Sweden phone number data contains contact numbers collected from trusted sources. We define this data by ensuring that all phone numbers come from reliable and verified sources. You can even check source URLs to see where the data is collected from. Being clear makes it easy for you to trust the data. Our team is always available with 24/7 support if you need help or have questions about the data. Also, we focus on opt-in data, meaning that everyone on the list has given permission to be contacted. Sweden number data gives you access to contact information from people in Sweden. We define this data by making sure every number is accurate and useful. If you ever receive an incorrect number, we provide a replacement guarantee. We’ll make sure to fix any mistakes for you. Furthermore, we collect the data on a customer-permission basis. That means each person has agreed to share their contact details. This ensures that you are only getting numbers from people who have given permission. Moreover, we work hard to provide this data from List to Data that you can trust. By offering a replacement guarantee, we make sure that all the phone numbers you get are correct and reliable.

The main dataset is a 304 MB file of trajectory data (I90_94_stationary_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) vehicles and non-automated vehicles on a highway in an urban environment. Supporting files include aerial reference images for six distinct data collection “Runs” (I90_94_Stationary_Run_X_ref_image.png, where X equals 1, 2, 3, 4, 5, and 6). Associated centerline files are also provided for each “Run” (I-90-stationary-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I90_94Stationary.csv” for more details). The dataset defines six northbound lanes using these centerline files. Twelve different numerical IDs are used to define the six northbound lanes (1, 2, 3, 4, 5, 6, 10, 11, 12, 13, 14, and 15) depending on the run. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. Lane IDs are provided in the reference images in red text for each data collection run (I90_94_Stationary_Run_X_ref_image_annotated.jpg, where X equals 1, 2, 3, 4, 5, and 6). This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using the fixed location aerial videography approach with one high-resolution 8K camera mounted on a helicopter hovering over a short segment of I-94 focusing on the merge and diverge points in Chicago, IL. The altitude of the helicopter (approximately 213 meters) enabled the camera to capture 1.3 km of highway driving and a major weaving section in each direction (where I-90 and I-94 diverge in the northbound direction and merge in the southbound direction). The segment has two off-ramps and two on-ramps in the northbound direction. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (4:00 PM-6:00 PM CT) on a cloudy day. During this period, two SAE Level 2 ADAS-equipped vehicles drove through the segment, entering the northbound direction upstream of the target section, exiting the target section on the right through I-94, and attempting to perform a total of three lane-changing maneuvers (if safe to do so). These vehicles are indicated in the dataset. As part of this dataset, the following files were provided: I90_94_stationary_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I90_94_Stationary_Run_X_ref_image.png are the aerial reference images that define the geographic region for each run X. I-90-stationary-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The "x" and "y" columns represent the horizontal and ve

As of November 2025, there were a reported 4,165 data centers in the United States, the most of any country worldwide. A further 499 were located in the United Kingdom, while 487 were located in Germany. What is a data center? Data centers are facilities designed to store and compute vast amounts of data efficiently and securely. Growing in importance amid the rise of cloud computing and artificial intelligence, data centers form the core infrastructure powering global digital transformation. Modern data centers consist of critical computing hardware such as servers, storage systems, and networking equipment organized into racks, alongside specialized secondary infrastructure providing power, cooling, and security. AI data centers Data centers are vital for artificial intelligence, with the world’s leading technology companies investing vast sums in new facilities across the globe. Purpose-built AI data centers provide the immense computing power required to train the most advanced AI models, as well as to process user requests in real time, a task known as inference. Increasing attention has therefore turned to the location of these powerful facilities, as governments grow more concerned with AI sovereignty. At the same time, rapid data center expansion has sparked a global debate over resource use, including land, energy, and water, as modern facilities begin to strain local infrastructure.

Bangladesh number dataset provides contact information from trusted sources. We only collect phone numbers from reliable sources and define this information. To ensure transparency, we also provide the source URL to show where the information was collected from. In addition, we offer 24/7 support. If you have a question or need help, we’re always here. However, we care about accuracy, so we carefully collect the Bangladesh number dataset from trusted sources. You may rely on this data for business or personal use. With customer support, you’ll never have to wait when you need help or more information. We use opt-in data to respect privacy. This way, we contact only people who want to hear from you. Bangladesh phone data gives you access to contacts in Bangladesh. Here you can filter information by gender, age, and relationship status. This makes it easy to find exactly the people you want to connect with. We define this data by ensuring it follows all GDPR rules to keep it safe and legal. Our system works hard to remove any invalid data so you get only accurate and valid numbers. List to Data is a helpful website for finding important phone numbers quickly. Also, our Bangladesh phone data is suitable for doing business targeting specific groups. You can easily filter your list to focus on specific types of customers. Since we remove invalid data regularly, you don’t have to deal with old or useless numbers. We assure you that all data follows strict GDPR rules, so you can use it without any problems. Bangladesh phone number list is a collection of phone numbers from people in Bangladesh. We define this list by providing 100% correct and valid phone numbers that are ready to use. Also, we offer a replacement guarantee if you ever receive an invalid number. This means you will always have accurate data. We collect phone numbers that we provide based on customer’s permission. Moreover, we work hard to provide the best Bangladesh phone number list for businesses and personal use. We gather data correctly, so you won’t have to worry about getting outdated or incorrect information. Our replacement guarantee means you’ll always have valid numbers, so you can relax and feel confident.

