cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1594/pangaea.940755
title: Concentrations of 366 emerging chemicals in 56 European wastewater treatment plant effluents by LC-HRMS target screening

description: 499 emerging chemicals (ECs) were investigated in 56 effluent samples from 52 European wastewater treatment plants (WWTPs). The samples were collected between August 2017 and April 2019, using on-site large volume solid phase extraction (LVSPE) (Schulze et al., 2017). Chemical target screening by liquid chromatography high-resolution mass spectrometry (LC-HRMS) resulted in 366 detected compounds. Different capacities and conventional treatment technologies, as well as two advanced treatment technologies are included. One sample was taken at a WWTP with activated carbon (AC) treatment. Three WWTPs applying ozonation were sampled before and after this treatment step, providing two samples each. One of these WWTPs had already been sampled before the upgrade, resulting in a total of 56 (52+4) samples. More information can be found in Finckh et al. (2022). The two main objectives were (1) to extend our knowledge on chemicals occurring in treated wastewater, and (2) to identify and prioritize compounds of concern based on three different risk assessment approaches for the identification of consensus mixture risk drivers of concern.
DOI: 10.1594/pangaea.950731
title: Concentrations of 56 potential endocrine disrupting chemicals in 56 European wastewater treatment plant effluents by LC-HRMS adn LC-MS/MS analysis

description: 79 potential endocrine disrupting chemicals (EDCs) were investigated in 56 effluent samples from 52 European wastewater treatment plants (WWTPs). The samples were collected between August 2017 and April 2019, using on-site large volume solid phase extraction (LVSPE) (Schulze et al., 2017). Chemical target analysis by liquid chromatography high-resolution and tandem mass spectrometry (LC-HRMS and LC-MS/MS, respectively) resulted in 56 detected compounds. Different capacities and conventional treatment technologies, as well as two advanced treatment technologies are included. One sample was taken at a WWTP with activated carbon treatment. Three WWTPs applying ozonation were sampled before and after this treatment step, providing two samples each. One of these WWTPs had already been sampled before the upgrade, resulting in a total of 56 (52+4) samples. More information can be found in Finckh et al. (2022). The two main objectives of this study were (i) to compare three different receptor-based estrogenicity assays (ERα-GeneBLAzer, p-YES, ERα-CALUX®), and (ii) to investigate a combined approach of chemical target analysis and receptor-based testing for estrogenicity, glucocorticogenic activity, androgenicity and progestagenic activity (ERα-, GR-, AR- and PR-GeneBLAzer assays, respectively) in treated wastewater.
DOI: 10.25345/c5vt1h150
title: MassIVE MSV000094306 - GNPS [D-Asp3] Microcystins increased Collision Energy
title: [D-Asp3] Microcystin-LR, [D-Asp3] Microcystin-RR, Microcystin-HilR 200ppb 2: 13C4(+)-Anatoxin-a, Homoanatoxin-a, CYN, 7-epi-CYN, 7-deoxy-CYN 200ppb Standard solutions Collision Energy: 60eV-80eV
DOI: 10.1594/pangaea.889755
title: Percentages diatom abundances from Lake Palaeo-Makgadikgadi (Botswana)

description: Diatoms from a 30-cm thick sedimentary unit have been sampled each 0.5 cm to reconstruct the late Pleistocene lake phase of Lake Palaeo-Makgadikgadi in the Middle Kalahari (Botswana).
description: Supplement to: Schmidt, Mareike; Fuchs, Markus; Henderson, Andrew C G; Kossler, Annette; Leng, Melanie J; Mackay, Anson W; Shemang, Elisha; Riedel, Frank (2017): Paleolimnological features of a mega-lake phase in the Makgadikgadi Basin (Kalahari, Botswana) during Marine Isotope Stage 5 inferred from diatoms. Journal of Paleolimnology, 58, 373-390
DOI: 10.1594/pangaea.958984
title: Gut carbon, nitrogen and phosphorus composition of Euphausia superba and Salpa thompsoni from the northern Antarctic Peninsula

