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1,848 results for “Toxicity”
Microcystin-LR (one of the toxic congeners of microcystin) exposure causes crayfish to forget previous fight outcomes as well as previous opponents, MI, 2025
Harmful algal blooms are increasing across the globe as a function of nutrient run off and climate change. While the study of the mechanisms that underlie the blooms themselves are abundant, the impact of the toxins produced by blooms needs more research. Crayfish are keystone species in aquatic habitats and rely on chemoreception for many of their ecosystem functions. Crayfish were placed in a fight arena for measure both the outcome and dynamics aspects of agonistic encounters. Then the loser of the fights were exposed to either controls or microcystin for four days. Afterwards, the crayfish fought either another unfamiliar opponent or the previous opponent. The data contained in this package has the concentrations of microcystin exposure, morphological data on the crayfish, and then behavioral data from the two different fights. The behavioral data has two categories: time spent at different levels of intensity and time it took the crayfish pair to reach a certain intensity.
Shifts in Bacterioplankton During Cyanobacterial Blooms Reflect Bloom Toxicity and lake Trophic State, OR 2019-2020
Harmful cyanobacterial blooms (cyanoHABs) typically occur in human-impacted eutrophic lakes suffering from nutrient pollution, but they also occur in lakes spanning the trophic and disturbance gradients. CyanoHABs change the bacterioplankton community structure with increases in specific cyanobacteria strains, as well as shifts in heterotrophic taxa. Bacterioplankton community shifts during cyanoHABs can be somewhat predictable but have been only studied in a limited number of lakes, most highly productive and in developed watersheds. The Cascade Mountains (USA) offer an unique area to study cyanotoxin variation and shifts in bacterioplankton composition across a productivity gradient in lakes with documented cyanoHABs but removed from most development. We explored associations of bacterioplankton communities with cyanoHABs and toxins within a season, as well as across lakes and years via physicochemical metrics, passive toxin samplers and 16S rRNA gene sequencing. The data set is a compilation of physicochemical, meteorological, biological as well as physical lake characteristics and sampling information. Water temperature was tracked continuously within a season for the three lakes while single point measurements of water temperature were taken for the other lakes in the spatial (n= 29) and intra-annual subset (n =12). Daily air temperature, precipitation and aerosol optical depth were extracted from the PRISM Gridded Climate data. Nutrient concentrations were measured for all lakes and analyzed for nitrogen and phosphorus via colorimetry in a flow analyzer. Chlorophyll-a concentrations were measured from filter samples via fluorimetry. Microcystin concentrations from grab and SPATT samples were analyzed via an ELISA kit for Microcystin-LR. Bacterioplankton diversity metrics were calculated from the processed 16S rRNA sequences along with the relative abundance of potentially toxigenic cyanobacteria. Bacterioplankton composition was and can be derived from the raw
CompTox Zebrafish developmental toxicity processed data
<p>Please cite the published paper and original data source.</p> <p>A classification data set for 19 endpoints of Zebrafish developmental toxicity for QSAR modelling.</p> <p>The original data was downloaded from <a href="https://comptox.epa.gov/dashboard">https://comptox.epa.gov/dashboard</a></p> <p>See also references below.</p> <ul> <li>20201230_tx_zf.csv -- Endpoint data</li> <li>20201230_tx_zf_smiles.txt -- SMILES of the structures</li> <li>20201230_tx_zf_descriptors_rdkit.csv -- Descriptors</li> <li>20201230_tx_zf_fp_r3_v5120.csv -- Fingerprints</li> </ul>
S29 | PHYTOTOXINS | Toxic Plant Phytotoxin (TPPT) Database
<p>This is the collection associated with list S29 PHYTOTOXINS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S29 PHYTOTOXINS <strong>Toxic Plant Phytotoxin (TPPT) Database</strong></p> <p>A comprehensive toxic plant-phytotoxin (TPPT) database provided by Günthardt et al 2018, DOI: <a href="https://pubs.acs.org/doi/10.1021/acs.jafc.8b01639">10.1021/acs.jafc.8b01639</a><br>More information on the <a href="https://www.agroscope.admin.ch/agroscope/en/home/publications/apps/tppt.html">Agroscope TPPT website</a>.</p> <p>Updated 20/11/2019 to correct InChIKey errors; updated 16/7/2022 to create merged SMILES column for PubChem deposition. 27/6/2025 added new CSV without duplicate CAS headers. </p>