Zip file containing all data and analysis files for Experiment 1 in:Weiers, H., Inglis, M., & Gilmore, C. (under review). Learning artificial number symbols with ordinal and magnitude information.Article abstractThe question of how numerical symbols gain semantic meaning is a key focus of mathematical cognition research. Some have suggested that symbols gain meaning from magnitude information, by being mapped onto the approximate number system, whereas others have suggested symbols gain meaning from their ordinal relations to other symbols. Here we used an artificial symbol learning paradigm to investigate the effects of magnitude and ordinal information on number symbol learning. Across two experiments, we found that after either magnitude or ordinal training, adults successfully learned novel symbols and were able to infer their ordinal and magnitude meanings. Furthermore, adults were able to make relatively accurate judgements about, and map between, the novel symbols and non-symbolic quantities (dot arrays). Although both ordinal and magnitude training was sufficient to attach meaning to the symbols, we found beneficial effects on the ability to learn and make numerical judgements about novel symbols when combining small amounts of magnitude information for a symbol subset with ordinal information about the whole set. These results suggest that a combination of magnitude and ordinal information is a plausible account of the symbol learning process.© The Authors

ReaxFF is a computationally efficient model for reactive molecular dynamics simulations that has been applied to a wide variety of chemical systems. When ReaxFF parameters are not yet available for a chemistry of interest, they must be (re)optimized, for which one defines a set of training data that the new ReaxFF parameters should reproduce. ReaxFF training sets typically contain diverse properties with different units, some of which are more abundant (by orders of magnitude) than others. To find the best parameters, one conventionally minimizes a weighted sum of squared errors over all of the data in the training set. One of the challenges in such numerical optimizations is to assign weights so that the optimized parameters represent a good compromise among all the requirements defined in the training set. This work introduces a new loss function, called Balanced Loss, and a workflow that replaces weight assignment with a more manageable procedure. The training data are divided into categories with corresponding “tolerances”, i.e., acceptable root-mean-square errors for the categories, which define the expectations for the optimized ReaxFF parameters. Through the Log-Sum-Exp form of Balanced Loss, the parameter optimization is also a validation of one’s expectations, providing meaningful feedback that can be used to reconfigure the tolerances if needed. The new methodology is demonstrated with a nontrivial parametrization of ReaxFF for water adsorption on alumina. This results in a new force field that reproduces both the rare and frequent properties of a validation set not used for training. We also demonstrate the robustness of the new force field with a molecular dynamics simulation of water desorption from a γ-Al2O3 slab model.

The dataset included in this repository was used to perform numerical simulations of hydrodynamics, salinity and temperature in the Bay of Cádiz, including the calibration and testing of the model. The simulation were performed with a 2D implementation of the Delft3D model.
Brief description of the files included in the dataset: REDEXT_T_HIS_GolfoDeCadiz is a text file with the data from buoy 2342 used to define the offshore boundary conditions for waves (Hm0, Tp, Dmd), salinity (Sa2) and temperature (Ts2), and the wind forcing (Vv_md, Dv_md). See heading for variable units. Field_data.mat: this file contains the field data used to calibrate and validate the model. There is one structure for each of the five instruments deployed (I1, I2, I3, I3a and I3b) with measurements from acoustic Doppler current profilers (ADCPs) and conductivity-temperature (CTs) devices. For each instruments, data are split by calibration and testing periods. The variables therein are: X and Y: X and Y UTM 30S coordinates of the deployment location time: date of the data in julian days pressure: pressure data (m) vE and vN: East and North depth average velocities (m/s) sal: depth-average salinity (psu) temp: depth-average water temperature (ºC) Heat_model.mat: a file containing the variables needed for the heat model. The values correspond to the closest station to the Cádiz bay and were considered as representative of the condition therein. The variables are: Time: date in Julian days Cloudiness: daily-average cloudiness (%) Humidity: daily-average atmospheric relative humidity (%) Radiation: daily-average (short wave) solar radiation (W/m2) Temperature: daily-average air surface temperature (ºC) Flow_wave.zip: a zip file containing the necessary files to run the 13-month Delft3D simulation (October 2012 to November 2013) with online coupling of the Flow and Wave modules, including the harmonics of the astronomical tide in the .bca file Flow.zip: a zip file containing the necessary files to run the 13-month Delft3D simulation (October 2012 to November 2013) with only the Flow module activated. Some of the files are binary (.mat format) but can be easily read using open source tools (e.g., Python or Octave). The research leading to this dataset was founded by the Bay of Cadiz Port Authority and the Department of Innovation, Science and Business of the Andalusian Regional Government (Project P09-TEP-4630), the Spanish Ministry of Economy and Competitiveness through the Projects CTM2017-89531-R (PIRATES), CTM2017-82274-R (MICROBAHIA2) and by the “Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo”, CYTED (project PROTOCOL 917PTE0538). The authors would also thank to the staff of the Environmental Fluid Dynamics research group (GDFA, University of Granada) for their support during the field survey. The first author was partially funded by the Andalusian Regional Government, Research Grant RNM-6352.

