglicht eine kontinuierliche und transparente Surveillance der Akutversorgungslage in deutschen Notaufnahmen. Dazu werden die aktuellen Daten mit einem Referenzmittelwert verglichen, um die Belastungssituation von Notaufnahmen zu erfassen. Der Datensatz enthält aggregierte Angaben zu Besuchszahlen, mittlerer Aufenthaltsdauer sowie Abweichungen vom Referenzwert.
DOI: 10.57760/sciencedb.36791
title: Annual Mean Temperature Raster Data of Xiluodu Reservoir Area (2001-2020)
title: Annual Mean Temperature Raster Data of Xiluodu Reservoir Area (2001-2020)

description: This dataset is the annual mean temperature raster dataset of the Xiluodu Reservoir Area from 2001 to 2020 (the scope of the reservoir area is delineated along the ridge line based on digital elevation model data).The original data of this dataset is the "Monthly Mean Temperature Dataset of China (1901-2024) with 1 km Resolution", which is sourced from the National Earth System Science Data Center (https://www.geodata.cn/). The original dataset is in NetCDF format; it needs to be converted into monthly raster data in TIFF format by creating layers from different bands in ArcGIS.The annual dataset is obtained by averaging the raster data of all months within a specific year.This dataset adopts the GCS_WGS_1984 coordinate system, with a spatial resolution of 1 km and a unit of °C.
description: This dataset is the annual mean temperature raster dataset of the Xiluodu Reservoir Area from 2001 to 2020 (the scope of the reservoir area is delineated along the ridge line based on digital elevation model data).The original data of this dataset is the "Monthly Mean Temperature Dataset of China (1901-2024) with 1 km Resolution", which is sourced from the National Earth System Science Data Center (https://www.geodata.cn/). The original dataset is in NetCDF format; it needs to be converted into monthly raster data in TIFF format by creating layers from different bands in ArcGIS.The annual dataset is obtained by averaging the raster data of all months within a specific year.This dataset adopts the GCS_WGS_1984 coordinate system, with a spatial resolution of 1 km and a unit of °C.
DOI: 10.57760/sciencedb.36789
title: Annual Mean Temperature Raster Data of Longpan Reservoir Area (2001-2020)
title: Annual Mean Temperature Raster Data of Longpan Reservoir Area (2001-2020)

description: This dataset is the annual mean temperature raster dataset of the Longpan Reservoir Area from 2001 to 2020 (the scope of the reservoir area is delineated along the ridge line based on digital elevation model data).The original data of this dataset is the "Monthly Mean Temperature Dataset of China (1901-2024) with 1 km Resolution", which is sourced from the National Earth System Science Data Center (https://www.geodata.cn/). The original dataset is in NetCDF format; it needs to be converted into monthly raster data in TIFF format by creating layers from different bands in ArcGIS. The annual dataset is obtained by averaging the raster data of all months within a specific year. This dataset adopts the GCS_WGS_1984 coordinate system, with a spatial resolution of 1 km and a unit of °C.
description: This dataset is the annual mean temperature raster dataset of the Longpan Reservoir Area from 2001 to 2020 (the scope of the reservoir area is delineated along the ridge line based on digital elevation model data).The original data of this dataset is the "Monthly Mean Temperature Dataset of China (1901-2024) with 1 km Resolution", which is sourced from the National Earth System Science Data Center (https://www.geodata.cn/). The original dataset is in NetCDF format; it needs to be converted into monthly raster data in TIFF format by creating layers from different bands in ArcGIS. The annual dataset is obtained by averaging the raster data of all months within a specific year. This dataset adopts the GCS_WGS_1984 coordinate system, with a spatial resolution of 1 km and a unit of °C.
DOI: 10.57760/sciencedb.j00266.00001
title: 2001-2020 Xiluodu Reservoir Area Vegetation Net Primary Productivity Raster Data
title: 2001-2020 Xiluodu Reservoir Area Vegetation Net Primary Productivity Raster Data

