A challenge set for elementary-level Math Word Problems (MWP). An MWP consists of a short Natural Language narrative that describes a state of the world and poses a question about some unknown quantities.

The examples in SVAMP test a model across different aspects of solving MWPs: 1) Is the model question sensitive? 2) Does the model have robust reasoning ability? 3) Is it invariant to structural alterations?

Third grade English Language Arts (ELA) and Math test results for the 2016-2017 school year by census tract for the state of Michigan. Data Driven Detroit obtained these datasets from MI School Data, for the State of the Detroit Child tool in July 2017. Test results were originally obtained on a school level and aggregated to census tract by Data Driven Detroit. Student data was suppressed when less than five students were tested per school.Click here for metadata (descriptions of the fields).

The Trends in International Mathematics and Science Study, 2015 (TIMSS 2015) is a data collection that is part of the Trends in International Mathematics and Science Study (TIMSS) program; program data are available since 1999 at . TIMSS 2015 (https://nces.ed.gov/timss/) is a cross-sectional study that provides international comparative information of the mathematics and science literacy of fourth-, eighth-, and twelfth-grade students and examines factors that may be associated with the acquisition of math and science literacy in students. The study was conducted using direct assessments of students and questionnaires for students, teachers, and school administrators. Fourth-, eighth-, and twelfth-graders in the 2014-15 school year were sampled. Key statistics produced from TIMSS 2015 provide reliable and timely data on the mathematics and science achievement of U.S. students compared to that of students in other countries. Data are expected to be released in 2018.

qwertyuiopasdfg/Filtered-math dataset hosted on Hugging Face and contributed by the HF Datasets community

Increase the percentage of 8th grade students statewide who score proficient or above in math from 69.9% in 2013 to NA% by 2019 (target not yet established).

Here we provide preprocessed fMRI data and analysis scripts used in "Quantitative modeling demonstrates format-invariant representations of mathematical problems in the brain”

The preprocessed data are provided for each participant (sub-01 to sub-08).

The analysis scripts require MATLAB (R2019b) and FreeSurfer.

Ridge regression

ID = 'sub-01';
Method = 'Operator';
Code = 1; 
%1: all operators
%2: only single operators
%3: only double operators
Ridge(ID, Method, Code)
Ridge_CrossModal(ID, Method, Code)

Modality invariance and modality specificity

ModInv(ID, Method, Code)
ModInv_MinusRead(ID, Method, Code)
ModSpec(ID, Method, Code)

Representational similarity analysis

RSM_Group_WholeCortex(Method, Code)

RSM_Group_WholeCortex(Method, Code)
RSM_Group_WholeCortex_MinusRead(Method, Code)

Principal component analysis

PCA_Group_WholeCortex(Method, Code)

If you have any questions, please send an email to nakai.tomoya [at] neuro.mimoza.jp.

Long-range arena (LRA) is an effort toward systematic evaluation of efficient transformer models. The project aims at establishing benchmark tasks/datasets using which we can evaluate transformer-based models in a systematic way, by assessing their generalization power, computational efficiency, memory foot-print, etc. Long-Range Arena is specifically focused on evaluating model quality under long-context scenarios. The benchmark is a suite of tasks consisting of sequences ranging from 1K to 16K tokens, encompassing a wide range of data types and modalities such as text, natural, synthetic images, and mathematical expressions requiring similarity, structural, and visual-spatial reasoning.

