cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17937297
title: Conductivity Temperature Depth (CTD) Data sampled on the course OC4920 (Observing the Ocean from Micro to Macro Scale) with R/V Skagerak, May 2025.

description: Cruise: 2505Title: OC4920Start: 2025-05-05End: 2025-05-08Area: Skagerrak (SDN:C16::03), Kattegat (SDN:C16::02)PI: Sebastiaan  Swart <sebastiaan.swart@gu.se>Org: University of Gothenburg

Funding:The data collected is enabled through the financial support of the Skagerak Facility and the UGot Faculty of Science and Technology

Description:OC4920 is a Masters course in Oceanography of the Marine Sciences program at GU. The course will provide an understanding of the ocean processes that impact its  motion (e.g. circulation and property exchange) and the scales that they occur, that are relevant for 21st century science. The course will attempt to have a overall view of the ocean, in the sense that a number of oceanic domains will be incorporated and taught on. The course will cover the importance of the different scales that processes occur in space and time, such as from micro- scale mixing processes to the larger geostrophic flows. The course will include a field work element, where students are expected to take the knowledge from the course to design a small field experiment using a research ship and scientific equipment/sensors. The students will then use the collected field observations to complete tasks in analyzing and interpreting the data.

 

Lat           Long      Cast57.5592 11.5227 SBE09_0789_20250506_0908_7720_2522_00157.3878 10.9145 SBE09_0789_20250506_1138_7720_2522_00257.4560 10.9170 SBE09_0789_20250506_1258_7720_2522_00357.5575 10.9285 SBE09_0789_20250506_1352_7720_2522_00457.9482 10.7845 SBE09_0789_20250507_0844_7720_2522_00557.9317 10.7827 SBE09_0789_20250507_0915_7720_2522_00657.9138 10.7795 SBE09_0789_20250507_0938_7720_2522_00757.8978 10.7805 SBE09_0789_20250507_1006_7720_2522_00857.8808 10.7790 SBE09_0789_20250507_1044_7720_2522_00957.8635 10.7767 SBE09_0789_20250507_1111_7720_2522_01057.8473 10.7783 SBE09_0789_20250507_1130_7720_2522_01157.8305 10.7748 SBE09_0789_20250507_1152_7720_2522_01257.8137 10.7713 SBE09_0789_20250507_1213_7720_2522_01357.7973 10.7698 SBE09_0789_20250507_1238_7720_2522_01457.7797 10.7718 SBE09_0789_20250507_1257_7720_2522_01557.7613 10.7678 SBE09_0789_20250507_1317_7720_2522_01657.9102 11.1208 SBE09_0789_20250508_0920_7720_2522_01757.5590 11.5233 SBE09_0789_20250508_1212_7720_2522_018
DOI: 10.5281/zenodo.17939830
title: Spatial patterns and determinants of multidimensional beta diversity in the United States freshwater fishes
DOI: 10.5281/zenodo.17939831
title: Spatial patterns and determinants of multidimensional beta diversity in the United States freshwater fishes
DOI: 10.15468/dl.ku22rs
title: Occurrence Download

description: A dataset containing 22881 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "TaxonKey is Vanessa atalanta",
 "Year 2002-2002"
 ]
}

 The dataset includes 22881 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053583-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.txku56
title: Occurrence Download

description: A dataset containing 517 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Canada",
 "OccurrenceStatus is Present",
 "TaxonKey is Pectenia plumbea"
 ]
}

