cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21233/vfnn-ts98
title: Neal Pond diatom surface sample dataset

description: Raw data for the Neal Pond diatom surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/fycm-jv97
title: Blanchard Lake water chemistry dataset

description: Raw data for the Blanchard Lake water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/gtxe-ma81
title: LDDsite2085 water chemistry dataset

description: Raw data for the LDDsite2085 water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/zdj5-1019
title: Gallseredsmossen testate amoebae surface sample dataset

description: Raw data for the Gallseredsmossen testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/2747-7237
title: Andrus Lake water chemistry dataset

description: Raw data for the Andrus Lake water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.5281/zenodo.15365019
title: PDHD and PONV After Spinal Anesthesia

description: This dataset contains clinical trial data from a randomized, controlled trial designed to evaluate the effectiveness of gabapentin, ketamine, their combination, and placebo for preventing postoperative nausea, vomiting, and headache after spinal anesthesia in elective cesarean delivery. The trial included four groups: one receiving only gabapentin, one receiving only ketamine, one receiving a combination of both drugs, and a placebo group. Participants were assessed for the primary outcomes—headache, nausea severity, and vomiting frequency—at four time points: 1, 6, 12, and 24 hours after surgery. Additionally, maternal satisfaction was measured at 24 hours, and neonatal health was evaluated using Apgar scores recorded at 1 and 5 minutes post-delivery. The data includes both continuous and categorical variables, such as the severity of headache and nausea, which were recorded on 0-10 visual analogue scales (VAS). The dataset also includes baseline demographic and clinical variables, including maternal age, BMI, and obstetric history (e.g., gravidity, ASA classification). This dataset was collected in a single-center setting, with randomization performed using a 1:1:1:1 allocation scheme to ensure equal distribution of participants across the four groups. A total of [N] participants were enrolled, with complete data for all primary outcomes and minimal missing data (less than 5% across all variables). Participants were assigned to receive either gabapentin (300 mg orally), ketamine (0.25 mg/kg IV bolus), a combination of both drugs, or placebo (matching oral capsules and IV saline). All participants were healthy, non-obese parturients (classified as ASA II) scheduled for elective cesarean delivery under spinal anesthesia. Exclusion criteria included a history of chronic headache, prior use of antiemetics within 24 hours, or any contraindications to the study drugs. The primary goal of the study was to determine whether gabapentin, ketamine, or their combination could reduce the incidence and severity of nausea, vomiting, and headache compared to placebo. Secondary outcomes included the assessment of neonatal health (via Apgar scores) and maternal satisfaction at 24 hours. The data collected from this study will be valuable for future meta-analyses of post-cesarean anesthesia-related complications. They will provide insight into the role of gabapentin and ketamine in preventing these common post-operative issues. The dataset allows for comparing individual drugs versus their combination, making it useful for analyzing potential synergistic effects or dose-sparing properties. Additionally, it provides a complete picture of both maternal and neonatal outcomes, offering valuable data for obstetric anesthesiologists in tailoring post-operative care.
DOI: 10.5281/zenodo.15365020
title: PDHD and PONV After Spinal Anesthesia

description: This dataset contains clinical trial data from a randomized, controlled trial designed to evaluate the effectiveness of gabapentin, ketamine, their combination, and placebo for preventing postoperative nausea, vomiting, and headache after spinal anesthesia in elective cesarean delivery. The trial included four groups: one receiving only gabapentin, one receiving only ketamine, one receiving a combination of both drugs, and a placebo group. Participants were assessed for the primary outcomes—headache, nausea severity, and vomiting frequency—at four time points: 1, 6, 12, and 24 hours after surgery. Additionally, maternal satisfaction was measured at 24 hours, and neonatal health was evaluated using Apgar scores recorded at 1 and 5 minutes post-delivery. The data includes both continuous and categorical variables, such as the severity of headache and nausea, which were recorded on 0-10 visual analogue scales (VAS). The dataset also includes baseline demographic and clinical variables, including maternal age, BMI, and obstetric history (e.g., gravidity, ASA classification). This dataset was collected in a single-center setting, with randomization performed using a 1:1:1:1 allocation scheme to ensure equal distribution of participants across the four groups. A total of [N] participants were enrolled, with complete data for all primary outcomes and minimal missing data (less than 5% across all variables). Participants were assigned to receive either gabapentin (300 mg orally), ketamine (0.25 mg/kg IV bolus), a combination of both drugs, or placebo (matching oral capsules and IV saline). All participants were healthy, non-obese parturients (classified as ASA II) scheduled for elective cesarean delivery under spinal anesthesia. Exclusion criteria included a history of chronic headache, prior use of antiemetics within 24 hours, or any contraindications to the study drugs. The primary goal of the study was to determine whether gabapentin, ketamine, or their combination could reduce the incidence and severity of nausea, vomiting, and headache compared to placebo. Secondary outcomes included the assessment of neonatal health (via Apgar scores) and maternal satisfaction at 24 hours. The data collected from this study will be valuable for future meta-analyses of post-cesarean anesthesia-related complications. They will provide insight into the role of gabapentin and ketamine in preventing these common post-operative issues. The dataset allows for comparing individual drugs versus their combination, making it useful for analyzing potential synergistic effects or dose-sparing properties. Additionally, it provides a complete picture of both maternal and neonatal outcomes, offering valuable data for obstetric anesthesiologists in tailoring post-operative care.
DOI: 10.21233/jg2f-zt71
title: Saint-Linaire pollen dataset