Description from: Long Range Arena : A Benchmark for Efficient Transformers

This dataset shows all school level performance data used to create CPS School Report Cards for the 2011-2012 school year. Metrics are described as follows (also available for download at http://bit.ly/uhbzah): NDA indicates "No Data Available." SAFETY ICON: Student Perception/Safety category from 5 Essentials survey // SAFETY SCORE: Student Perception/Safety score from 5 Essentials survey // FAMILY INVOLVEMENT ICON: Involved Families category from 5 Essentials survey // FAMILY INVOLVEMENT SCORE: Involved Families score from 5 Essentials survey // ENVIRONMENT ICON: Supportive Environment category from 5 Essentials survey // ENVIRONMENT SCORE: Supportive Environment score from 5 Essentials survey // INSTRUCTION ICON: Ambitious Instruction category from 5 Essentials survey // INSTRUCTION SCORE: Ambitious Instruction score from 5 Essentials survey // LEADERS ICON: Effective Leaders category from 5 Essentials survey // LEADERS SCORE: Effective Leaders score from 5 Essentials survey // TEACHERS ICON: Collaborative Teachers category from 5 Essentials survey // TEACHERS SCORE: Collaborative Teachers score from 5 Essentials survey // PARENT ENGAGEMENT ICON: Parent Perception/Engagement category from parent survey // PARENT ENGAGEMENT SCORE: Parent Perception/Engagement score from parent survey // AVERAGE STUDENT ATTENDANCE: Average daily student attendance // RATE OF MISCONDUCTS (PER 100 STUDENTS): # of misconducts per 100 students//AVERAGE TEACHER ATTENDANCE: Average daily teacher attendance // INDIVIDUALIZED EDUCATION PROGRAM COMPLIANCE RATE: % of IEPs and 504 plans completed by due date // PK-2 LITERACY: % of students at benchmark on DIBELS or IDEL // PK-2 MATH: % of students at benchmark on mClass // GR3-5 GRADE LEVEL MATH: % of students at grade level, math, grades 3-5 // GR3-5 GRADE LEVEL READ: % of students at grade level, reading, grades 3-5 // GR3-5 KEEP PACE READ: % of students meeting growth targets, reading, grades 3-5 // GR3-5 KEEP PACE MATH: % of students meeting growth targets, math, grades 3-5 // GR6-8 GRADE LEVEL MATH: % of students at grade level, math, grades 6-8 // GR6-8 GRADE LEVEL READ: % of students at grade level, reading, grades 6-8 // GR6-8 KEEP PACE MATH: % of students meeting growth targets, math, grades 6-8 // GR6-8 KEEP PACE READ: % of students meeting growth targets, reading, grades 6-8 // GR-8 EXPLORE MATH: % of students at college readiness benchmark, math // GR-8 EXPLORE READ: % of students at college readiness benchmark, reading // ISAT EXCEEDING MATH: % of students exceeding on ISAT, math // ISAT EXCEEDING READ: % of students exceeding on ISAT, reading // ISAT VALUE ADD MATH: ISAT value-add value, math // ISAT VALUE ADD READ: ISAT value-add value, reading // ISAT VALUE ADD COLOR MATH: ISAT value-add color, math // ISAT VALUE ADD COLOR READ: ISAT value-add color, reading // STUDENTS TAKING ALGEBRA: % of students taking algebra // STUDENTS PASSING ALGEBRA: % of students passing algebra // 9TH GRADE EXPLORE (2009): Average EXPLORE score, 9th graders who tested in fall 2009 // 9TH GRADE EXPLORE (2010): Average EXPLORE score, 9th graders who tested in fall 2010 // 10TH GRADE PLAN (2009): Average PLAN score, 10th graders who tested in fall 2009 // 10TH GRADE PLAN (2010): Average PLAN score, 10th graders who tested in fall 2010 // NET CHANGE EXPLORE AND PLAN: Difference between Grade 9 Explore (2009) and Grade 10 Plan (2010) // 11TH GRADE AVERAGE ACT (2011): Average ACT score, 11th graders who tested in fall 2011 // NET CHANGE PLAN AND ACT: Difference between Grade 10 Plan (2009) and Grade 11 ACT (2011) // COLLEGE ELIGIBILITY: % of graduates eligible for a selective four-year college // GRADUATION RATE: % of students who have graduated within five years // COLLEGE/ ENROLLMENT RATE: % of students enrolled in college // COLLEGE ENROLLMENT (NUMBER OF STUDENTS): Total school enrollment // FRESHMAN ON TRACK RATE: Freshmen On-Track rate // RCDTS: Region County District Type Schools Code

This tile contains all multibeam data held by Geoscience Australia on August 2012 within the specified area. The data has been gridded to 50m resolution.

Some deeper data has also been interpolated within the mapped area.

The image provided can be viewed on the free software CARIS Easyview, available from the CARIS website: www.caris.com under Free Downloads.

You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html

CriticBench is a comprehensive benchmark designed to assess the abilities of Large Language Models (LLMs) to critique and rectify their reasoning across various tasks. It encompasses five reasoning domains:

Mathematical Commonsense Symbolic Coding Algorithmic

CriticBench compiles 15 datasets and incorporates responses from three LLM families. By utilizing CriticBench, researchers evaluate and dissect the performance of 17 LLMs in generation, critique, and correction reasoning (referred to as GQC reasoning). Notable findings include:

A linear relationship in GQC capabilities, with critique-focused training significantly enhancing performance. Task-dependent variation in correction effectiveness, with logic-oriented tasks being more amenable to correction. GQC knowledge inconsistencies that decrease as model size increases. An intriguing inter-model critiquing dynamic, where stronger models excel at critiquing weaker ones, while weaker models surprisingly surpass stronger ones in self-critique.