Iran number dataset allows you to filter phone numbers by gender, age, and relationship status. You can easily find the contacts you need with this helpful feature. We create this list to give you the best data for your search. Additionally, we remove invalid data regularly to keep the list fresh and accurate. This method keeps your info fresh, giving you the latest details each time. Moreover, using the Iran number dataset helps you find the exact contacts you need. You can filter numbers based on gender, age, or relationship status, making it simple to target your audience. We follow GDPR rules to respect everyone’s privacy. Plus, we remove invalid data and provide updates. Therefore, you always have access to the latest, reliable contact details. Iran phone data contains 100% correct and valid phone numbers. We define this list to ensure all numbers are checked. Thus, they can be used easily. If a number doesn’t work, we offer a replacement guarantee. This means we will replace any invalid numbers with correct ones at no extra cost. List to Data is a helpful website for finding important phone numbers quickly. Additionally, the Iran phone data gives you reliable contact information. Every number is verified to ensure it’s valid. If you find any invalid numbers, our replacement guarantee covers them. We make sure you only get the correct data. By collecting numbers with customer permission, we respect privacy and ensure fair information sharing. Iran phone number list helps you find contact numbers easily. This list includes phone numbers collected from trusted sources. We define this list to ensure you have accurate and reliable data. You can see the source websites to find out where we got the info. This transparency builds trust, so you feel confident using the list. Also, we offer 24/7 support to help you whenever you need it. Also, the data comes from opt-in sources, meaning people agreed to be contacted. Moreover, the Iran phone number list makes it easy to connect with people. You can trust the data because we collect it from reliable sources and verify it using the source URLs provided. Our customer support makes sure you can get answers anytime you need help.

This web map contains layers that contain some of the more commonly used variables from the General Community Profile information from the Australian Bureau of Statistics 2021 census. Data is available for Country, Greater Capital City Statistical Area (GCCSA), Local Government Area (LGA), Statistical Area Level 1 (SA1) and 2 (SA2), and Suburb and Localities (SAL) boundaries.The General Community Profile contains a series of tables showing the characteristics of persons, families and dwellings in a selected geographic area. The data is based on place of usual residence (that is, where people usually live, rather than where they were counted on Census night). Community Profiles are excellent tools for researching, planning and analysing geographic areas for a number of social, economic and demographic characteristics.Download the data here.Data and Geography notes:View the Readme files located in the DataPacks and GeoPackages zip files.To access the 2021 DataPacks, visit https://www.abs.gov.au/census/find-census-data/datapacksGlossary terms and definitions of classifications can be found in the 2021 Census DictionaryMore information about Census data products is available at https://www.abs.gov.au/census/guide-census-data/about-census-tools/datapacksDetailed geography information: https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/main-structure-and-greater-capital-city-statistical-areas: 2021 Statistical Area Level 1 (SA1), 2021 Statistical Area Level 2 (SA2), 2021 Greater Capital City Statistical Areas (GCCSA), 2021 Australia (AUS)https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/non-abs-structures: 2021 Suburbs and Localities (SAL), 2021 Local Government Areas (LGA)Please note that there are data assumptions that should be considered when analysing the ABS Census data. These are detailed within the Census documents referenced above. These include:Registered Marital StatusIn December 2017, amendments to the Marriage Act 1961 came into effect enabling marriage equality for all couples. For 2021, registered marriages include all couples.Core Activity Need for AssistanceMeasures the number of people with a profound or severe core activity limitation. People with a profound or severe core activity limitation are those needing assistance in their day to day lives in one or more of the three core activity areas of self-care, mobility and communication because of a long-term health condition (lasting six months or more), a disability (lasting six months or more), or old age. Number of Motor VehiclesExcludes motorbikes, motor scooters and heavy vehicles.Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.Source: Australian Bureau of Statistics

Japan number dataset allows you to filter phone numbers based on different criteria. You can pick contacts by gender, age, and whether they are single or taken. This feature makes it easy for you to find the right contacts for your needs. We define this title so you can access the most relevant information. Additionally, we regularly remove invalid data to keep the list accurate and reliable. Also, using the Japan number dataset makes your search much simpler. You can easily find contacts that fit your specific needs. Following GDPR rules helps us respect everyone’s privacy while providing useful information. Moreover, we always remove invalid data to keep the list correct. This way, you get the most reliable contact numbers. Japan Phone Data contains contact numbers collected from trusted sources. We define this title to make sure you have reliable and correct information. You can check the source URLs to see where we got the data. Moreover, we provide support 24/7 to help you with any questions. We are always available to support you. Additionally, we only collect opt-in data. This means that everyone on the list has agreed to share their contact details. With Japan Phone Data, you can feel confident that you have the right information. We gather data from trusted sources to ensure every number is correct. If you have any questions, you can reach out for help anytime. We want to help you connect with others easily. The List to Data helps you to find contact information for businesses. Japan phone number list helps you find the right phone numbers easily. You can filter this list by gender, age, and relationship status. This feature helps narrow your search and find exactly what you need. We define this list to provide the best data. Additionally, we remove invalid data regularly to keep the list fresh. Using the Japan phone number list is simple and quick. You can find contacts that match your needs without any hassle. Furthermore, we work hard to remove invalid data so you only see valid numbers. This effort helps keep your searches accurate and efficient. Overall, this list is a great tool for connecting with people in Japan while respecting their privacy.