description: Samples were collected between 03/26/2018 and 04/27/2018 around the northern tip of the Antarctic Peninsula (63° 0' 1.843'' S, 60° 0' 16.901''W) onboard the RV Polarstern during the PS112 campaign in order to identify the elemental composition and stoichiometry of the Antarctic krill (Euphausia superba) and salps (Salpa thompsoni). The sampling stations were situated in the survey grid of the Antarctic Marine Living Resources Program (AMLR). At the time of sampling, the study area was ice free and ambient Chlorophyll a levels were comparable to previous studies in this time frame (e.g. Schofield et al., 2017). The phytoplankton community was dominated by dinoflagellates, diatoms and prymnesiophytes, which is typical for autumn (March–May) around the NAP (Pauli et al., 2021). Krill and salps were sampled by using the 1.8 m² Isaacs-Kidd Midwater Trawl Net (IKMT) equipped with a 505 μm mesh which is suitable to collect both salps and krill in good condition. The net was towed obliquely to 170 m, or 20 m from the bottom, at a speed of 2 kts. Size in mm (using graph paper, Seibert publisher) and stage of krill and salp individuals were determined on board before further processing. Prior to the elemental analysis, the digestive system of krill and salps was dissected and stored at -20 °C to determine the elemental signature of the gut content. Salp individuals were immediately dissected on board, krill individuals were frozen on board and dissected later in the lab. Measurements of gut elemental composition were done on 50 and 16 samples for krill and salps, respectively. Prior to the analyses each sample was homogenized with 500 µl distilled water. For the carbon/nitrogen (C/N) samples, 250 µl of the homogenate were transferred into pre-weighed tin capsules while the rest of the homogenized tissue was transferred into pre-weighed glass tubes for phosphorus analysis. Samples were dried at 70 °C for three weeks prior to analysis. After drying, we measured the dry weight (DW) of all samples by using a high-resolution balance (Mettler Toledo, XP-26). After drying, all C/N samples were sealed and analyzed using a CHN analyzer (Thermo, Flash EA 1112). The phosphorus samples were combusted at 450 °C for 5 hours and the ash-free dry weight (AFDW) was measured. Particulate organic phosphorus (POP) was measured photometrically by molybdate reaction after sulfuric acid and heat digestion at 90 °C, modified after (Grasshoff et al., 2009).
DOI: 10.1594/pangaea.969134
title: Hydrographic data from the Rio Grande Cone region, southwestern Atlantic Ocean in 2022

description: This dataset contains 12 vertical profiles of eight variables: Temperature, potential temperature, conductivity, salinity, pressure, potential density, fluorescence. All data was obtained during the TRIATLAS-1, carried out from Oct 31 to Nov 9, 2022. The cruise took place in the area of the Rio Grande Cone, an important region in the southwestern Atlantic Ocean regarding sediment transport, biological production and mesoscale circulation. The cruise grid covered an area of approximately 6,500 squared kilometers, from the shelf-break to the open ocean. The cruise goal was to investigate the behavior and abundance of mesopelagic fauna in the Rio Grande Cone region and the potential of this region to be a hot spot for biological production. All data was obtained with a SeaBird SBE 25plus Sealogger CTD (conductivity, temperature and depth) and are properly treated by the manufacturer provided software.
DOI: 10.15468/dl.fahy9j
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Brazil",
 "DatasetKey is FNJV - Fonoteca Neotropical Jacques Vielliard",
 "TaxonKey is one of (Pyrrhura griseipectus Salvadori, 1900, Pyrrhura leucotis griseipectus Salvadori, 1900)"
 ]
}

 The dataset includes 14 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076692-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5519/qd.t11k8sty
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (125 records)
DOI: 10.15468/dl.4xrj8j
title: Occurrence Download

description: A dataset containing 112 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((143.61123 -40.76264,143.56527 -41.0568,144.18423 -41.1763,143.93705 -40.75856,143.61123 -40.76264))",
 "HasCoordinate is true",
 "TaxonKey is Puffinus tenuirostris (Temminck, 1836)"
 ]
}

 The dataset includes 112 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076693-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vcah99
title: Occurrence Download

description: A dataset containing 734 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Peces presentes en la colección Ictiológica CIACOL asociados a la región de Mitú, cuenca media del Río Vaupés"
 ]
}

 The dataset includes 734 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076694-240626123714530/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.25539931
title: Does the accounting of the local symmetry fragments in quasi-SMILES improve the predictive potential of the QSAR models of toxicity toward tadpoles?

description: Models of toxicity to tadpoles have been developed as single parameters based on special descriptors which are sums of correlation weights, molecular features, and experimental conditions. This information is presented by quasi-SMILES. Fragments of local symmetry (FLS) are involved in the development of the model and the use of FLS correlation weights improves their predictive potential. In addition, the index of ideality correlation (<i>IIC</i>) and correlation intensity index (<i>CII</i>) are compared. These two potential predictive criteria were tested in models built through Monte Carlo optimization. The <i>CII</i> was more effective than <i>IIC</i> for the models considered here.
DOI: 10.6084/m9.figshare.25539934.v2
title: Flow-through imaging and automated analysis of oil-exposed early stage Atlantic cod (<i>Gadus morhua</i>)