description: This dataset is the vegetation net primary productivity raster data of Xiluodu Reservoir Area from 2001 to 2020 (the reservoir area is delineated along the ridge line based on digital elevation model data). The original data is the MOD17A3HGF products of MODIS, with a spatial resolution of 500m and a temporal resolution of 8 days. The annual NPP dataset is obtained by accumulating and summing the values of all 8-day astronomical images in a specific year. This dataset adopts the GCS_WGS_1984 coordinate system, with the unit of g·C/m².
description: This dataset is the vegetation net primary productivity raster data of Xiluodu Reservoir Area from 2001 to 2020 (the reservoir area is delineated along the ridge line based on digital elevation model data). The original data is the MOD17A3HGF products of MODIS, with a spatial resolution of 500m and a temporal resolution of 8 days. The annual NPP dataset is obtained by accumulating and summing the values of all 8-day astronomical images in a specific year. This dataset adopts the GCS_WGS_1984 coordinate system, with the unit of g·C/m².
DOI: 10.57760/sciencedb.36792
title: Annual Precipitation Raster Data of Longpan Reservoir Area (2001-2020)
title: Annual Precipitation Raster Data of Longpan Reservoir Area (2001-2020)

description: This dataset is a raster dataset of precipitation in Longpan Reservoir Area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this dataset is the "1901-2024 China 1km Resolution Monthly Precipitation Dataset", sourced from the National Earth System Science Data Center (https://www.geodata.cn/). The original dataset is a NetCDF format file, and monthly raster data in TIFF format needs to be created in ArcGIS based on different bands. The annual dataset is obtained by accumulating and summing the raster data of all months within a specific year. This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km and units of 0.1mm. 
description: This dataset is a raster dataset of precipitation in Longpan Reservoir Area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this dataset is the "1901-2024 China 1km Resolution Monthly Precipitation Dataset", sourced from the National Earth System Science Data Center (https://www.geodata.cn/). The original dataset is a NetCDF format file, and monthly raster data in TIFF format needs to be created in ArcGIS based on different bands. The annual dataset is obtained by accumulating and summing the raster data of all months within a specific year. This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km and units of 0.1mm.
DOI: 10.57760/sciencedb.36798
title: Annual Mean Wind Speed Raster Data of Xiluodu Reservoir Area (2001-2020)
title: Annual Mean Wind Speed Raster Data of Xiluodu Reservoir Area (2001-2020)

description: This dataset is a raster dataset of average wind speed in the Xiluodu reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this dataset is the "China Regional 1km Resolution Monthly Average Wind Speed Dataset (2000-2020)", sourced from the National Earth System Science Data Center. The annual dataset is obtained by averaging the raster data of all months within a specific year. This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km and units of m/s. 
description: This dataset is a raster dataset of average wind speed in the Xiluodu reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this dataset is the "China Regional 1km Resolution Monthly Average Wind Speed Dataset (2000-2020)", sourced from the National Earth System Science Data Center. The annual dataset is obtained by averaging the raster data of all months within a specific year. This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km and units of m/s.
DOI: 10.57760/sciencedb.36797
title: 2001-2020 Longpan Reservoir Area Vegetation Net Primary Productivity Raster Data
title: 2001-2020 Longpan Reservoir Area Vegetation Net Primary Productivity Raster Data

description: This dataset is the vegetation net primary productivity raster data of Longpan Reservoir Area from 2001 to 2020 (the reservoir area is delineated along the ridge line based on digital elevation model data). The original data is the MOD17A3HGF products of MODIS, with a spatial resolution of 500m and a temporal resolution of 8 days. The annual NPP dataset is obtained by accumulating and summing the values of all 8-day astronomical images in a specific year. This dataset adopts the GCS_WGS_1984 coordinate system, with the unit of g·C/m².
description: This dataset is the vegetation net primary productivity raster data of Longpan Reservoir Area from 2001 to 2020 (the reservoir area is delineated along the ridge line based on digital elevation model data). The original data is the MOD17A3HGF products of MODIS, with a spatial resolution of 500m and a temporal resolution of 8 days. The annual NPP dataset is obtained by accumulating and summing the values of all 8-day astronomical images in a specific year. This dataset adopts the GCS_WGS_1984 coordinate system, with the unit of g·C/m².
DOI: 10.57760/sciencedb.36796
title: Annual SPEI Raster Data of Xiluodu Reservoir Area (2001-2020)
title: Annual SPEI Raster Data of Xiluodu Reservoir Area (2001-2020)

description: This dataset is the annual SPEI raster dataset for the Xiluodu reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this data set is "HSPEI: a 1-km spatial resolution SPEI Dataset across Chinese Mainland from 2001 to 2022”, sourced from the Scientific Data Bank (https://www.scidb.cn/ ).This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km. 
description: This dataset is the annual SPEI raster dataset for the Xiluodu reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this data set is "HSPEI: a 1-km spatial resolution SPEI Dataset across Chinese Mainland from 2001 to 2022”, sourced from the Scientific Data Bank (https://www.scidb.cn/ ).This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km.
DOI: 10.57760/sciencedb.36795
title: Annual SPEI Raster Data of Longpan Reservoir Area (2001-2020)
title: Annual SPEI Raster Data of Longpan Reservoir Area (2001-2020)

