AndreaBozzo/ceres-open-data-index huggingface dataset:
Trojan Detection Software Challenge - nlp-summary-jan2022-train

Round 9 Train DatasetThis is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform one of three tasks, sentiment classification, named entity recognition, or extractive question answering on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 210 Sentiment Classification, Named Entity Recognition, and Extractive Question Answering AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Tags: adversarial-machine-learning, ai, artificial-intelligence, machine-learning, trojan-detection
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - nlp-question-answering-sep2021-holdout

Round 8 Holdout DatasetThis is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform extractive question answering on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 360 QA AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Tags: adversarial-machine-learning, ai, artificial-intelligence, machine-learning, trojan-detection
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - nlp-question-answering-sep2021-train

Round 8 Train DatasetThis is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform extractive question answering (QA on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 120 QA AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - nlp-named-entity-recognition-may2021-holdout

Round 7 Holdout DatasetThis is the holdout data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform named entity recognition (NER) on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 384 named entity recognition AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - nlp-sentiment-classification-apr2021-holdout

Round 6 Holdout DatasetThis is the holdout data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform text sentiment classification on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 480 sentiment classification AI models using a small set of model architectures. The models were trained on text data drawn from product reviews. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - nlp-named-entity-recognition-may2021-train

Round 7 Train DatasetThis is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform named entity recognition (NER) on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 192 sentiment classification AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - nlp-sentiment-classification-apr2021-train

Round 6 Train DatasetThis is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform text sentiment classification on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 48 adversarially trained, sentiment classification AI models using a small set of model architectures. The models were trained on text data drawn from movie and product reviews. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - nlp-sentiment-classification-mar2021-holdout

Round 5 Holdout DatasetThis is the holdout data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform text sentiment classification on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 504 adversarially trained, sentiment classification AI models using a small set of model architectures. The models were trained on text data drawn from movie and product reviews. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present. Errata: The following models were contaminated during dataset packaging. This caused nominally clean models to have a trigger. Please avoid using these models. Due to the similarity between the Round5 and Round6 datasets (both contain similarly trained sentiment classification AI models), the dataset authors suggest ignoring the Round5 data and only using the Round6 dataset. Corrupted Models: [id-00000000, id-00000019, id-00000033, id-00000084, id-00000087, id-00000104, id-00000146, id-00000148, id-00000167, id-00000212, id-00000221, id-00000230, id-00000233, id-00000237, id-00000239, id-00000246, id-00000281, id-00000284, id-00000288, id-00000295, id-00000302, id-00000303, id-00000310, id-00000343, id-00000349, id-00000351, id-00000361, id-00000366, id-00000367, id-00000369, id-00000371, id-00000376, id-00000407, id-00000418, id-00000423, id-00000425, id-00000428, id-00000439]

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - image-classification-feb2021-holdout

Round 4 Holdout DatasetThe data being generated and disseminated is the holdout data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 288 adversarially trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology
Dark solitons in BECs dataset 2.0

Atomic Bose-Einstein condensates (BECs) are widely investigated systems that exhibit quantum phenomena on a macroscopic scale. For example, they can be manipulated to contain solitonic excitations including conventional solitons, vortices, and many more. Broadly speaking, solitonic excitations are solitary waves that retain their size and shape and often propagate at a constant speed. They are present in many systems, at scales ranging from microscopic, to terrestrial and even astronomical. However, unlike naturally occurring physical systems, the parameters governing BECs are under strict experimental control.The enlarged Dark solitons in BECs dataset v.2.0 dataset was created to enable the implementation of machine learning (ML) techniques to automate the analysis of data coming from cold atom experiments. It includes quantitative estimates of all longitudinal solitons quality as well as new fine-grained solitonic excitation categories of all detected excitations. It is freely available to the whole ML and physics community the opportunity to develop novel ML techniques for cold atom systems and to further explore the intersection of ML and quantum physics.

Tags: bose-einstein-condensates, dark-solitons, machine-learning
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - image-classification-dec2020-holdout

Round 3 Holdout DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 288 adversarially trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - image-classification-aug2020-holdout

Round 2 Holdout DatasetThe data being generated and disseminated is the holdout data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 144 trained, human level, image classification AI models using a variety of  architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology
NOAA/WDS Paleoclimatology - Cook - Longfellow Trail - PIST - ITRDB PA008

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Tree Ring. The data include parameters of tree ring with a geographic location of Pennsylvania, United States Of America. The time period coverage is from 271 to -31 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Tags: biological records, climate indicators, continent, doc/noaa/nesdis/ncei, earth science, eastern white pine, land records, latitude 41.33, longfellow trail, longitude -79.2, material: null, national centers for environmental information, nesdis, noaa, north america, paleoclimate, paleoclimate indicators, pennsylvania, pinus strobus l., pist, tree ring, tree rings, tree-ring, u.s. department of commerce, united states of america, weymouth pine, what: age, what: number of samples, what: total ring width, what: tree ring standardized growth index, width
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
Trojan Detection Software Challenge - image-classification-dec2020-train