description: Toxicology studies in early fish life stages serve an important function in measuring the impact of potentially harmful substances, such as crude oil, on marine life. Morphometric analysis of larvae can reveal the effects of such substances in retarding growth and development. These studies are labor intensive and time consuming, typically resulting in only a small number of samples being considered. An automated system for imaging and measurement of experimental animals, using flow-through imaging and an artificial neural network to allow faster sampling of more individuals, has been described previously and used in toxicity experiments. This study compares the performance of the automated imaging and analysis system with traditional microscopy techniques in measuring biologically relevant endpoints using two oil treatments as positive controls. We demonstrate that while the automated system typically underestimates morphometric measurements relative to analysis of manual microscopy images, it shows similar statistical results to the manual method when comparing treatments across most endpoints. It allows for many more individual specimens to be sampled in a shorter time period, reducing labor requirements and improving statistical power in such studies, and is noninvasive allowing for repeated sampling of the same population.
DOI: 10.6084/m9.figshare.25562392
title: A QSAR study for predicting malformation in zebrafish embryo

description: Developmental toxicity tests are extremely expensive, require a large number of animals, and are time-consuming. It is necessary to develop a new approach to simplify the analysis of developmental endpoints. One of these endpoints is malformation, and one group of ongoing methods for simplifying is <i>in silico</i> models. In this study, we aim to develop a quantitative structure–activity relationship (QSAR) model and identify the best algorithm for predicting malformations, as well as the most important and effective physicochemical properties associated with malformation. The dataset was extracted from a reliable database called COMPTOX. Physicochemical properties (descriptors) were calculated using Mordred and RDKit chemoinformatics software. The data were cleaned, preprocessed, and then split into training and testing sets. Machine learning algorithms, such as gradient boosting model (GBM) and logistic regression (LR), as well as deep learning models, including multilayer perceptron (MLP) and neural networks (NNs) trained with train set data and different sets of descriptors. The models were then validated with test set and various statistical parameters, such as Matthew’s correlation coefficient (MCC) and balanced accuracy (BAC) score, were used to compare the models. A set of descriptors containing with 78% AUC was identified as the best set of descriptors. Gradient boosting was determined to be the best algorithm with 78% predictive power. The descriptors that were the most effective for developing models directly impact the mechanism of malformation, and GBM is the best model due to its MCC and BAC.
DOI: 10.6084/m9.figshare.25562392.v2
title: A QSAR study for predicting malformation in zebrafish embryo

description: Developmental toxicity tests are extremely expensive, require a large number of animals, and are time-consuming. It is necessary to develop a new approach to simplify the analysis of developmental endpoints. One of these endpoints is malformation, and one group of ongoing methods for simplifying is <i>in silico</i> models. In this study, we aim to develop a quantitative structure–activity relationship (QSAR) model and identify the best algorithm for predicting malformations, as well as the most important and effective physicochemical properties associated with malformation. The dataset was extracted from a reliable database called COMPTOX. Physicochemical properties (descriptors) were calculated using Mordred and RDKit chemoinformatics software. The data were cleaned, preprocessed, and then split into training and testing sets. Machine learning algorithms, such as gradient boosting model (GBM) and logistic regression (LR), as well as deep learning models, including multilayer perceptron (MLP) and neural networks (NNs) trained with train set data and different sets of descriptors. The models were then validated with test set and various statistical parameters, such as Matthew’s correlation coefficient (MCC) and balanced accuracy (BAC) score, were used to compare the models. A set of descriptors containing with 78% AUC was identified as the best set of descriptors. Gradient boosting was determined to be the best algorithm with 78% predictive power. The descriptors that were the most effective for developing models directly impact the mechanism of malformation, and GBM is the best model due to its MCC and BAC.
DOI: 10.6084/m9.figshare.25539931.v2
title: Does the accounting of the local symmetry fragments in quasi-SMILES improve the predictive potential of the QSAR models of toxicity toward tadpoles?

description: Models of toxicity to tadpoles have been developed as single parameters based on special descriptors which are sums of correlation weights, molecular features, and experimental conditions. This information is presented by quasi-SMILES. Fragments of local symmetry (FLS) are involved in the development of the model and the use of FLS correlation weights improves their predictive potential. In addition, the index of ideality correlation (<i>IIC</i>) and correlation intensity index (<i>CII</i>) are compared. These two potential predictive criteria were tested in models built through Monte Carlo optimization. The <i>CII</i> was more effective than <i>IIC</i> for the models considered here.
DOI: 10.6084/m9.figshare.25562392.v1
title: A QSAR study for predicting malformation in Zebrafish embryo