(1) CriticBench: Benchmarking LLMs for Critique-Correct Reasoning. https://arxiv.org/abs/2402.14809. (2) CriticBench: Benchmarking LLMs for Critique-Correct Reasoning. http://export.arxiv.org/abs/2402.14809. (3) CriticBench: Benchmarking LLMs for Critique-Correct Reasoning. https://openreview.net/forum?id=sc5i7q6DQO. (4) CriticBench: Benchmarking LLMs for Critique-Correct Reasoning - arXiv.org. https://arxiv.org/html/2402.14809v2. (5) undefined. https://doi.org/10.48550/arXiv.2402.14809.

The Evolution of the Manosphere Across the Web

We make available data related to subreddit and standalone forums from the manosphere.

We also make available Perspective API annotations for all posts.

You can find the code in GitHub.

Please cite this paper if you use this data:

@article{ribeiroevolution2021, title={The Evolution of the Manosphere Across the Web}, author={Ribeiro, Manoel Horta and Blackburn, Jeremy and Bradlyn, Barry and De Cristofaro, Emiliano and Stringhini, Gianluca and Long, Summer and Greenberg, Stephanie and Zannettou, Savvas}, booktitle = {{Proceedings of the 15th International AAAI Conference on Weblogs and Social Media (ICWSM'21)}}, year={2021} }

1. Reddit data

We make available data for forums and for relevant subreddits (56 of them, as described in subreddit_descriptions.csv). These are available, 1 line per post in each subreddit Reddit in /ndjson/reddit.ndjson. A sample for example is:

{ "author": "Handheld_Gaming", "date_post": 1546300852, "id_post": "abcusl", "number_post": 9.0, "subreddit": "Braincels", "text_post": "Its been 2019 for almost 1 hour And I am at a party with 120 people, half of them being foids. The last year had been the best in my life. I actually was happy living hope because I was redpilled to the death.

Now that I am blackpilled I see that I am the shortest of all men and that I am the only one with a recessed jaw.

Its over. Its only thanks to my age old friendship with chads and my social skills I had developed in the past year that a lot of men like me a lot as a friend.

No leg lengthening syrgery is gonna save me. Ignorance was a bliss. Its just horror now seeing that everyone can make out wirth some slin hoe at the party.

I actually feel so unbelivably bad for turbomanlets. Life as an unattractive manlet is a pain, I cant imagine the hell being an ugly turbomanlet is like. I would have roped instsntly if I were one. Its so unfair.

Tallcels are fakecels and they all can (and should) suck my cock.

If I were 17cm taller my life would be a heaven and I would be the happiest man alive.

Just cope and wait for affordable body tranpslants.", "thread": "t3_abcusl" }

1. Forums

We here describe the .sqlite and .ndjson files that contain the data from the following forums.

(avfm) --- https://d2ec906f9aea-003845.vbulletin.net (incels) --- https://incels.co/ (love_shy) --- http://love-shy.com/lsbb/ (redpilltalk) --- https://redpilltalk.com/ (mgtow) --- https://www.mgtow.com/forums/ (rooshv) --- https://www.rooshvforum.com/ (pua_forum) --- https://www.pick-up-artist-forum.com/ (the_attraction) --- http://www.theattractionforums.com/

The files are in folders /sqlite/ and /ndjson.

2.1 .sqlite

All the tables in the sqlite. datasets follow a very simple {key:value} format. Each key is a thread name (for example /threads/housewife-is-like-a-job.123835/) and each value is a python dictionary or a list. This file contains three tables:

idx each key is the relative address to a thread and maps to a post. Each post is represented by a dict:

"type": (list) in some forums you can add a descriptor such as [RageFuel] to each topic, and you may also have special types of posts, like sticked/pool/locked posts.
"title": (str) title of the thread; "link": (str) link to the thread; "author_topic": (str) username that created the thread; "replies": (int) number of replies, may differ from number of posts due to difference in crawling date; "views": (int) number of views; "subforum": (str) name of the subforum; "collected": (bool) indicates if raw posts have been collected; "crawled_idx_at": (str) datetime of the collection.

processed_posts each key is the relative address to a thread and maps to a list with posts (in order). Each post is represented by a dict:

"author": (str) author's username; "resume_author": (str) author's little description; "joined_author": (str) date author joined; "messages_author": (int) number of messages the author has; "text_post": (str) text of the main post; "number_post": (int) number of the post in the thread; "id_post": (str) unique post identifier (depends), for sure unique within thread; "id_post_interaction": (list) list with other posts ids this post quoted; "date_post": (str) datetime of the post, "links": (tuple) nice tuple with the url parsed, e.g. ('https', 'www.youtube.com', '/S5t6K9iwcdw'); "thread": (str) same as key; "crawled_at": (str) datetime of the collection.

raw_posts each key is the relative address to a thread and maps to a list with unprocessed posts (in order). Each post is represented by a dict:

"post_raw": (binary) raw html binary; "crawled_at": (str) datetime of the collection.