description: This dataset is the annual SPEI raster dataset for the Longpan reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this data set is "HSPEI: a 1-km spatial resolution SPEI Dataset across Chinese Mainland from 2001 to 2022”, sourced from the Scientific Data Bank (https://www.scidb.cn/ ).This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km. 
description: This dataset is the annual SPEI raster dataset for the Longpan reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this data set is "HSPEI: a 1-km spatial resolution SPEI Dataset across Chinese Mainland from 2001 to 2022”, sourced from the Scientific Data Bank (https://www.scidb.cn/ ).This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km.
DOI: 10.57760/sciencedb.36794
title: Annual Mean Wind Speed Raster Data of Longpan Reservoir Area (2001-2020)
title: Annual Mean Wind Speed Raster Data of Longpan Reservoir Area (2001-2020)

description: This dataset is a raster dataset of average wind speed in the Longpan reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this dataset is the "China Regional 1km Resolution Monthly Average Wind Speed Dataset (2000-2020)", sourced from the National Earth System Science Data Center (https://www.geodata.cn/). The annual dataset is obtained by averaging the raster data of all months within a specific year. This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km and units of m/s. 
description: This dataset is a raster dataset of average wind speed in the Longpan reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this dataset is the "China Regional 1km Resolution Monthly Average Wind Speed Dataset (2000-2020)", sourced from the National Earth System Science Data Center (https://www.geodata.cn/). The annual dataset is obtained by averaging the raster data of all months within a specific year. This dataset uses the GCS_WGS_1984 coordinate system with a spatial resolution of 1km and units of m/s.
DOI: 10.57760/sciencedb.36793
title: Annual Precipitation Raster Data of Xiluodu Reservoir Area (2001-2020)
title: Annual Precipitation Raster Data of Xiluodu Reservoir Area (2001-2020)

description: This dataset is a raster dataset of precipitation in the Xiluodu reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this dataset is the "1901-2024 China 1km Resolution Monthly Precipitation Dataset", sourced from the National Earth System Science Data Center (https://www.geodata.cn/). The original dataset is a NetCDF format file, and monthly raster data in TIFF format needs to be created in ArcGIS based on different bands. The annual dataset is obtained by accumulating and summing the raster data of all months within a specific year. This dataset adopts the GCS_WGS_1984 coordinate system, with a spatial resolution of 1km and units of 0.1mm. 
description: This dataset is a raster dataset of precipitation in the Xiluodu reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this dataset is the "1901-2024 China 1km Resolution Monthly Precipitation Dataset", sourced from the National Earth System Science Data Center (https://www.geodata.cn/). The original dataset is a NetCDF format file, and monthly raster data in TIFF format needs to be created in ArcGIS based on different bands. The annual dataset is obtained by accumulating and summing the raster data of all months within a specific year. This dataset adopts the GCS_WGS_1984 coordinate system, with a spatial resolution of 1km and units of 0.1mm.
DOI: 10.5281/zenodo.16565016
title: ros2 unbag - fast ROS 2 bag export for any format

description: ros2 unbag is a ROS 2 CLI plugin with optional GUI for extracting selected topics from .db3 or .mcap bag files into formats like CSV, JSON, PCD, images, and more.

It comes with export routines for all message types (sensor data, point clouds, images). You need a special file format or message type? Add your own export plugin for any ROS 2 message or format, and chain custom processors to filter, transform or enrich messages (e.g. drop fields, compute derived values, remap frames).

Optional resampling synchronizes your data streams around a chosen master topic—aligning each other topic either to its last‑known sample (“last”) or to the temporally closest sample (“nearest”)—so you get a consistent sample count in your exports.

For high‑throughput workflows, ros2 unbag can spawn multiple worker processes and lets you tune CPU usage. Your topic selections, processor chains, export parameters and resampling mode (last or nearest) can be saved to and loaded from a JSON configuration, ensuring reproducibility across runs.
DOI: 10.5281/zenodo.16565017
title: ros2 unbag - fast ROS 2 bag export for any format

description: ros2 unbag is a ROS 2 CLI plugin with optional GUI for extracting selected topics from .db3 or .mcap bag files into formats like CSV, JSON, PCD, images, and more.