description: Background Developmental toxicity tests are extremely expensive, require a large number of animals, and are time-consuming. It is necessary to develop a new approach to simplify the analysis of developmental endpoints. One of these endpoints is malformation, and one group of ongoing methods for simplifying is in silico models. In this study, we aim to develop a Quantitive Structure- Activity Relationship (QSAR) model and identify the best algorithm for predicting malformations, as well as the most important and effective physicochemical properties associated with malformation. Methods The dataset was extracted from a reliable database called COMPTOX. Physicochemical properties (descriptors) were calculated using Mordred and RDKit chemoinformatic software. The data were cleaned, preprocessed, and then split into training and testing sets. Machine learning algorithms, such as Gradient Boosting (GBM) and logistic regression (LR), as well as deep learning models, including multilayer perceptron (MLP) and neural networks (NN) trained with train set data and different sets of descriptors. The models were then validated with test set and various statistical parameters, such as Matthew’s correlation coefficient (MCC) and balanced accuracy score, were used to compare the models Results A set of descriptors containing with 78% AUC was identified as the best set of descriptors. Gradient Boosting was determined to be the best algorithm with 78% predictive power. Conclusion The descriptors that were the most effective for developing models directly impact the mechanism of malformation, and gradient boosting is the best model due to its Matthews correlation coefficient (MCC) and balanced accuracy (BAC).
DOI: 10.15468/dl.5uyybg
title: Occurrence Download

description: A dataset listing the 170 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is Peces presentes en la colección Ictiológica CIACOL asociados a la región de Mitú, cuenca media del Río Vaupés"
 ]
}

 The dataset's 170 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076695-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.he4c9g
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "DatasetKey is Plant Specimen from Herbarium (CDBI) in China, Chengdu Institute of Botany, Chinese Academy of Sciences",
 "TaxonKey is Hordeum vulgare var. nudum Döll, 1880"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076696-240626123714530/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.26789680
title: Validation of ‘Creactability’ Scale in Football: A Rasch Modeling Approach_data_football.xlsx

description: The raw data for the study "Validation of ‘Creactability’ Scale in Football: A Rasch Modeling Approach."
DOI: 10.6084/m9.figshare.26789680.v3
title: Validation of ‘Creactability’ Scale in Football: A Rasch Modeling Approach_data_football.xlsx

description: The raw data for the study "Validation of ‘Creactability’ Scale in Football: A Rasch Modeling Approach."
DOI: 10.6084/m9.figshare.26789680.v2
title: data_football.xlsx

description: The raw data for the study "Validation of ‘Creactability’ Scale in Football: A Rasch Modeling Approach."
DOI: 10.5281/zenodo.11908582
title: Image data of bright deposits in permanently shadowed craters on Ceres

description: These are image data described in "Spectral properties of bright deposits in permanently shadowed craters on Ceres" by Schröder et al., published as Astronomy & Astrophysics 688 (2024) A178, doi:10.1051/0004-6361/202450247. The data correspond to the permanently shadowed craters on Ceres shown in Figs. 2 to 6 in the paper.

There is a data set for each of the following permanently shadowed regions: NP04, NP05, NP07, and SP01. Each data set consists of 4 parts: (1) unprojected images in IMG format (header + binary), (2) unprojected images in FITS format, (3) projected images in IMG format, and (4) projected images in FITS format. The images contain reflectance values (I/F) in floating-point format. They are corrected for in-field stray light and have been registered to a global shape model to correct minor pointing errors. The data files are equipped with a rudimentary header.

File name syntax: In "FC21B0045550_15282040820F2D.IMG", "FC2" is the camera model, "45550" is the image number, and "F2" is the filter.
DOI: 10.5281/zenodo.11908583
title: Image data of bright deposits in permanently shadowed craters on Ceres

description: These are image data described in "Spectral properties of bright deposits in permanently shadowed craters on Ceres" by Schröder et al., published as Astronomy & Astrophysics 688 (2024) A178, doi:10.1051/0004-6361/202450247. The data correspond to the permanently shadowed craters on Ceres shown in Figs. 2 to 6 in the paper.

There is a data set for each of the following permanently shadowed regions: NP04, NP05, NP07, and SP01. Each data set consists of 4 parts: (1) unprojected images in IMG format (header + binary), (2) unprojected images in FITS format, (3) projected images in IMG format, and (4) projected images in FITS format. The images contain reflectance values (I/F) in floating-point format. They are corrected for in-field stray light and have been registered to a global shape model to correct minor pointing errors. The data files are equipped with a rudimentary header.

File name syntax: In "FC21B0045550_15282040820F2D.IMG", "FC2" is the camera model, "45550" is the image number, and "F2" is the filter.
DOI: 10.15468/dl.czkphn
title: Occurrence Download

description: A dataset containing 755 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Amaranthus spinosus L.",
 "Year 2019-2024"
 ]
}

 The dataset includes 755 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076698-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4xyq5k
title: Occurrence Download

description: A dataset containing 209682 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "Year is 2023",
 "GadmGid is one of (COL.8.9_2)"
 ]
 }
 ]
}

 The dataset includes 209682 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076697-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nupkm3
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Brazil",
 "DatasetKey is Field Museum of Natural History (Zoology) Bird Collection",
 "TaxonKey is one of (Pyrrhura griseipectus Salvadori, 1900, Pyrrhura leucotis griseipectus Salvadori, 1900)"
 ]
}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0076699-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.