2.2 .ndjson

Each line consists of a json object representing a different comment with the following fields:

"author": (str) author's username; "resume_author": (str) author's little description; "joined_author": (str) date author joined; "messages_author": (int) number of messages the author has; "text_post": (str) text of the main post; "number_post": (int) number of the post in the thread; "id_post": (str) unique post identifier (depends), for sure unique within thread; "id_post_interaction": (list) list with other posts ids this post quoted; "date_post": (str) datetime of the post, "links": (tuple) nice tuple with the url parsed, e.g. ('https', 'www.youtube.com', '/S5t6K9iwcdw'); "thread": (str) same as key; "crawled_at": (str) datetime of the collection.

1. Perspective

We also run each post and reddit post through perspective, the files are located in the /perspective/ folder. They are compressed with gzip. One example output

{ "id_post": 5200, "hate_output": { "text": "I still can\u2019t wrap my mind around both of those articles about these c~~~s sleeping with poor Haitian Men. Where\u2019s the uproar?, where the hell is the outcry?, the \u201cpig\u201d comments or the \u201ccreeper comments\u201d. F~~~ing hell, if roles were reversed and it was an article about Men going to Europe where under 18 sex in legal, you better believe they would crucify the writer of that article and DEMAND an apology by the paper that wrote it.. This is exactly what I try and explain to people about the double standards within our modern society. A bunch of older women, wanna get their kicks off by sleeping with poor Men, just before they either hit or are at menopause age. F~~~ing unreal, I\u2019ll never forget going to Sweden and Norway a few years ago with one of my buddies and his girlfriend who was from there, the legal age of consent in Norway is 16 and in Sweden it\u2019s 15. I couldn\u2019t believe it, but my friend told me \u201c hey, it\u2019s normal here\u201d . Not only that but the age wasn\u2019t a big different in other European countries as well. One thing i learned very quickly was how very Misandric Sweden as well as Denmark were.", "TOXICITY": 0.6079781, "SEVERE_TOXICITY": 0.53744453, "INFLAMMATORY": 0.7279288, "PROFANITY": 0.58842486, "INSULT": 0.5511079, "OBSCENE": 0.9830818, "SPAM": 0.17009115 } }

1. Working with sqlite

A nice way to read some of the files of the dataset is using SqliteDict, for example:

from sqlitedict import SqliteDict processed_posts = SqliteDict("./data/forums/incels.sqlite", tablename="processed_posts")

for key, posts in processed_posts.items(): for post in posts: # here you could do something with each post in the dataset pass

1. Helpers

Additionally, we provide two .sqlite files that are helpers used in the analyses. These are related to reddit, and not to the forums! They are:

channel_dict.sqlite a sqlite where each key corresponds to a subreddit and values are lists of dictionaries users who posted on it, along with timestamps.

author_dict.sqlite a sqlite where each key corresponds to an author and values are lists of dictionaries of the subreddits they posted on, along with timestamps.

These are used in the paper for the migration analyses.

1. Examples and particularities for forums

Although we did our best to clean the data and be consistent across forums, this is not always possible. In the following subsections we talk about the particularities of each forum, directions to improve the parsing which were not pursued as well as give some examples on how things work in each forum.

6.1 incels

Check out an archived version of the front page, the thread page and a post page, as well as a dump of the data stored for a thread page and a post page.

types: for the incel forums the special types associated with each thread in the idx table are “Sticky”, “Pool”, “Closed”, and the custom types added by users, such as [LifeFuel]. These last ones are all in brackets. You can see some examples of these in the on the example thread page.

quotes: quotes in this forum were quite nice and thus, all quotations are deterministic.