 The dataset includes 517 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053584-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30884861
title: Unravelling unknown aspects of the biology of short-spined thrips

description: The arrival of the exotic pest <i>Thrips parvispinus</i> (Karny) to the continental US and South Florida has raised several questions about its biology. Several laboratory experiments were conducted in this study to evaluate the response of <i>T. parvispinus</i> to South Florida temperatures and cold fronts, reproduction mode, pupation site, and survival on different food sources. Life table parameters were used to calculate the developmental time and reproductive parameters of <i>T. parvispinus</i> when fed on beans (<i>Phaseolus vulgaris</i> L.). At the temperatures of 17, 22, 27 and 32 °C, this pest was able to develop and reproduce, but at 12 °C, eggs failed to hatch. The average annual temperature (27 °C) of South Florida provided the best conditions to develop and reproduce, with a life cycle of 12.77 ± 0.31 d and the highest intrinsic rate of increase. Thrips mortality increased significantly with exposure to low temperatures (5, 10 or 15 °C), reaching 100% at 5 °C after 5 d of exposure. Experiments also showed that in the absence of leaf tissue, <i>T. parvispinus</i> is less likely to survive more than 24 h and that it pupates in the soil at an average depth of 2.5 cm. Furthermore, reproductive biology bioassays indicated that the Florida <i>T. parvispinus</i> population reproduces through arrhenotokous parthenogenesis. These results address knowledge gaps regarding the <i>T. parvispinus</i> biology and its potential for expansion to new areas and provide the foundation for the development of an IPM program.
DOI: 10.6084/m9.figshare.30884861.v1
title: Unravelling unknown aspects of the biology of short-spined thrips

description: The arrival of the exotic pest <i>Thrips parvispinus</i> (Karny) to the continental US and South Florida has raised several questions about its biology. Several laboratory experiments were conducted in this study to evaluate the response of <i>T. parvispinus</i> to South Florida temperatures and cold fronts, reproduction mode, pupation site, and survival on different food sources. Life table parameters were used to calculate the developmental time and reproductive parameters of <i>T. parvispinus</i> when fed on beans (<i>Phaseolus vulgaris</i> L.). At the temperatures of 17, 22, 27 and 32 °C, this pest was able to develop and reproduce, but at 12 °C, eggs failed to hatch. The average annual temperature (27 °C) of South Florida provided the best conditions to develop and reproduce, with a life cycle of 12.77 ± 0.31 d and the highest intrinsic rate of increase. Thrips mortality increased significantly with exposure to low temperatures (5, 10 or 15 °C), reaching 100% at 5 °C after 5 d of exposure. Experiments also showed that in the absence of leaf tissue, <i>T. parvispinus</i> is less likely to survive more than 24 h and that it pupates in the soil at an average depth of 2.5 cm. Furthermore, reproductive biology bioassays indicated that the Florida <i>T. parvispinus</i> population reproduces through arrhenotokous parthenogenesis. These results address knowledge gaps regarding the <i>T. parvispinus</i> biology and its potential for expansion to new areas and provide the foundation for the development of an IPM program.
DOI: 10.15468/dl.r9jchm
title: Occurrence Download

description: A dataset containing 159 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Swedish Malaise Trap Project (SMTP) - Ismaridae"
 ]
}

 The dataset includes 159 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053586-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.va3h23
title: Occurrence Download

description: A dataset containing 24736 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tetrax tetrax"
 ]
}

 The dataset includes 24736 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053587-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5255/ukda-sn-858169
title: ProTechThem: Digital Harms of Sharenting. Survey and Interview Data, UK and Italy, 2022-2024

description: This data collection brings together four complementary datasets on parents’ practices and perceptions of sharing information about their children online (“sharenting”), combining nationally weighted surveys with in-depth qualitative interviews in the UK and Italy. The motivation is to provide a robust, comparative evidence base on how and why parents post (or avoid posting) content about children on social media and online platforms, how they assess and manage privacy and risk, and what harms or negative experiences they and their families may encounter. By integrating large-scale survey data with interview narratives, the collection supports both population-level description and detailed exploration of decision-making, norms, and lived experience across two national contexts.