This dataset contains data collected during a study "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems" conducted by Martin Lnenicka (University of Hradec Králové, Czech Republic), Anastasija Nikiforova (University of Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Serbia), Daniel Rudmark (Swedish National Road and Transport Research Institute, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Karlo Kević (University of Zagreb, Croatia), Anneke Zuiderwijk (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).

As there is a lack of understanding of the elements that constitute different types of value-adding public data ecosystems and how these elements form and shape the development of these ecosystems over time, which can lead to misguided efforts to develop future public data ecosystems, the aim of the study is: (1) to explore how public data ecosystems have developed over time and (2) to identify the value-adding elements and formative characteristics of public data ecosystems. Using an exploratory retrospective analysis and a deductive approach, we systematically review 148 studies published between 1994 and 2023. Based on the results, this study presents a typology of public data ecosystems and develops a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems, and develops a conceptual model of the evolutionary generation of public data ecosystems represented by six generations called Evolutionary Model of Public Data Ecosystems (EMPDE). Finally, three avenues for a future research agenda are proposed.

This dataset is being made public both to act as supplementary data for "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems ", Telematics and Informatics*, and its Systematic Literature Review component that informs the study.

Description of the data in this data set

PublicDataEcosystem_SLR provides the structure of the protocol

Spreadsheet#1 provides the list of results after the search over three indexing databases and filtering out irrelevant studies

Spreadsheets #2 provides the protocol structure.

Spreadsheets #3 provides the filled protocol for relevant studies.

The information on each selected study was collected in four categories:(1) descriptive information,(2) approach- and research design- related information,(3) quality-related information,(4) HVD determination-related information

Descriptive Information

Article number

A study number, corresponding to the study number assigned in an Excel worksheet

Complete reference

The complete source information to refer to the study (in APA style), including the author(s) of the study, the year in which it was published, the study's title and other source information.

Year of publication

The year in which the study was published.

Journal article / conference paper / book chapter

The type of the paper, i.e., journal article, conference paper, or book chapter.

Journal / conference / book

Journal article, conference, where the paper is published.

DOI / Website

A link to the website where the study can be found.

Number of words

A number of words of the study.

Number of citations in Scopus and WoS

The number of citations of the paper in Scopus and WoS digital libraries.

Availability in Open Access

Availability of a study in the Open Access or Free / Full Access.

Keywords

Keywords of the paper as indicated by the authors (in the paper).

Relevance for our study (high / medium / low)

What is the relevance level of the paper for our study

Approach- and research design-related information

Approach- and research design-related information

Objective / Aim / Goal / Purpose & Research Questions

The research objective and established RQs.

Research method (including unit of analysis)

The methods used to collect data in the study, including the unit of analysis that refers to the country, organisation, or other specific unit that has been analysed such as the number of use-cases or policy documents, number and scope of the SLR etc.

Study’s contributions

The study’s contribution as defined by the authors

Qualitative / quantitative / mixed method

Whether the study uses a qualitative, quantitative, or mixed methods approach?

Availability of the underlying research data

Whether the paper has a reference to the public availability of the underlying research data e.g., transcriptions of interviews, collected data etc., or explains why these data are not openly shared?

Period under investigation

Period (or moment) in which the study was conducted (e.g., January 2021-March 2022)

Use of theory / theoretical concepts / approaches? If yes, specify them

Does the study mention any theory / theoretical concepts / approaches? If yes, what theory / concepts / approaches? If any theory is mentioned, how is theory used in the study? (e.g., mentioned to explain a certain phenomenon, used as a framework for analysis, tested theory, theory mentioned in the future research section).

Quality-related information

Quality concerns

Whether there are any quality concerns (e.g., limited information about the research methods used)?

Public Data Ecosystem-related information

Public data ecosystem definition

How is the public data ecosystem defined in the paper and any other equivalent term, mostly infrastructure. If an alternative term is used, how is the public data ecosystem called in the paper?

Public data ecosystem evolution / development

Does the paper define the evolution of the public data ecosystem? If yes, how is it defined and what factors affect it?

What constitutes a public data ecosystem?

What constitutes a public data ecosystem (components & relationships) - their "FORM / OUTPUT" presented in the paper (general description with more detailed answers to further additional questions).

Components and relationships

What components does the public data ecosystem consist of and what are the relationships between these components? Alternative names for components - element, construct, concept, item, helix, dimension etc. (detailed description).

Stakeholders

What stakeholders (e.g., governments, citizens, businesses, Non-Governmental Organisations (NGOs) etc.) does the public data ecosystem involve?

Actors and their roles

What actors does the public data ecosystem involve? What are their roles?

Data (data types, data dynamism, data categories etc.)

What data do the public data ecosystem cover (is intended / designed for)? Refer to all data-related aspects, including but not limited to data types, data dynamism (static data, dynamic, real-time data, stream), prevailing data categories / domains / topics etc.