Inter-Chemical Correlation results for the study: HHEARx2017-1593 (Role of environmental toxicants in modulating disease severity in children with NAFLD)
Title: Role of environmental toxicants in modulating disease severity in children with NAFLD <br>Species: Homo sapiens <br>Number of samples: 436 <br>Number of named analytes: 7 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=30 <br>
Predicted and experimental chemical and ecotoxicological properties for the toxic unit based hazard assessment
<p><strong>Description</strong></p> <p>This dataset contains ecotoxicity data of 1585 chemicals of environmental concern (CECs) and chemical identifiers. The ecotoxicity data was retrieved from <a href="https://cfpub.epa.gov/ecotox">US EPA ECOTOX Knowlegdebase</a> in ASCII file format and was aggregated for the ecotoxicity groups algae, crustaceans, and fish based on the ideas of <a href="https://dx.doi.org/10.1002/etc.3460">Busch et al. 2016</a>. The dataset includes the 5-percentile, the mean and the geomean of all retrieved ecotoxicity for each compound. Missing ecotoxicity data was estimated with ECOSAR 1.0 algorithms for green algae, daphnids, and fish using <a href="https://www.ufz.de/index.php?en=34593">ChemProp 6.8</a>. The main purpose of this dataset is the <a href="http://doi.org/10.1016/0043-1354(70)90018-7">toxic unit</a> (TU) based hazard assessment of environmental water samples. Chemical properties were estimated using <a href="https://github.com/kmansouri/OPERA">OPERA 2.7</a>, <a href="https://chemaxon.com/products/instant-jchem">Instant JChem</a>, and ACD Percepta 2015 based on QSAR-ready SMILES derived from OPERA 2.7. All data aggregated from EcoTox Knowledgebase (e.g., raw values, species, etc.) is available in the dataset in the detailed sheets. REcoTox, the processing script written in R is available on <a href="https://github.com/tsufz/REcoTox/releases/latest">GitHub</a>.</p> <p><strong>CAUTION</strong></p> <p>It needs to be emphasized that quantitative-structure activity relationship data is just an estimate, which does not necessarily reflect the real property and behaviour of a modelled compound. The calculated data needs to be reviewed in deep. Especially for non-polar or very polar compounds, the QSAR predictions might fail. If a compound ranks high in the TU ranking, it is required to search for literature or regulative data evidences to underpin the finding to avoid false positive prioritizations.</p> <p><strong>RELEASE NOTE</strong></p> <p>Version 210714_v1 was created with <a href="https://github.com/tsufz/REcoTox/releases/tag/v0.1.0">REcoTox version v0.1.0</a>.</p>
Interlaboratory testing of CuSO4 toxicity in the “Standardized Aquatic Microcosm” protocol consisting of multiple phytoplankton and animals in a chemically defined medium.
Four different laboratories conducted a total of ten experiments of the “Standardized Aquatic Microcosm” to test the reproducibility of results to control, low, medium, and high concentrations of CuSO4. In nine experiments, treatments consisted of six replicates of 0, 500, 1000, and 2000 ppb Cu++. One experiment, ME74, used 0, 127, 255, 509 ppb. The purpose was to test a chemically defined medium (thus negating differences due to local water supplies) and the same 10 species of phytoplankton and 5 animals, including Daphnia. Microbes were undefined. The protocol included the weekly re-introduction of small numbers of each species to allow potential recovery from toxicity. Control microcosms had a “spring algal bloom” terminated by zooplankton grazing and multiple competitive interactions. The copper inhibited some phytoplankton more than others and killed many grazers, especially Daphnia. The data set presented several interesting statistical properties that would yield new insights. (a) The results were very similar, but the timing varied— the higher the concentration of copper, the longer the inhibition and mortality of organisms, so those at 500 ppb recovered earlier, the 1000 ppb recovered later, and at 2000 ppb most never recovered. But if compared on each sampling day, e.g., 10, 14, … to 64, results appear highly variable. (b) In at least one experiment, the toxicity of copper was challenging to demonstrate statistically because high variability in the timing of recovery of the intermediate concentration increased pooled variances. (c) The elimination of highly-sensitive dominant organisms allowed less-sensitive organisms to increase in abundance. Within natural environments, the observation that some species increase in the presence of toxic substances has been used to discredit toxicity testing without considering the relative sensitivities of competing or predatory species. (d) The competitive interactions among organisms, e.g., cyanobacteria and green alga