description: Raw data for the Saint-Linaire pollen dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.18154/rwth-2025-04398
title: Experimental Dataset: Experimental Study on the Influence of Methane-Cofiring on Pulverized Coal Flames under Air and Oxy-Fuel Conditions

description: doi:10.18154/RWTH-2025-04398
description: This data publication contains the quantitative values of the narrow-band imaging and laser Doppler velocimetry measurements published in the conference article, named 'Experimental Study on the Influence of Methane-Cofiring on Pulverized Coal Flames under Air and Oxy-Fuel Conditions'. Experiments are carried out using a pilot-scale down-fired cylindrical combustion chamber equipped with a swirl burner. Studied operating conditions of flames of lignite/methane fuel mixtures have an identical thermal output of 40 kWth, with the thermal output share of methane gradually decreasing from 50 vol.% to 0 vol.% while the lignite share (Rhenish lignite) increased from 50 vol.% to 100 vol.% (self-sustained condition). To visualize flame structure and to identify reaction zones, narrow-band imaging is employed. Narrow-band flame imaging is conducted at two different wavelengths (OH* and CH* radical band heads). A detailed flow field characterization of the respective flames is conducted by two-dimensional laser Doppler velocimetry measurements. Radial profiles of axial and tangential velocities have been determined using an optical long-range laser Doppler velocimetry (LDV) system operated in backscatter mode and mounted to a traverse. Solid fuel particles are used as flow tracers. Results and the experimental conditions from the present dataset are in depth described in the conference article.
DOI: 10.21233/6sf1-b149
title: Tooele vertebrate fauna dataset

description: Raw data for the Tooele vertebrate fauna dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.wvj6f2
title: Occurrence Download

description: A dataset containing 3848 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Vincetoxicum rossicum (Kleopow) Barbar.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is Canada",
 "Year is one of (1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024)"
 ]
}

 The dataset includes 3848 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021553-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/kdkq-pe71
title: Whitney L. water chemistry dataset

description: Raw data for the Whitney L. water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/2b4a-vg39
title: Lake Hattie water chemistry dataset

description: Raw data for the Lake Hattie water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/prrb-zj65
title: Lake Erie water chemistry dataset

description: Raw data for the Lake Erie water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/s1hh-t651
title: Jungerman vertebrate fauna dataset

description: Raw data for the Jungerman vertebrate fauna dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/kg0a-nw84
title: Rhododendron Pond pollen surface sample dataset

description: Raw data for the Rhododendron Pond pollen surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.5281/zenodo.6560037
title: Grand Traffic Auto (GTA) Dataset: a Collection of Synthetic Images for Vehicle Detection, Segmentation and Counting

description: The Dataset

A synthetic dataset for vehicle detection, segmentation, and counting. It comprises images extracted from the highly photo-realistic video game Grand Theft Auto V developed by Rockstar North. Each image is labeled by the game engine providing pixel-wise masks and bounding boxes localizing vehicle instances.The dataset includes about 10k synthetic images depicting several urban scenarios with various background scenes, lighting, camera positions, traffic densities, and weather conditions. In total, we labeled more than 411,000 vehicles.

We provide two annotation files:





coco_annotations.json --> JSON file that follows the golden standard MS COCO data format (for more info see https://cocodataset.org/#format-data). All the vehicles are labeled with the COCO category 'car'. It is suitable for vehicle detection and instance segmentation.




dot_annotations.csv --> CSV file that contains xy coordinates of the centroids of the vehicles. Dot annotation is commonly used for the visual counting task.



 

Citing our work

If you found this dataset useful, please cite the following paper



@inproceedings{Ciampi_visapp_2021,
	doi = {10.5220/0010303401850195},
	url = {https://doi.org/10.5220%2F0010303401850195},
	year = 2021,
	publisher = {{SCITEPRESS} - Science and Technology Publications},
	author = {Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato},
	title = {Domain Adaptation for Traffic Density Estimation},
	booktitle = {Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications}
}



and this Zenodo Dataset



@dataset{ciampi_gta_6560038,
author={Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato},
  title        = {{Grand Traffic Auto (GTA) Dataset: a Collection of Synthetic Images for Vehicle Detection, Segmentation and Counting}},
  month        = may,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {1.0.0},
  doi          = {10.5281/zenodo.6560038},
  url          = {https://doi.org/10.5281/zenodo.6560038}
}



 

Contact Information

If you would like further information about the dataset or if you experience any issues downloading files, please contact us at luca.ciampi@isti.cnr.it
DOI: 10.5281/zenodo.6560038
title: Grand Traffic Auto (GTA) Dataset: a Collection of Synthetic Images for Vehicle Detection, Segmentation and Counting

description: The Dataset

A synthetic dataset for vehicle detection, segmentation, and counting. It comprises images extracted from the highly photo-realistic video game Grand Theft Auto V developed by Rockstar North. Each image is labeled by the game engine providing pixel-wise masks and bounding boxes localizing vehicle instances.The dataset includes about 10k synthetic images depicting several urban scenarios with various background scenes, lighting, camera positions, traffic densities, and weather conditions. In total, we labeled more than 411,000 vehicles.