Two survey datasets capture patterns of posting behaviour, platform use, motivations, privacy settings and protective strategies, perceptions of risk and concern, exposure to safety information, and experiences of online harms. The UK survey includes responses from 1,013 parents recruited via the YouGov panel and weighted to be representative of UK adults with children under 18; the Italian survey includes 506 parents recruited via the YouGov Italian panel and weighted to be representative of Italian adults with children under 18. Both surveys are provided as SPSS files with accompanying variable lists and the full questionnaires (including routing/logic), enabling replication and cross-national comparison.

The qualitative component comprises 30 semi-structured interviews with UK parents (collected February 2023-January 2024) and 22 interviews with Italian participants. The Italian interviews include two strands: interviews with moderators/administrators of parenting-focused Facebook groups (and one parent user) conducted in mid-2022, and interviews with Italian parents conducted December 2023-February 2024 using a revised guide to mirror the UK parent interviews. Across countries, interviews explore sharenting practices, attitudes to privacy and risk, strategies for controlling audiences and information flows, experiences of harm, and how norms are shaped within families and online communities. All transcripts are anonymised, and the Italian materials include both original-language transcripts and English translations, alongside documentation on recruitment, consent, and topic guides. Together, these four datasets enable comparative research on digital parenting practices, perceived and experienced risks to children, and the role of platforms and online communities in shaping what is shared about children and how parents attempt to keep them safe.
DOI: 10.15468/dl.patu64
title: Occurrence Download

description: A dataset containing 7991 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Spain",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Panurus biarmicus"
 ]
}

 The dataset includes 7991 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053588-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zkh688
title: Occurrence Download

description: A dataset containing 75663 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "OccurrenceStatus is Present",
 "TaxonKey is Microtus agrestis"
 ]
}

 The dataset includes 75663 records from 596 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053567-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16615101
title: Vero Cell Culture Image Dataset

description: This dataset offers a collection of light microscope and transmission electron microscope (TEM) images of Vero E6 cell cultures , primarily aimed at researchers in biology and artificial intelligence. The data facilitates the study of how subtle variations in culture conditions impact cell morphology and can be used for training AI models in bioimage analysis.

The experiment consisted of two main stages: 



Preparation Stage: Vero E6 cells (ATCC-CRL-1586) were thawed and passaged until healthy.

Experiment Stage: Cell cultures were split into two distinct paths, differing only by the concentration of fetal bovine serum (FBS) in the growth medium (Medium 1 with 10% FBS vs. Medium 2 with 2% FBS).


Light microscope images were captured daily throughout both the preparation and experiment stages.  Images were obtained using an EVOS FL microscope with Bright Field or Phase illumination and 10x or 20x objectives.

TEM images were acquired from one sample per path at the conclusion of the experimental stage, using a Tecnai G2 Spirit BioTWIN microscope.

A comprehensive procedure description document is included with this dataset, providing full details on materials, methods, equipment specifications, and subculturing procedures. The Contract Research Organization (CRO) passage tracking and light microscope image descriptions are provided as separate documents. No descriptios of TEM images is provided, except the image file name.
DOI: 10.25976/r245-z904
title: LaHave River Water Quality Monitoring

description: Coastal Action initiated the LaHave River Watershed Project (LRWP) in 2007, in response to increasing public concern over the health of the river, with a goal of identifying and reducing environmental degradation in the watershed.  Over the years, this project has involved water quality monitoring, habitat assessment and restoration, invasive species research, and community outreach.  The LRWP is guided by an advisory committee comprised of government, academic, industry, non-profit, and citizen representatives and is funded by multiple partners.
description: A YSI Pro-DSS and Pro Plus are used to collect the water's physical characteristics in-situ. The probe's pH and conductivity are calibrated monthly, while DO% is calibrated once before each field day, and twice-more throughout the sample collection process. All chemical and biological samples are sent to an accredited laboratory for analysis, where samples are also subject to the lab's QAQC. Samples are held on ice until delivery to the lab - within 24 hours of collection.
DOI: 10.5281/zenodo.17939870
title: Energy Data from Danish Energy Community