Processes / activities / dimensions, data lifecycle phases

What processes, activities, dimensions and data lifecycle phases (e.g., locate, acquire, download, reuse, transform, etc.) does the public data ecosystem involve or refer to?

Level (if relevant)

What is the level of the public data ecosystem covered in the paper? (e.g., city, municipal, regional, national (=country), supranational, international).

Other elements or relationships (if any)

What other elements or relationships does the public data ecosystem consist of?

Additional comments

Additional comments (e.g., what other topics affected the public data ecosystems and their elements, what is expected to affect the public data ecosystems in the future, what were important topics by which the period was characterised etc.).

New papers

Does the study refer to any other potentially relevant papers?

Additional references to potentially relevant papers that were found in the analysed paper (snowballing).

Format of the file.xls, .csv (for the first spreadsheet only), .docx

Licenses or restrictionsCC-BY

For more info, see README.txt

During a prefeasibility study conducted at the Muara Laboh geothermal prospect, geology, geochemistry, and geophysics (3G) data were acquired to define resource size, concession area boundaries, and to serve as input to a development plan to assess the project cost and project economics. In addition to 3G data, a heat loss survey was conducted to provide undisturbed heat flow of the geothermal system. A conceptual model of Muara Laboh was constructed and a numerical model was built on STARS-CMG constrained by heat loss and data from shallow wells. The model was used to estimate subsurface temperature distribution, to estimate the location and thickness of the outflow tongue, and to assist in defining well pad locations during the exploration stage.

Overview

This dataset contains version 3.0 (March 2025 release) of the Global Fishing Watch apparent fishing effort dataset. Data is available for 2012-2024 and based on positions of >190,000 unique automatic identification system (AIS) devices on fishing vessels, of which up to ~96,000 are active in a given year. Fishing vessels are identified via a machine learning model, vessel registry databases, and manual review by GFW and regional experts. Vessel time is measured in hours, calculated by assigning to each AIS position the amount of time elapsed since the previous AIS position of the vessel. The time is counted as apparent fishing hours if the GFW fishing detection model - a neural network machine learning model - determines the vessel is engaged in fishing behavior during that AIS position.

Data are spatially binned into grid cells that measure 0.01 or 0.1 degrees on a side; the coordinates defining each cell are provided in decimal degrees (WGS84) and correspond to the lower-left corner. Data are available in the following formats:

1. Daily apparent fishing hours by flag state and gear type at 100th degree resolution
2. Monthly apparent fishing hours by flag state and gear type at 10th degree resolution
3. Daily apparent fishing hours by MMSI at 10th degree resolution

The fishing effort dataset is accompanied by a table of vessel information (e.g. gear type, flag state, dimensions).

File structure

Fishing effort and vessel presence data are available as .csv files in daily formats. Files for each year are stored in separate .zip files. A README.txt and schema.json file is provided for each dataset version and contains the table schema and additional information. There is also a README-known-issues-v3.txt file outlining some of the known issues with the version 3 release.

Files are names according to the following convention:

• Daily file format:

• [fleet/mmsi]-daily-csvs-[100/10]-v3-[year].zip

• [fleet/mmsi]-daily-csvs-[100/10]-v3-[date].csv

• Monthly file format:

• fleet-monthly-csvs-10-v3-[year].zip

• fleet-monthly-csvs-10-v3-[date].csv

• Fishing vessel format: fishing-vessels-v3.csv

• README file format: README-[fleet/mmsi/fishing-vessels/known-issues]-v3.txt

File identifiers:

• [fleet/mmsi]: Data by fleet (flag and geartype) or by MMSI

• [100/10]: 100th or 10th degree resolution

• [year]: Year of data included in .zip file

• [date]: Date of data included in .csv files. For monthly data, [date]corresponds to the first date of the month

Examples: fleet-daily-csvs-100-v3-2020.zip; mmsi-daily-csvs-10-v3-2020-01-10.csv; fishing-vessels-v3.csv; README-fleet-v3.txt; fleet-monthly-csvs-10-v3-2024.zip; fleet-monthly-csvs-10-v3-2024-08-01.csv

Key documentation

• For an overview of how GFW turns raw AIS positions into estimates of fishing hours, see this page.

• The models used to produce this dataset were developed as part of this publication: D.A. Kroodsma, J. Mayorga, T. Hochberg, N.A. Miller, K. Boerder, F. Ferretti, A. Wilson, B. Bergman, T.D. White, B.A. Block, P. Woods, B. Sullivan, C. Costello, and B. Worm. "Tracking the global footprint of fisheries." Science 361.6378 (2018). Model details are available in the Supplementary Materials.

• The README-known-issues-v3.txt file describing this dataset's specific caveats can be downloaded from this page. We highly recommend that users read this file in full.

• The README-mmsi-v3.txt file, the README-fleet-v3.txt file, and the README-fishing-vessels-v3.txt files are downloadable from this page and contain the data description for (respectively) the fishing hours by MMSI dataset, the fishing hours by fleet dataset, and the vessel information file. These readmes contain key explanations about the gear types and flag states assigned to vessels in the dataset.

• File name structure for the datafiles are available below on this page and file schema can be downloaded from this page.