We provide two annotation files:





coco_annotations.json --> JSON file that follows the golden standard MS COCO data format (for more info see https://cocodataset.org/#format-data). All the vehicles are labeled with the COCO category 'car'. It is suitable for vehicle detection and instance segmentation.




dot_annotations.csv --> CSV file that contains xy coordinates of the centroids of the vehicles. Dot annotation is commonly used for the visual counting task.



 

Citing our work

If you found this dataset useful, please cite the following paper



@inproceedings{Ciampi_visapp_2021,
	doi = {10.5220/0010303401850195},
	url = {https://doi.org/10.5220%2F0010303401850195},
	year = 2021,
	publisher = {{SCITEPRESS} - Science and Technology Publications},
	author = {Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato},
	title = {Domain Adaptation for Traffic Density Estimation},
	booktitle = {Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications}
}



and this Zenodo Dataset



@dataset{ciampi_gta_6560038,
author={Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato},
  title        = {{Grand Traffic Auto (GTA) Dataset: a Collection of Synthetic Images for Vehicle Detection, Segmentation and Counting}},
  month        = may,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {1.0.0},
  doi          = {10.5281/zenodo.6560038},
  url          = {https://doi.org/10.5281/zenodo.6560038}
}



 

Contact Information

If you would like further information about the dataset or if you experience any issues downloading files, please contact us at luca.ciampi@isti.cnr.it
DOI: 10.21233/dxyb-cs11
title: Peskowesk Lake diatom dataset

description: Raw data for the Peskowesk Lake diatom dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.24403/jp.1459893
title: Kompetenzen fördern, Netzwerke bilden und Bibliotheken sichtbar machen : die Code Week Niedersachsen unterstützt die digitale Bildung von Kindern und Jugendlichen / Nils Arndt, Martina Schürmann
DOI: 10.24403/jp.1459902
title: Sichere Räume für Kinder und Jugendliche : Schutzkonzepte in Bibliotheken / Mareen Reichardt
DOI: 10.24403/jp.1459891
title: Erstmal scheitern : wie sich Schreibkompetenzen Studierender stärken lassen / Lukas Musumeci, Mirjam Schubert
DOI: 10.24403/jp.1459889
title: Alles »kafkaesk«? : ein kritischer Rückblick auf das Kafka-Jahr 2024, die Veränderungen der Lesekultur und die Probleme der Bibliotheken / Jan-Pieter Barbian
DOI: 10.21233/xxf5-z557
title: Beaver Lake water chemistry dataset

description: Raw data for the Beaver Lake water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.5281/zenodo.15365030
title: RVUsersParkingReviews
DOI: 10.5281/zenodo.15365031
title: RVUsersParkingReviews
DOI: 10.7910/dvn/mvvxpf
title: Replication Data for: Understanding Policy Instrument Preferences under Conflicting Beliefs and Uncertainty

description: Anticipating policy instrument preferences can be an important step in policy design to address pressing sustainability problems. But studying preferences for policy instruments is a difficult task because sustainability problems involve a non-negligible degree of trade-offs and uncertainty. We therefore study the role of actors' underlying ideologies (policy core beliefs) and risk attitudes in forming their preferences for different instruments. Combining the advocacy coalition framework with multi-attribute utility theory, both ideologies and attitudes toward uncertain policy consequences can influence instrument preferences. So far, policy studies literature has paid little attention to trade-offs between policy core beliefs or risk attitudes. Using Bayesian regression models on data from actors in Swiss pesticide risk reduction policy, we found that attitudes toward trade-offs and risk are indeed relevant to explain preferences for different regulatory and market-based instruments addressing agricultural pesticide use. Therefore, when designing policies for sustainability problems, considering the relative importance of policy core beliefs for different actors can help to find effective and broadly supported solutions. In addition, risk attitudes should be considered when policy design involves more coercive and stimulative policy instruments.
DOI: 10.21233/hegr-j434
title: Moidach More testate amoebae surface sample dataset

description: Raw data for the Moidach More testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.7910/dvn/83hmgq
title: Data for 'Measuring intramolecular connectivity in long RNA molecules using two-dimensional DNA patch-probe arrays'

description: Data from the experiments discussed in the paper, as well as MCMC samples from Bayesian analysis. See publication for more details.
DOI: 10.21233/gtrf-jb32
title: Makokou pollen surface sample dataset

description: Raw data for the Makokou pollen surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/e6be-ca31
title: Noordzee Fugro K14-2 pollen dataset

description: Raw data for the Noordzee Fugro K14-2 pollen dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/6s60-tz38
title: MD95-2002 dinoflagellates dataset

description: Raw data for the MD95-2002 dinoflagellates dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.