Round 3 Training DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 1008 adversarially trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - image-classification-aug2020-train

Round 2 Training DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 1104 trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - image-classification-jun2020-train

Round 1 Training DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 1000 trained, human level, image classification AI models using the following architectures (Inception-v3, DenseNet-121, and ResNet50). The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present. Errata: This dataset had a software bug in the trigger embedding code that caused 4 models trained for this dataset to have a ground truth value of 'poisoned' but which did not contain any triggers embedded. These models should not be used. Models Without a Trigger Embedded: id-00000184 id-00000599 id-00000858 id-00001088 Google Drive Mirror: https://drive.google.com/open?id=1uwVt3UCRL2fCX9Xvi2tLoz_z-DwbU6Ce

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology
NOAA/WDS Paleoclimatology - Storegga Slide d18O N. pachyderma data covering the last 3000 years

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Paleoceanography. The data include parameters of paleoceanography with a geographic location of North Atlantic Ocean. The time period coverage is from 2906 to -49 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Tags: atlantic ocean, climate indicators, doc/noaa/nesdis/ncei, earth science, latitude 63.762222, longitude 5.255278, material: neogloboquadrina pachyderma dextral, material: null, national centers for environmental information, nesdis, noaa, north atlantic ocean, ocean, ocean/lake records, oxygen isotopes, p1003mc/sc, paleocean, paleoceanography, paleoclimate, paleoclimate indicators, storegga slide, u.s. department of commerce, what: age, what: delta 18o
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
Condition of Public School Facilities, 2012-13

Condition of Public School Facilities, 2012-13 (FRSS 105), is a study that is part of the Fast Response Survey System (FRSS) program; program data is available since 1998-99 at https://nces.ed.gov/surveys/frss/downloads.asp that will provide national data about the condition of public schools in 2012 based on a survey conducted by NCES. FRSS 105 (https://nces.ed.gov/surveys/frss/index.asp) is a cross-sectional survey that collects and report data on key education issues at the elementary and secondary levels. The study was conducted using questionnaires of principals. Schools in September 2012 were sampled. The study's weighted response rate was 90 percent. Key statistics produced from FRSS 105 will provide information about the condition of school facilities and the costs to bring them into good condition; school plans for repairs, renovations, and replacements; the age of public schools; and school improvements to increase energy efficiency.

Tags: 0ee4621b-38be-46bb-8360-219726022a58, conditions, facilities, fast-response-survey-system, feasibility, frss, pretest, public-school
Organization: Department of Education
Impact of Filter Material and Holding Time on Spore Sampling Efficiency in Water

Complete dataset for "Impact of Filter Material and Holding Time on Spore Sampling Efficiency in Water". 

This dataset is associated with the following publication:
Ratliff, K., A. Abdel-Hady, M. Monge, A. Mikelonis, and A. Touati. Impact of Filter Material and Holding Time on Spore Sampling Efficiency in Water.   Letters in Applied Microbiology. Blackwell Publishing, Malden, MA, USA, 76(2): ovad005, (2023).

Tags: bacillus-anthracis, edna, filter-sampling, remediation
Organization: U.S. Environmental Protection Agency
Dataset of Biota-Sediment Accumulation Factors for PFAS, Version 1

The file is a compilation of reported Biota-Sediment Accumulation Factors (BSAFs) for PFAS for aquatic species.  The file has the BSAFs as reported in the source and then, adjusted to units of kg-OC/kg-ww.  File also contains location information (where the measurement was done), an evaluation of its study quality, citation, number of samples used in determining the BSAF, complete taxonomic information, kinetic information when available, and when available, concentrations in the sediment and organism used to determine the BSAF. 

This dataset is associated with the following publication:
Burkhard, L., and L. Votava. Biota-Sediment Accumulation Factors for Per- and Polyfluoroalkyl Substances.   ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 42(2): 277-295, (2022).

Tags: bioaccumulation, bioconcentration, bsaf, pfas
Organization: U.S. Environmental Protection Agency
ToxCast data and SeqAPASS results related to case study chemicals and targets referenced in Leveraging New Approach Methodologies to Complement Aquatic Life Criteria Derivation

Excel file containing ToxCast bioactivity data for carbaryl, celecoxib, chromium triacetate,  HgCl2, nonylphenol, pentachlorophenol, PFOA, PFOS, pioglitazone, sodium dichromate, TCDD.
The .zip file contains SeqAPASS results for multiple protein targets. 

This dataset is associated with the following publication:
Schaupp, C., C. Lalone, B. Blackwell, G. Ankley, and D. Villeneuve. Leveraging ToxCast data and protein sequence conservation to complement aquatic life criteria derivation.   Integrated Environmental Assessment and Management. Allen Press, Inc., Lawrence, KS, USA, 19(1): 224-238, (2023).

Tags: adverse-outcome-pathways-aops, aquatic-life-criteria, new-approach-methodologies, new-approach-methods-alternatives-to-animal-testi, toxcast
Organization: U.S. Environmental Protection Agency