description: These anonymized data show detailed 5-minute data from a well established energy community in Denmark, with 12 HP, 14 PV, 8 EV chargers and a BESS. Data can be accessed through the NEOGRID Webportal and/or via the NEOGRID OpenAPI.
DOI: 10.5281/zenodo.17939869
title: Energy Data from Danish Energy Community

description: These anonymized data show detailed 5-minute data from a well established energy community in Denmark, with 12 HP, 14 PV, 8 EV chargers and a BESS. Data can be accessed through the NEOGRID Webportal and/or via the NEOGRID OpenAPI.
DOI: 10.34740/kaggle/dsv/14165120
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.9grarx
title: Occurrence Download

description: A dataset containing 136 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Swedish Malaise Trap Project (SMTP) - Heloridae"
 ]
}

 The dataset includes 136 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053591-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3217/36pqd-6n721
title: Entanglement Hamiltonian after a local quench

description: This data set contains the data used to produce the figures of the preprint arXiv:2508.19406. The top level directory contains the python notebook script (see e.g. https://ipynb.js.org/) for the figures, while the subdirectories contain all the corresponding data files.
DOI: 10.3217/avqgc-d1z32
title: Entanglement Hamiltonian after a local quench

description: This data set contains the data used to produce the figures of the preprint arXiv:2508.19406. The top level directory contains the python notebook script (see e.g. https://ipynb.js.org/) for the figures, while the subdirectories contain all the corresponding data files.
DOI: 10.15468/dl.7qsdjz
title: Occurrence Download

description: A dataset containing 5734 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "OccurrenceStatus is Present",
 "TaxonKey is Anser cygnoides"
 ]
}

 The dataset includes 5734 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053592-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30884744
title: Raw outputs for Simulation Study 2: Error Rate

description: The folders contain all the raw output of the second simulation study from the paper:<br>"Generalization in neural posterior estimation: Case studies with the racing diffusion model".<br>The structure of the folders is as follows:<br><br>- `experiment_*`: Study folder<br> - `rdm_*`: Study condition folder (e.g., rdm_simple refers to the default settings)<br> - `flow_matching`: Output related to neural posterior estimation (NPE)<br> - `checkpoints`: Includes a `model.keras` file that contains the checkpoint of the NPE<br> for this condition<br> - `metrics`: Aggregated metrics based on posterior samples from NPE and MCMC<br> - `npe_samples`: Raw posterior samples from NPE<br> - `mcmc_samples`: Raw posterior samples obtained from Markov chain Monte Carlo (MCMC)<br> - `test_data`: Test datasets<br>The names of the study conditions are as follows:<br><br>- `experiment_2`:<br> - `rdm_simple`: Default settings with `mu_v_slope=1.5` and `beta_b=0.15` (condition medium-fixed)<br> - `rdm_simple_lower`: Default settings with `mu_v_slope=2.5` and `beta_b=0.25` (condition lower-fixed)<br> - `rdm_simple_meta`: Default settings with `mu_v_slope~U(0.5, 2.5)` and `beta_b~U(0.05, 0.25)` (condition full-varying)<br> - `rdm_simple_lower`: Default settings with `mu_v_slope~U(0.5, 1.5)` and `beta_b~U(0.05, 0.15)` (condition lower-varying)<br> - `rdm_simple_upper`: Default settings with `mu_v_slope~U(1.5, 2.5)` and `beta_b~U(0.15, 0.25)` (condition upper-varying)<br> - `rdm_simple_upper`: Default settings with `mu_v_slope=0.5` and `beta_b=0.05` (condition upper-fixed)<br><br>All datasets and posterior samples are stored in HDF5 files. Aggregated statistics are stored in CSV format.
DOI: 10.6084/m9.figshare.30884744.v1
title: Raw outputs for Simulation Study 2: Error Rate