RDF version of the data from Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018)
<p>RDF version of the data from Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018)</p>
Toxic Content Detection in online social networks: a new dataset from Brazilian Reddit Communities
<p>This is new dataset of 2,500 manually annotated examples of comments extracted from the top 10 largest Brazilian subreddits on Reddit. The dataset has been annotated by crowd-sourcing efforts with contributions from the departments of computer science (DCC) and the linguistic group @ UFMG. As part of our contribution to the toxicity automatic detection and moderation of online social networks, we're making the dataset public for research.</p> <h3>Dataset</h3> <p>The dataset contains 2,500 manually annotated comments from the most popular brazilian communities on Reddit. The data sampling proccess was a stratified sampling by the number of generated publications by subreddit and the month of publication. The list of communities collected is presented below. The collected data period ranges from January 2022 to December 2022.</p> <p> </p> <table> <tbody> <tr> <td><strong>Subreddit</strong></td> <td><strong>Posts</strong></td> <td><strong>Comments</strong></td> </tr> <tr> <td>r/brasil</td> <td>110,829 </td> <td>2,136,866</td> </tr> <tr> <td>r/desabafos</td> <td>115,876</td> <td>1,211,643</td> </tr> <tr> <td>r/futebol</td> <td>35,826</td> <td>1,214,412</td> </tr> <tr> <td>r/saopaulo</td> <td>7,308</td> <td>81,969</td> </tr> <tr> <td>r/eu_nvr</td> <td>12,631</td> <td>188,620</td> </tr> <tr> <td>r/botecodoreddit</td> <td>7,059</td> <td>57,298</td> </tr> <tr> <td>r/conversas</td> <td>21,967</td> <td>326,061</td> </tr> <tr> <td>r/investimentos</td> <td>9,756</td> <td>141,823</td> </tr> <tr> <td>r/tiodopave</td> <td>2,371</td> <td>11,584</td> </tr> <tr> <td>r/brasilivre</td> <td>67,301</td> <td>1,219265</td> </tr> <tr> <td>Total</td> <td>390,924</td> <td>6,589,541</td> </tr> </tbody> </table> <p> </p> <h3><strong>Annotation proccess</strong></h3> <p>The annotators were divided into groups of raters and each group was assigned a batch of comments to label. The raters were then asked to label a comment as <strong>Toxic</strong>, <strong>Non-toxic</strong>, <strong>I do not know</strong> and <strong>Missing info</strong>. During the annotation process, the raters were encouraged to assign one of the uncertain labels when they're not sure about the toxicity of a comment or the context is missing. </p> <h3>Available data</h3> <p>The dataset is available as csv file and the label was assigned as a majority vote among the raters. The available data are the original collected comment id and body. The label was created from the original classification from the annotators. No data processing has been done on this version of the dataset. The overall schema of the dataset if presented below.</p> <p>- <strong>id</strong>: The unique identifier of the comment on the Reddit platform<br>- <strong>body</strong>: The original comment text publication<br>- <strong>is_toxic</strong>: The final label of a given comment. The label is <strong>0</strong> for non-toxic comments, <strong>1</strong> for toxic comments and <strong>-1</strong> for comments where the raters disagreed about the toxicity.</p>
Supporting Online Toxicity Detection with Knowledge Graphs: Data
<p>This data repository contains the output files from the analysis of the paper "Supporting Online Toxicity Detection with Knowledge Graphs" presented at the International Conference on Web and Social Media 2022 (ICWSM-2022).</p> <p> </p> <p>The data contains annotations of gender and sexual orientation entities provided by the Gender and Sexual Orientation Ontology (https://bioportal.bioontology.org/ontologies/GSSO).</p> <p>We analyse demographic group samples from the Civil Comments Identities dataset (https://www.tensorflow.org/datasets/catalog/civil_comments).</p>
Changes in soil moisture and temperature modify the toxicity of sodium selenite and sodium selenate for Folsomia candida (Collembola) Willem 1902
<p>Effects of sublethal concentrations of selenite and selenate were tested on parameters of mortality, reproduction, growth, and oxidative stress parameters of <em>Folsomia candida</em> (Collembola) in case of different climate scenarios. The standard 20°C and the increased 25°C temperatures were combined with three different soil moisture conditions: drought, standard water content and increased water content.</p>