• A FAQ describing the updates in this version and the differences between this dataset and the data available from the GFW Map and APIs is available here.

Use Cases

The apparent fishing hours dataset is intended to allow users to analyze patterns of fishing across the world’s oceans at temporal scales as fine as daily and at spatial scales as fine as 0.1 or 0.01 degree cells. Fishing hours can be separated out by gear type, vessel flag and other characteristics of vessels such as tonnage.

Potential applications for this dataset are broad. We offer suggested use cases to illustrate its utility. The dataset can be integrated as a static layer in multi-layered analyses, allowing researchers to investigate relationships between fishing effort and other variables, including biodiversity, tracking, and environmental data, as defined by their research objectives.

A few example questions that these data could be used to answer:

• What flag states have fishing activity in my area of interest?

• Do hotspots of longline fishing overlap with known migration routes of sea turtles?

• How does fishing time by trawlers change by month in my area of interest? Which seasons see the most trawling hours and which see the least?

Caveats

This global dataset estimates apparent fishing hours effort. The dataset is based on publicly available information and statistical classifications which may not fully capture the nuances of local fishing practices. While we manually review the dataset at a global scale and in a select set of smaller test regions to check for issues, given the scale of the dataset we are unable to manually review every fleet in every region. We recognize the potential for inaccuracies and encourage users to approach regional analyses with caution, utilizing their own regional expertise to validate findings. We welcome your feedback on any regional analysis at research@globalfishingwatch.org to enhance the dataset's accuracy.

Caveats relating to known sources of inaccuracy as well as interpretation pitfalls to avoid are described in the README-known-issues-v3.txt file available for download from this page. We highly recommend that users read this file in full. The issues described include:

• Data from 2024 should be considered provisional, as vessel classifications may change as more data from 2025 becomes available.

• MMSI is used in this dataset as the vessel identifier. While MMSI is intended to serve as the unique AIS identifier for an individual vessel, this does not always hold in practice.

• The Maritime Identification Digits (MID), the first 3 digits of MMSI, are the only source of information on vessel flag state when the vessel does not appear on a registry. The MID may be entered incorrectly, obscuring information about an MMSI’s flag state.

• AIS reception is not consistent across all areas and changes over time.

Alternative ways to access

1. Query using SQL in the Global Fishing Watch public BigQuery dataset: global-fishing-watch.fishing_effort_v3

2. Download the entire dataset from the Global Fishing Watch Data Download Portal (https://globalfishingwatch.org/data-download/datasets/public-fishing-effort)

Collective intelligence constitutes a foundational element within online community question-and-answering (CQA) platforms, such as Stack Overflow, being the source of most programming-related issues. Despite this relevance, concerns remain regarding issues surrounding user participation. Precedent research tends to focus on simple numerical measurements to analyse participation, which may sideline the inherent, subtler aspects. The proposed study aims to bridge this gap by operationalising 11 distinct metrics to represent user participation, behaviour, and community value across different regions of the USA. The study also conducts inductive content analysis to understand the impact of regional contextual factors on users' knowledge sharing patterns. This replication package is provided for those interested in further examining our research methodology.

The main dataset is a 232 MB file of trajectory data (I395-final.csv) that contains position, speed, and acceleration data for non-automated passenger cars, trucks, buses, and automated vehicles on an expressway within an urban environment. Supporting files include an aerial reference image (I395_ref_image.png) and a list of polygon boundaries (I395_boundaries.csv) and associated images (I395_lane-1, I395_lane-2, …, I395_lane-6) stored in a folder titled “Annotation on Regions.zip” to map physical roadway segments to the numerical lane IDs referenced in the trajectory dataset. In the boundary file, columns “x1” to “x5” represent the horizontal pixel values in the reference image, with “x1” being the leftmost boundary line and “x5” being the rightmost boundary line, while the column "y" represents corresponding vertical pixel values. The origin point of the reference image is located at the top left corner. The dataset defines five lanes with five boundaries. Lane -6 corresponds to the area to the left of “x1”. Lane -5 corresponds to the area between “x1” and “x2”, and so forth to the rightmost lane, which is defined by the area to the right of “x5” (Lane -2). Lane -1 refers to vehicles that go onto the shoulder of the merging lane (Lane -2), which are manually separated by watching the videos.

This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which was one of the six collected as part of the TGSIM project, contains data collected from six 4K cameras mounted on tripods, positioned on three overpasses along I-395 in Washington, D.C. The cameras captured distinct segments of the highway, and their combined overlapping and non-overlapping footage resulted in a continuous trajectory for the entire section covering 0.5 km. This section covers a major weaving/mandatory lane-changing between L'Enfant Plaza and 4th Street SW, with three lanes in the eastbound direction and a major on-ramp on the left side. In addition to the on-ramp, the section covers an off-ramp on the right side. The expressway includes one diverging lane at the beginning of the section on the right side and one merging lane in the middle of the section on the left side. For the purposes of data extraction, the shoulder of the merging lane is also considered a travel lane since some vehicles illegally use it as an extended on-ramp to pass other drivers (see I395_ref_image.png for details). The cameras captured continuous footage during the morning rush hour (8:30 AM-10:30 AM ET) on a sunny day. During this period, vehicles equipped with SAE Level 2 automation were deployed to travel through the designated section to capture the impact of SAE Level 2-equipped vehicles on adjacent vehicles and their behavior in congested areas, particularly in complex merging sections. These vehicles are indicated in the dataset.