description: The folders contain all the raw output of the second simulation study from the paper:<br>"Generalization in neural posterior estimation: Case studies with the racing diffusion model".<br>The structure of the folders is as follows:<br><br>- `experiment_*`: Study folder<br> - `rdm_*`: Study condition folder (e.g., rdm_simple refers to the default settings)<br> - `flow_matching`: Output related to neural posterior estimation (NPE)<br> - `checkpoints`: Includes a `model.keras` file that contains the checkpoint of the NPE<br> for this condition<br> - `metrics`: Aggregated metrics based on posterior samples from NPE and MCMC<br> - `npe_samples`: Raw posterior samples from NPE<br> - `mcmc_samples`: Raw posterior samples obtained from Markov chain Monte Carlo (MCMC)<br> - `test_data`: Test datasets<br>The names of the study conditions are as follows:<br><br>- `experiment_2`:<br> - `rdm_simple`: Default settings with `mu_v_slope=1.5` and `beta_b=0.15` (condition medium-fixed)<br> - `rdm_simple_lower`: Default settings with `mu_v_slope=2.5` and `beta_b=0.25` (condition lower-fixed)<br> - `rdm_simple_meta`: Default settings with `mu_v_slope~U(0.5, 2.5)` and `beta_b~U(0.05, 0.25)` (condition full-varying)<br> - `rdm_simple_lower`: Default settings with `mu_v_slope~U(0.5, 1.5)` and `beta_b~U(0.05, 0.15)` (condition lower-varying)<br> - `rdm_simple_upper`: Default settings with `mu_v_slope~U(1.5, 2.5)` and `beta_b~U(0.15, 0.25)` (condition upper-varying)<br> - `rdm_simple_upper`: Default settings with `mu_v_slope=0.5` and `beta_b=0.05` (condition upper-fixed)<br><br>All datasets and posterior samples are stored in HDF5 files. Aggregated statistics are stored in CSV format.
DOI: 10.5281/zenodo.17900480
title: Training data and simulations; Modeling uncertainty with engression: a deep generative time-series approach

description: This collection contains training data and simulations for the paper:

Basil Kraft, Steven Stalder, William Hugo Aeberhard, Nicolás Herrington Ruiz, Nicolai Meinshausen, Xinwei Shen, and Lukas Gudmundsson: Modeling uncertainty with engression: a deep generative time-series approach. Authorea. May 27, 2025.



data.zip (harmonized_basins.zarr): training data

engression_runs.zip: engression runs

quantiles_runs.zip: quantile regression runs


We recommend using this training data only to reproduce our work. In this case, please cite all original datasets (see below). Otherwise, it is highly recommended to use the CAMELS-CH dataset directly.

The training data contains data from the following sources, aggregated to catchment scale:



CAMELS-CH data  



https://zenodo.org/records/15025258 



Static catchment properties:



The New Swiss Glacier Inventory SGI2016: From a Topographical to a Glaciological Dataset, Linsbauer et al (2021): 10.3389/feart.2021.704189

Hochauflösende Bodenkarten für den Schweizer Wald, Baltensweiler et al. (2022): 10.3188/szf.2022.0288

The Habitat Map of Switzerland: A Remote Sensing, Composite Approach for a High Spatial and Thematic Resolution Product: Price et al. (2023), 10.3390/rs15030643

swissALTI3D, swisstopo (2018): https://www.swisstopo.admin.ch/en/height-model-swissalti3d#Additional-information, last access: 1 April 2024

SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, Poggio et al. (2021): 10.5194/soil-7-217-2021



Meteorological features were retrieved from MeteoSwiss.



https://www.meteoschweiz.admin.ch/dam/jcr:818a4d17-cb0c-4e8b-92c6-1a1bdf5348b7/ProdDoc_TabsD.pdf, last access: 1 April 2024

https://www.meteoschweiz.admin.ch/dam/jcr:4f51f0f1-0fe3-48b5-9de0-15666327e63c/ProdDoc_RhiresD.pdf, last access: 1 April 2024