Wikipedia Talk Page 'Climate Change' Sentiment and Toxicity Dataset
<p>The given dataset was prepared as part of a master's research project under the Master's program in Computational Social Systems at RWTH Aachen University.</p> <p>The talk page was parsed using the <a href="https://aclanthology.org/E17-3006/">GraWiTas tool</a> in JSON format. The file <em>Climate_change.comment_list.json </em> is raw export of discussions that needs to be cleaned before using it for calculating sentiment and toxicity scores.</p> <p>The sentiment scores were calculated using VADER and toxicity scores using the Perspective API by Google.</p> <p>Date & time of dump is: 27-06-2022 12:12 UTC+02:00</p>
Annotated Data in Spanish for Toxicity and Insults in Digital Social Networks
<p>This repository contains data sets and materials for a gold standard elaboration on toxicity and incivility in the digital sphere based on human coding to benchmark algorithmic classification tasks with transformers and LLMs. <strong>The labelling progress is 62%</strong>.</p> <p>We are labelling two samples of novel datasets of political digital interactions on Twitter (rebranded as X). The first set comprises almost 5 million data points from three Latin American protest events: (a) protests against the coronavirus and judicial reform measures in Argentina during August 2020; (b) protests against education budget cuts in Brazil in May 2019; and (c) the social outburst in Chile stemming from protests against the underground fare hike in October 2019. We are focusing on interactions in Spanish to elaborate a gold standard for digital interactions in this language, therefore, we prioritise Argentinian and Chilean data. The second set contains more than 31 million messages and more than 9 million interactions between 2010 and 2022, covering the election of members of the first Constitutional Convention in Chile, the drafting process and the referendum in which the proposal was rejected.</p> <p>This project is generously funded by the <strong>OpenAI Academic Programme</strong>, <strong>2024 FAE-UDP Research Grant</strong>, and partially by the <strong>St Hilda's College Muriel Wise Fund at the University of Oxford</strong>. The <a href="https://training-datalab.com/"><strong>Training Data Lab</strong></a> research group also logistically supports this project.</p>
Group Contribution Models for Tetrahymena Pyriformis Toxicity
<p>A data set of several models for Tetrahymena Pyriformis Toxicity (THPT) based on additive schemes (group contribution models).</p> <p>Model building is performed by means of Ambit-GCM software (<a href="http://ambit.sourceforge.net/">http://ambit.sourceforge.net/</a>, <a href="https://doi.org/10.5281/zenodo.1470793">https://doi.org/10.5281/zenodo.1470793</a>)</p> <p>The main goal of the created THPT models is to illustrate Ambit-GCM software usage.</p> <p>Typically biological activity is not well modeled by means of additive schemes. In current modeling setup we demonstrate combination of group contribution scheme with external descriptors.</p>
Consensus models to predict oral rat acute toxicity and validation on a dataset coming from the industrial context
<p>We report predictive models of acute oral systemic toxicity representing a follow-up of our previous work in the framework of the NICEATM project. It includes the update of original models through the addition of new data and an external validation of the models using a dataset relevant for the chemical industry context. A regression model for LD50 and classification model for toxicity classes according to the Global Harmonized System categories were prepared. ISIDA descriptors were used to encode molecular structures. Machine learning algorithms included Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayesian. Selected individual models were combined in consensus.</p> <p>The different datasets were compared using the Generative Topographic Mapping approach. It appeared that the NICEATM datasets were lacking some relevant chemotypes for chemical industry. The new models trained on enlarged data sets have applicability domain (AD) sufficiently large to accommodate industrial compounds. The fraction of compounds inside the models’ AD increased from 58 % (NICEATM model) to 94 % (new model). Yet, the increase of training sets only slightly improved of the models’ prediction performance: RMSE values decreased from 0.56 to 0.47 and balanced accuracies increased from 0.69 to 0.71 for NICEATM and new models, respectively.</p>
Toxins: Toxic (985) DwCA
Various datasets about toxicity of organisms.<p></p>Various datasets about toxicity of organisms, literature sources.