As part of this dataset, the following files were provided:

• I395-final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.
• I395_ref_image.png is the aerial reference image that defines the geographic region and the associated roadway segments.
• I395_boundaries.csv contains the coordinates that define the roadway segments (n=X). The columns "x1" to "x5" represent the horizontal pi

IABSE Task Group 3.1 has the mandate to define reference results for the validation of methodologies and programs used to study both stability and buffeting responses of long-span bridges. To this end, the working group set up a benchmark procedure consisting of several steps with increasing complexity to define reference results useful for this validation. The simplest step (1.1a) was presented in Part 1. In this paper (Part 2), the contributions and reference results of the second sub-step (1.1c) are discussed. It consists of the simulation of the aeroelastic response of a three-degree-of-freedom bridge deck section forced by turbulent wind, using experimental aerodynamic coefficients measured in a wind tunnel. The increase in complexity, compared to the previous step, involves the experimental definition of unsteady force coefficients that are defined in a limited range of reduced velocities, and inclusion of the lateral motion and horizontal turbulent wind velocity components. Comparison of the different outputs, obtained by Task Group 3.1 participants with the same input data, is presented, revealing differences that are not always negligible. Moreover, the increase in complexity of the test case results in larger spreads of the results compared to the fully analytical case, analysed in Part 1.

ABSTRACT Metallic components for engineering applications are often employed under the action of thermal and mechanical loads, which tend to reduce their lifespan. Mechanical surface treatments appear as an alternative capable of extending the service life of these materials. These processes act by plastically deforming the surface, increasing its surface hardness and inducing compressive residual stresses. The combination of these factors leads to an increase of the part lifespan because it diminishes the nucleation and propagation of cracks, which are responsible for material failure by fracture. Although surface treatment processes such as shot peening and laser shock peening are widely used to induce compressive stresses, roller burnishing not only induces compressive stress and increases surface hardness, but also promotes the reduction of surface roughness. In this work, two-dimensional numerical simulation using the Finite Element Method (FEM) method for roller burnishing the hardened ABNT 4140 (40 HRC) steel was performed, considering the tool a rigid element, while the part was assumed as elasto-plastic. Burnishing force and feed rate were selected as input parameters which influence on roughness and residual stress induction was investigated. Moreover, unlike other two-dimensional numerical models, the initial roughness of the part was introduced in order to assess its influence on the process. In addition to that, the numerical simulation occurs in the interval from one peak to the other, defined by the feed mark of the previous turning operation (0.2 mm). The HertzWin software was used to define the feed rate for each condition, which was based on the half contact width (b) obtained for each force separately. The numerical results suggest the reduction of roughness and the increase of compressive residual when the rolling force is increased and the feed rate is reduced.

Despite the diverse genetic origins of autism spectrum disorders (ASDs), affected individuals share strikingly similar and correlated behavioural traits that include perceptual and sensory processing challenges. Notably, the severity of these sensory symptoms is often predictive of the expression of other autistic traits. However, the origin of these perceptual deficits remains largely elusive. Here, we show a recurrent impairment in visual threat perception that is similarly impaired in 3 independent mouse models of ASD with different molecular aetiologies. Interestingly, this deficit is associated with reduced avoidance of threatening environments—a nonperceptual trait. Focusing on a common cause of ASDs, the Setd5 gene mutation, we define the molecular mechanism. We show that the perceptual impairment is caused by a potassium channel (Kv1)-mediated hypoexcitability in a subcortical node essential for the initiation of escape responses, the dorsal periaqueductal grey (dPAG). Targeted pharmacological Kv1 blockade rescued both perceptual and place avoidance deficits, causally linking seemingly unrelated trait deficits to the dPAG. Furthermore, we show that different molecular mechanisms converge on similar behavioural phenotypes by demonstrating that the autism models Cul3 and Ptchd1, despite having similar behavioural phenotypes, differ in their functional and molecular alteration. Our findings reveal a link between rapid perception controlled by subcortical pathways and appropriate learned interactions with the environment and define a nondevelopmental source of such deficits in ASD.

The 2011 Population and Housing Census marks a milestone in census exercises in Europe. For the first time, European legislation defined in detail a set of harmonised high-quality data from the population and housing censuses conducted in the EU Member States. As a result, the data from the 2011 round of censuses offer exceptional flexibility to cross-tabulate different variables and to provide geographically detailed data.

EU Member States have developed different methods to produce these census data. The national differences reflect the specific national situations in terms of data source availability, as well as the administrative practices and traditions of that country.

The EU census legislation respects this diversity. The Regulation of the European Parliament and of the Council on population and housing censuses (Regulation (EC) No 763/2008) is focussed on output harmonisation rather than input harmonisation. Member States are free to assess for themselves how to conduct their 2011 censuses and which data sources, methods and technology should be applied given the national context. This gives the Member States flexibility, in line with the principles of subsidiarity and efficiency, and with the competences of the statistical institutes in the Member States.

