The purpose of this project is to develop spatially discrete end-to-end models of the California Current LME, linking oceanography, biogeochemistry, food web interactions, habitat, fisheries, economics, monitoring, and management into a common model framework. This framework allows for thought experiments, including evaluation of alternate management strategies, identifying robust indicators, and assessing relative importance of different ecosystem drivers in regulating important processes. NMFS personnel are conducting this work in broad collaboration with other NOAA scientists, academics, and NGOs. The specific work entails model development, scoping issues with stakeholders and policy makers, running scenarios, and analyzing and writing up the results. Products will include peer-reviewed papers, presentations, and workshops with modelers and/or stakeholders. Management audiences include NMFS west coast regions and the PFMC. The project is an on-going, stand-alone project with no firm deadline for completion. Results of Atlantis ecosystem model simulations Metadata and .nc datafile at https://www.nodc.noaa.gov/oceanacidification/data/0131198.xml Generated from Atlantis ecosystem model, version AtlantisTrunk5425. Model code from CSIRO Australia, available via SVN after contacting CSIRO staff at http://atlantis.cmar.csiro.au/.

The purpose of this project is to develop spatially discrete end-to-end models of the northern Gulf of California, linking oceanography, biogeochemistry, food web interactions, habitat, fisheries, economics, monitoring, and management into a common model framework. This framework allows for thought experiments, including evaluation of alternate management strategies, identifying robust indicators, and assessing relative importance of different ecosystem drivers in regulating important processes. NMFS personnel are conducting this work in broad collaboration with a consortium of Mexican federal, state, NGO and academic scientists. The specific work entails model development, scoping issues with stakeholders and policy makers, running scenarios, and analyzing and writing up the results. Products include peer-reviewed papers, presentations, and workshops with modelers and/or stakeholders. Management audiences include Mexican governmental bodies and conservation organizations. The project is an on-going, stand-alone project with no firm deadline for completion. Outputs of Atlantis model scenarios.

Stop

## Overview

Stop is a dataset for object detection tasks - it contains Stop annotations for 404 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The dataset consists of parameters (p0 and p1) for a semi-empirical electronic stopping force model. Parameters for 107 fitted datasets are given in p0p1.txt. Further, quantitative comparisons of the model values to the experimental values are given in Deltas.txt, available to download with the dataset.

As described in the README.md file, the GitHub repository github.com/USEPA/PRTR-QSTR-models/tree/data-driven are Python scripts written to run Quantitative Structure–Transfer Relationship (QSTR) models based on chemical structure-based machine learning (ML) models for supporting environmental regulatory decision-making. Using features associated with annual chemical transfer amounts, chemical generator industry sectors, environmental policy stringency, gross value added by industry sectors, chemical descriptors, and chemical unit prices, as in the GitHub repository PRTR_transfers, the QSTR models developed here can predict potential EoL activities for chemicals transferred to off-site locations for EoL management. Also, this contribution shows that QSTR models aid in estimating the mass fraction allocation of chemicals of concern transferred off-site for EoL activities. Also, it describes the Python libraries required for running the code, how to use it, the obtained outputs files after running the Python script, and how to obtain all manuscript figures and results. This dataset is associated with the following publication: Hernandez-Betancur, J.D., G.J. Ruiz-Mercado, and M. Martín. Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models. ACS Sustainable Chemistry & Engineering. American Chemical Society, Washington, DC, USA, 11(9): 3594-3602, (2023).

Most street object datasets require your model to do object detection and extract multiple objects from a single image. Which is fine if you're working with complex models like YOLO or R-CNN. This dataset however is for image classification training that could be done with any simple CNN model or even traditional ML models with enough processing and feature extraction.

A convenient csv file with image paths and encoded labels is provided for use in image data generators.

Overview

Dataset contains 9879 images with varying sizes categorized into 7 classes.

• 0: bicycle
• 1: car
• 2: limit30
• 3: person
• 4: stop
• 5: trafficlight
• 6: truck

Sources

This dataset was obtained by performing some processing on the following dataset:

https://www.kaggle.com/datasets/ahmedyoussefff/street-objects-dataset/

https://universe.roboflow.com/project-mzmwg/street-objects-ag7dt

The preprocessing consisted of cropping each object specified by the YOLO format into its own separate image. Preprocessing code is available here: https://www.kaggle.com/code/owm4096/street-objects-classification-dataset-extraction