It comes with export routines for all message types (sensor data, point clouds, images). You need a special file format or message type? Add your own export plugin for any ROS 2 message or format, and chain custom processors to filter, transform or enrich messages (e.g. drop fields, compute derived values, remap frames).

Optional resampling synchronizes your data streams around a chosen master topic—aligning each other topic either to its last‑known sample (“last”) or to the temporally closest sample (“nearest”)—so you get a consistent sample count in your exports.

For high‑throughput workflows, ros2 unbag can spawn multiple worker processes and lets you tune CPU usage. Your topic selections, processor chains, export parameters and resampling mode (last or nearest) can be saved to and loaded from a JSON configuration, ensuring reproducibility across runs.
DOI: 10.5281/zenodo.16565371
title: betterosi - a python library for reading and writing open-simulation-interface files using betterproto2

description: A python library for reading and writing ASAM OSI (Open-Simulation-Interace) files (either .osi binary traces or MCAP files) using betterproto2 instead of the default protobuf generated code (better typing and enum support).



Supports writing and reading either mcap or osi files with betterosi.Writer and betterosi.read.

View OSI or MCAP file containing OSI GroundTruth betterosi-viewer <filepath.mcap / filepath.osi>(adapted from esmini)

Convert osi to mcap with betterosi-to-mcap <filepath to osi>.


The library uses code from esmini (betterosi/viewer.py) under MPL 2.0 license and the code from open-simulation-interface to read osi traces (betterosi/osi3trace.py).

The library uses code generation of python-betterproto2-compiler to generate python code from the protobuf definitions of open-simulation-interface.

Since OSI and esmini are under MPL, also this repository is published under MPL-2.0 license.
DOI: 10.5281/zenodo.16565372
title: betterosi - a python library for reading and writing open-simulation-interface files using betterproto2

description: A python library for reading and writing ASAM OSI (Open-Simulation-Interace) files (either .osi binary traces or MCAP files) using betterproto2 instead of the default protobuf generated code (better typing and enum support).



Supports writing and reading either mcap or osi files with betterosi.Writer and betterosi.read.

View OSI or MCAP file containing OSI GroundTruth betterosi-viewer <filepath.mcap / filepath.osi>(adapted from esmini)

Convert osi to mcap with betterosi-to-mcap <filepath to osi>.


The library uses code from esmini (betterosi/viewer.py) under MPL 2.0 license and the code from open-simulation-interface to read osi traces (betterosi/osi3trace.py).

The library uses code generation of python-betterproto2-compiler to generate python code from the protobuf definitions of open-simulation-interface.

Since OSI and esmini are under MPL, also this repository is published under MPL-2.0 license.
DOI: 10.5281/zenodo.17750166
title: Daten zum Verzeichnis der pädagogischen und theoretischen Werke Luigi Cherubinis

description: Innerhalb des Projekts Luigi Cherubini und die Kompositionslehre am Pariser Conservatoire als umfassende Ausbildungspraxis (ca. 1810-1840) (SNF-Projekt Nr. 200698) wurde 2021-2025 ein Online-Teilverzeichnis aller pädagogischen und theoretischen Werke Cherubinis verfasst. Die Werke wurden als Normdaten mit der RISM-Software Muscat katalogisiert.

Dieser Datensatz beinhaltet:



eine Übersicht über die bekannten Quellen für theoretischen und pädagogischen Werke von Luigi Cherubini (LCC_Uebersicht.pdf)

eine Tabelle der Werke mit einem Link zu den entsprechenden Einträge auf RISM Online (LCC_Tabelle.xlsx)

eine Tabelle der benutzen Originalquellen mit Links zu der Quellenbeschreibung auf RISM Online oder auf dem jeweiligen Bibliothekskatalog (LCC_Quellen.xlsx)


Die Werke in der Datei LCC_Tabelle.xlsx werden durch ein Kürzel für die jeweilige Gattung gekennzeichnet (siehe unten). Auf das Kürzel folgt eine laufende Nummerierung.

Gattungskürzel



CO Cours de contrepoint et de fugue

MH marches d’harmonie

MO morceaux de concours

BC basses chiffrées

CD chants donnés

SF sujets de fuge

SO solfèges 


Die Datei LCC_Tabelle.xlsx enthält mehrere Spalten, nach denen die einzelnen Werke gesucht oder sortiert werden können. 

Spaltennamen



LCC-Nummer

Tonart

Schlüssel (nur bei Solfèges)

Datum

Notizen

Werkbeschreibung

Quellen