However, certain important conditions must be met in order to achieve the objective of comparability of census data from different Member States and to assess the data quality:

Regulation (EC) No 1201/20092 contains definitions and technical specifications for the census topics (variables) and their breakdowns that are required to achieve Europe-wide comparability.

The specifications are based closely on international recommendations and have been designed to provide the best possible information value. The census topics include geographic, demographic, economic and educational characteristics of persons, international and internal migration characteristics as well as household, family and housing characteristics.

Regulation (EU) No 519/2010 requires the data outputs that Member States transmit to the Eurostat to comply with a defined programme of statistical data (tabulation) and with set rules concerning the replacement of statistical data. The content of the EU census programme serves major policy needs of the European Union. Regionally, there is a strong focus on the NUTS 2 level. The data requirements are adapted to the level of regional detail. The Regulation does not require transmission of any data that the Member States consider to be confidential.

The statistical data must be completed by metadata that will facilitate interpretation of the numerical data, including country-specific definitions plus information on the data sources and on methodological issues. This is necessary in order to achieve the transparency that is a condition for valid interpretation of the data.

Users of output-harmonised census data from the EU Member States need to have detailed information on the quality of the censuses and their results.

Regulation (EU) No 1151/2010) therefore requires transmission of a quality report containing a systematic description of the data sources used for census purposes in the Member States and of the quality of the census results produced from these sources. A comparably structured quality report for all EU Member States will support the exchange of experience from the 2011 round and become a reference for future development of census methodology (EU legislation on the 2011 Population and Housing Censuses - Explanatory Notes ).

In order to ensure proper transmission of the data and metadata and provide user-friendly access to this information, a common technical format is set for transmission for all Member States and for the Commission (Eurostat). The Regulation therefore requires the data to be transmitted in a harmonised structure and in the internationally established SDMX format from every Member State. In order to achieve this harmonised transmission, a new system has been developed – the CENSUS HUB.

The Census Hub is a conceptually new system used for the dissemination of the 2011 Census. It is based on the concept of data sharing, where a group of partners (Eurostat on one hand and National Statistical Institutes on the other) agree to provide access to their data according to standard processes, formats and technologies.

The Census Hub is a readily-accessible system that provided the following functions:

• Data providers (the NSIs) can make data available directly from their systems through a querying system. In parallel,
• Data users browse the hub to define a dataset of interest via the above structural metadata and retrieve the dataset from the NSIs.

From the data management point of view, the hub is based on agreed hypercubes (data-sets in the form of multi-dimensional aggregations). The hypercubes are not sent to the central system. Instead the following process operates:

1. a user defines a dataset through the web interface of the central hub and requests it;
2. the central hub translates the user request in one or more queries and sends them to the related NSIs’ systems;
3. NSIs’ systems process the query and send the result to the central hub in a standard format;
4. the central hub puts together all the results sent by the NSI systems and presents them in a user-specified format.

The main dataset is a 350 MB file of trajectory data (TGSIM-Foggy Bottom-Data.csv) that contains position, speed, and acceleration data for pedestrians, bicycles, scooters, non-automated passenger cars, automated vehicles, motorcycles, buses, and trucks in an urban environment. Supporting files include an aerial reference image (Reference_Image_Foggy Bottom.png) and a list of polygon boundaries (Foggy_Bottom_boundaries.txt) and associated images (i1.png, i2.png, …, i49.png stored in the folder titled “Annotation on Regions.zip”) to map physical roadway segments to numerical IDs (as referenced in the trajectory dataset).

This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected from twelve 4K stationary infrastructure cameras installed in the Foggy Bottom neighborhood of Washington, D.C. The cameras captured four intersections, adjacent crosswalks, road segments between the intersections, and partial road segments extending out from the intersections totaling more than one full block of coverage. These segments are represented by polygons to bound travel lanes, parking lanes, crosswalks, and intersections for detection and analysis purposes (see Reference_Image_Foggy Bottom.png for details). The cameras captured continuous footage during a weekday commute between 3:00PM-5:00PM ET on a sunny day. During this period, one test vehicle equipped with SAE Level 3 automation was deployed to perform various complex maneuvers at both stop signs and traffic signals, including both protected and permitted left turns, to capture human driving behaviors when interacting with automated vehicles. The automated vehicles are indicated in the dataset.

As part of this dataset, the following files were provided:

• TGSIM-Foggy Bottom-Data.csv contains the numerical data to be used for analysis that includes vehicle/bicycle/pedestrian trajectory data at every 0.1 second. Road user type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.0186613838586-meter conversion.
• Reference_Image_Foggy Bottom.png is the aerial reference image that defines the geographic region and the associated roadway segments.
• Foggy_Bottom_boundaries.txt contains the coordinates that define the roadway segments (n = 49). Each polygon is a list of four to six coordinate pairs measured in pixels (which can be converted to meters using the provided 1 pixel = 0.0186613838586-meter conversion), with (0,0) global reference coordinates at the top-left of the reference image.
• Annotation on Regions.zip, which includes i1.png, i2.png,..., i49.png, are images that visually map the road segment IDs (indicated by the number following the i in the file name) to the reference image.