The purpose of this project is to develop spatially discrete end-to-end models of the California Current LME, linking oceanography, biogeochemistry, food web interactions, habitat, fisheries, economics, monitoring, and management into a common model framework. This framework allows for thought experiments, including evaluation of alternate management strategies, identifying robust indicators, and assessing relative importance of different ecosystem drivers in regulating important processes. NMFS personnel are conducting this work in broad collaboration with other NOAA scientists, academics, and NGOs. The specific work entails model development, scoping issues with stakeholders and policy makers, running scenarios, and analyzing and writing up the results. Products will include peer-reviewed papers, presentations, and workshops with modelers and/or stakeholders. Management audiences include NMFS west coast regions and the PFMC. The project is an on-going, stand-alone project with no firm deadline for completion. Outputs of the ROMS model. Metadata and .nc datafile at https://www.nodc.noaa.gov/oceanacidification/data/0131198.xml Generated from Atlantis ecosystem model, version AtlantisTrunk5425. Model code from CSIRO Australia, available via SVN after contacting CSIRO staff at http://atlantis.cmar.csiro.au/.

Stop Sign Official

## Overview

Stop Sign Official is a dataset for object detection tasks - it contains Stop Sign annotations for 1,629 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The purpose of this project is to develop spatially discrete end-to-end models of the California Current LME, linking oceanography, biogeochemistry, food web interactions, habitat, fisheries, economics, monitoring, and management into a common model framework. This framework allows for thought experiments, including evaluation of alternate management strategies, identifying robust indicators, a...

Consistently not responding to stimuli during go/no-go training leads to lower evaluations of these NoGo stimuli. How this NoGo-devaluation-effect can be explained has remained unclear. Here, we ran three experiments to test the hypothesis that people form stimulus-stop-associations during the training, which predict the strength of the devaluation-effect. In Experiment 1, we tried to simultaneously measure the stimulus-stop-associations and NoGo-devaluation, but we failed to find these effects. In Experiment 2, we measured NoGo-devaluation with established procedures from previous work, and stimulus-stop-associations with a novel separate task. Results revealed a clear NoGo-devaluation-effect, which remained visible across multiple rating blocks. Interestingly, this devaluation-effect disappeared when stimulus-stop-associations were measured before stimulus evaluations, and there was no evidence supporting the formation of the stimulus-stop-associations. In Experiment 3, we found evidence for the acquisition of stimulus-stop-associations using an established task from previous work, but this time we found no subsequent NoGo-devaluation-effect. The present research suggests that the NoGo-devaluation-effect and stimulus-stop-associations can be found with standard established procedures, but that these effects are very sensitive to alterations of the experimental protocol. Furthermore, we failed to find evidence for both effects within the same experimental protocol, which has important theoretical and applied implications.

These contours represent the simulated potentiometric surface at the end of simulation (1998) in model layer 1 of the Death Valley regional ground-water flow system (DVRFS), an approximately 45,000 square-kilometer region of southern Nevada and California. The numerical ground-water flow model simulates prepumping conditions before 1913 and transient-flow conditions from 1913 to 1998 after pumping of ground water began. The DVRFS transient ground-water flow model is the most recent in a number of regional-scale models developed by the U.S. Geological Survey (USGS) for the U.S. Department of Energy (DOE) to support investigations at the Nevada Test Site (NTS) and at Yucca Mountain, Nevada (see "Larger Work Citation", Chapter A, page 8, for details).

The page contains a JAVA applet, which runs the following models: * Verhulst model. * Lotka-Volterra competition model * Lotka-Volterra predator-prey model (with and without prey carrying capacity) * May 1978 parasitoid-host model * Nicholson-Bailey model (with and without prey carrying capacity) * ITER model * ITER bifurcation diagram The programm runs on the client side of the Internet. Simply select the model of your choice in the upper pull-down list. Just try the rest. You can modify the parameters and let the model run. And let it stop. [ Modeling Paradigm: Simulative prediction ]

This paper discusses nine issues related to the further development and application of end-to-end models, the future of end-to-end modelling, and it's relevance to managing decision making. This paper is based on the discussions at a workshop entitled "Bridging the Gap between Lower and Higher Trophic Levels".

The statistics of a road vehicle state.

Comparison of Seconds to Output 500 Tokens, including reasoning model 'thinking' time; Lower is better by Model

Detect Stop Signs Testing

## Overview

Detect Stop Signs Testing is a dataset for object detection tasks - it contains Stopsigns annotations for 1,120 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Background Classification of the electrocardiogram using Neural Networks has become a widely used method in recent years. The efficiency of these classifiers depends upon a number of factors including network training. Unfortunately, there is a shortage of evidence available to enable specific design choices to be made and as a consequence, many designs are made on the basis of trial and error. In this study we develop prediction models to indicate the point at which training should stop for Neural Network based Electrocardiogram classifiers in order to ensure maximum generalisation. Methods Two prediction models have been presented; one based on Neural Networks and the other on Genetic Programming. The inputs to the models were 5 variable training parameters and the output indicated the point at which training should stop. Training and testing of the models was based on the results from 44 previously developed bi-group Neural Network classifiers, discriminating between Anterior Myocardial Infarction and normal patients. Results Our results show that both approaches provide close fits to the training data; p = 0.627 and p = 0.304 for the Neural Network and Genetic Programming methods respectively. For unseen data, the Neural Network exhibited no significant differences between actual and predicted outputs (p = 0.306) while the Genetic Programming method showed a marginally significant difference (p = 0.047). Conclusions The approaches provide reverse engineering solutions to the development of Neural Network based Electrocardiogram classifiers. That is given the network design and architecture, an indication can be given as to when training should stop to obtain maximum network generalisation.