RDF version of the data from Choi, JS. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources (2018)
<p>This is an RDFied version of the dataset published in Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018)</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1038/s41598-018-24483-z">https://doi.org/10.1038/s41598-018-24483-z</a></p> <p>The Original publication authors: Jang-Sik Choi, My Kieu Ha, Tung Xuan Trinh, Tae Hyun Yoon & Hyung-Gi Byun</p>
Dataset from "Venomous Peptides: Molecular Origin of the Toxicity of Snake Venom PLA2‑like Peptides"
<p>Dataset from "Venomous Peptides: Molecular Origin of the Toxicity of Snake Venom PLA2‑like Peptides", containing the most relevant all-atom output trajectories and input files ran with GROMACS 2021:</p> <p>1) <strong>pure_membrane_systems.7z</strong> - pure bilayer systems (AA1, AA2, AA5), including equilibration, calcium insertion, and umbrella sampling simulations;</p> <p>2) <strong>single_peptide_systems.7z</strong> - single peptide-containing systems (AA3, AA4, AA6), including equilibration, calcium insertion, and umbrella sampling simulations;</p> <p>3) <strong>multiple_peptide_systems.7z</strong> - multiple peptide-containing systems (AA7, AA8), including equilibration, calcium insertion, and umbrella sampling simulations.</p> <p>We have included the input files (.mdp), system topology (.top and .itp), initial and final structure files (.gro), the index file (.ndx), and the portable binary run input files (.tpr). We have also included the output trajectories of systems AA3, AA4, AA6-8 in .xtc format, and spaced every 500 ps.</p> <p>System composition is given in Table Z1. More details can be found in the related publication.</p> <p><strong>Table Z1. Simulated systems' details, including name, composition (in number of lipid and peptide molecules), number of atoms composing the systems, simulation (sim.) time, and total umbrella sampling (US) time.</strong> </p> <table> <tbody> <tr> <td><strong>System</strong></td> <td><strong>POPC/POPS/Peptide</strong></td> <td><strong>no. atoms (a)</strong></td> <td><strong>sim. time (µs)</strong></td> <td><strong>US time (µs)</strong></td> </tr> <tr> <td><strong>AA1</strong></td> <td>128/0/0</td> <td>40,226</td> <td>0.3</td> <td>10.8</td> </tr> <tr> <td><strong>AA2</strong></td> <td>0/128/0</td> <td>39,458</td> <td>0.3</td> <td>10.8</td> </tr> <tr> <td><strong>AA3</strong></td> <td>128/0/1</td> <td>40,504</td> <td>0.5</td> <td>32.3</td> </tr> <tr> <td><strong>AA4</strong></td> <td>0/128/1</td> <td>39,724</td> <td>0.5</td> <td>32.3</td> </tr> <tr> <td><strong>AA5</strong></td> <td>96/32/0</td> <td>40,034</td> <td>1.0</td> <td>-</td> </tr> <tr> <td><strong>AA6</strong></td> <td>96/32/1</td> <td>40,300</td> <td>1.0</td> <td>-</td> </tr> <tr> <td><strong>AA7</strong></td> <td>96/32/5</td> <td>41,364</td> <td>1.0</td> <td>10.8</td> </tr> <tr> <td><strong>AA8</strong></td> <td>96/32/13</td> <td>55,128</td> <td>2.0</td> <td>10.8</td> </tr> </tbody> </table> <p>(a) for the US simulations, the total number of atoms was reduced in 1 because two sodium ions were substituted by a single calcium ion.</p>
Laboratory toxicity incubation experiments on phytoplankton using trace metals (Cu, Cd, Zn)
<p>This data compilation contains previously published toxicity threshold concentrations of copper, cadmium and zinc for different phytoplankton, as determined by incubation experiments. The data was recalculated to nmol/L for consistency, assuming the following molar masses of copper, cadmium and zinc as 63.546, 112.411 and 65.380 g/mol, respectively, and salinity as 1.025 kg/L. The growth medium is included in the dataset, as well as the environment where the phytoplankton in question may commonly occur (open or coastal ocean). </p>
Potentially toxic trace metal (Cu, Cd) threshold concentrations for phytoplankton at given open and coastal locations
<p>This data compilation contains previously published threshold concentrations of copper and cadmium of phytoplankton in open and coastal oceans. The data was recalculated to nmol/L for consistency, assuming the following molar masses of copper and cadmium as 63.546 and 112.411, respectively, and salinity as 1.025 kg/L. The temperature and salinity provided by the authors were also included, in case there is a desire for future users to utilise different conversion methods to recalculate original data. Only data with information on whether the authors measured the trace metal concentrations in open or coastal marine environments were included, along with the name of the phytoplankton. The oceans were divided into geographical sections, namely the Atlantic Ocean, Indian Ocean, Pacific Ocean and Southern Ocean, and subsequently further subdivided according to the information authors have given in their publications. In this context, several chemically diverse seas were included in geographical regions in order to limit the number of broad ocean regimes. Chemically diverse sub-regimens were, however, considered within each geographical grouping.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.