This dataset contains scraped and processed text from roughly 100 years of articles published in the Wiley journal Science Education (formerly General Science Quarterly). This text has been cleaned and filtered in preparation for analysis using natural language processing techniques, particularly topic modeling with latent Dirichlet allocation (LDA). We also include a Jupyter Notebook illustrating how one can use LDA to analyze this dataset and extract latent topics from it, as well as analyze the rise and fall of those topics over the history of the journal.

The articles were downloaded and scraped in December of 2019. Only non-duplicate articles with a listed author (according to the CrossRef metadata database) were included, and due to missing data and text recognition issues we excluded all articles published prior to 1922. This resulted in 5577 articles in total being included in the dataset. The text of these articles was then cleaned in the following way:

• We removed duplicated text from each article: prior to 1969, articles in the journal were published in a magazine format in which the end of one article and the beginning of the next would share the same page, so we developed an automated detection of article beginnings and endings that was able to remove any duplicate text.
• We removed the reference sections of the articles, as well headings (in all caps) such as “ABSTRACT”.
• We reunited any partial words that were separated due to line breaks, text recognition issues, or British vs. American spellings (for example converting “per cent” to “percent”)
• We removed all numbers, symbols, special characters, and punctuation, and lowercased all words.
• We removed all stop words, which are words without any semantic meaning on their own—“the”, “in,” “if”, “and”, “but”, etc.—and all single-letter words.
• We lemmatized all words, with the added step of including a part-of-speech tagger so our algorithm would only aggregate and lemmatize words from the same part of speech (e.g., nouns vs. verbs).
• We detected and create bi-grams, sets of words that frequently co-occur and carry additional meaning together. These words were combined with an underscore: for example, “problem_solving” and “high_school”.

After filtering, each document was then turned into a list of individual words (or tokens) which were then collected and saved (using the python pickle format) into the file scied_words_bigrams_V5.pkl.

In addition to this file, we have also included the following files:

1. SciEd_paper_names_weights.pkl: A file containing limited metadata (title, author, year published, and DOI) for each of the papers, in the same order as they appear within the main datafile. This file also includes the weights assigned by an LDA model used to analyze the data
2. Science Education LDA Notebook.ipynb: A notebook file that replicates our LDA analysis, with a written explanation of all of the steps and suggestions on how to explore the results.
3. Supporting files for the notebook. These include the requirements, the README, a helper script with functions for plotting that were too long to include in the notebook, and two HTML graphs that are embedded into the notebook.

This dataset is shared under the terms of the Wiley Text and Data Mining Agreement, which allows users to share text and data mining output for non-commercial research purposes. Any questions or comments can be directed to Tor Ole Odden, t.o.odden@fys.uio.no.

The automotive start-stop system market is experiencing robust growth, driven by stringent fuel efficiency regulations and the increasing demand for eco-friendly vehicles. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This growth trajectory is fueled by several key factors. Firstly, the global push towards reducing carbon emissions is compelling automakers to integrate fuel-saving technologies like start-stop systems into their vehicle models. Secondly, advancements in battery technology are leading to more efficient and reliable start-stop systems, addressing previous concerns about durability and performance. Furthermore, the rising adoption of hybrid and electric vehicles naturally incorporates start-stop systems, further boosting market demand. The increasing awareness among consumers regarding fuel economy and environmental sustainability also contributes positively to market growth. Leading players such as Delphi Automotive plc, Denso Corp., Robert Bosch GmbH, Valeo SA., and Schaeffler Technologies AG & Co. KG are at the forefront of innovation and competition within this market. However, challenges such as the high initial cost of implementation and the potential for increased wear and tear on vehicle components remain as restraints. Future growth hinges on addressing these challenges through technological advancements, cost reductions, and enhanced consumer education. Regional variations in market penetration exist, with developed economies in North America and Europe showing higher adoption rates compared to emerging markets. The forecast period of 2025-2033 promises continued expansion as the market matures and integrates further technological refinements.

Stop Line

## Overview

Stop Line is a dataset for object detection tasks - it contains Stop Line annotations for 276 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).