6.2 LoveShy

Check out an archived version of the front page, the thread page and a post page, as well as a dump of the data stored for a thread page and a post page.

types: no types were parsed. There are some rules in the forum, but not significant.

quotes: quotes were obtained from exact text+author match, or author match + a jaccard

911 Public Safety Answering Point (PSAP) service area boundaries in New Mexico According to the National Emergency Number Association (NENA), a Public Safety Answering Point (PSAP) is a facility equipped and staffed to receive 9-1-1 calls. The service area is the geographic area within which a 911 call placed using a landline is answered at the associated PSAP. This dataset only includes primary PSAPs. Secondary PSAPs, backup PSAPs, and wireless PSAPs have been excluded from this dataset. Primary PSAPs receive calls directly, whereas secondary PSAPs receive calls that have been transferred by a primary PSAP. Backup PSAPs provide service in cases where another PSAP is inoperable. Most military bases have their own emergency telephone systems. To connect to such system from within a military base it may be necessary to dial a number other than 9 1 1. Due to the sensitive nature of military installations, TGS did not actively research these systems. If civilian authorities in surrounding areas volunteered information about these systems or if adding a military PSAP was necessary to fill a hole in civilian provided data, TGS included it in this dataset. Otherwise military installations are depicted as being covered by one or more adjoining civilian emergency telephone systems. In some cases areas are covered by more than one PSAP boundary. In these cases, any of the applicable PSAPs may take a 911 call. Where a specific call is routed may depend on how busy the applicable PSAPS are (i.e. load balancing), operational status (i.e. redundancy), or time of date / day of week. If an area does not have 911 service, TGS included that area in the dataset along with the address and phone number of their dispatch center. These are areas where someone must dial a 7 or 10 digit number to get emergency services. These records can be identified by a "Y" in the [NON911EMNO] field. This indicates that dialing 911 inside one of these areas does not connect one with emergency services. This dataset was constructed by gathering information about PSAPs from state level officials. In some cases this was geospatial information, in others it was tabular. This information was supplemented with a list of PSAPs from the Federal Communications Commission (FCC). Each PSAP was researched to verify its tabular information. In cases where the source data was not geospatial, each PSAP was researched to determine its service area in terms of existing boundaries (e.g. city and county boundaries). In some cases existing boundaries had to be modified to reflect coverage areas (e.g. "entire county north of Country Road 30"). However, there may be cases where minor deviations from existing boundaries are not reflected in this dataset, such as the case where a particular PSAPs coverage area includes an entire county, and the homes and businesses along a road which is partly in another county. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics.

COMBINE archive containing a working SBML model with semantic annotation in a separate RDF file.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Address Ranges Feature Shapefile (ADDRFEAT.dbf) contains the geospatial edge geometry and attributes of all unsuppressed address ranges for a county or county equivalent area. The term "address range" refers to the collection of all possible structure numbers from the first structure number to the last structure number and all numbers of a specified parity in between along an edge side relative to the direction in which the edge is coded. Single-address address ranges have been suppressed to maintain the confidentiality of the addresses they describe. Multiple coincident address range feature edge records are represented in the shapefile if more than one left or right address ranges are associated to the edge. The ADDRFEAT shapefile contains a record for each address range to street name combination. Address range associated to more than one street name are also represented by multiple coincident address range feature edge records. Note that the ADDRFEAT shapefile includes all unsuppressed address ranges compared to the All Lines Shapefile (EDGES.shp) which only includes the most inclusive address range associated with each side of a street edge. The TIGER/Line shapefile contain potential address ranges, not individual addresses. The address ranges in the TIGER/Line Files are potential ranges that include the full range of possible structure numbers even though the actual structures may not exist.

Abstract copyright UK Data Service and data collection copyright owner.BackgroundThe Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The Annual Population Survey, also held at the UK Data Archive, is derived from the LFS.The LFS was first conducted biennially from 1973-1983, then annually between 1984 and 1991, comprising a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter. From 1992 it moved to a quarterly cycle with a sample size approximately equivalent to that of the previous annual data. Northern Ireland was also included in the survey from December 1994. Further information on the background to the QLFS may be found in the documentation.The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.LFS DocumentationThe documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned (the latest questionnaire available covers July-September 2022). Volumes are updated periodically, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.LFS response to COVID-19From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.Occupation data for 2021 and 2022 data filesThe ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.2024 ReweightingIn February 2024, reweighted person-level data from July-September 2022 onwards were released. Up to July-September 2023, only the person weight was updated (PWT23); the income weight remains at 2022 (PIWT22). The 2023 income weight (PIWT23) was included from the October-December 2023 quarter. Users are encouraged to read the ONS methodological note of 5 February, Impact of reweighting on Labour Force Survey key indicators: 2024, which includes important information on the 2024 reweighting exercise.End User Licence and Secure Access QLFS dataTwo versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).The Secure Access version contains more detailed variables relating to:age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent childfamily unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of familynationality and country of originfiner detail geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district, and other categories;health: including main health problem, and current and past health problemseducation and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeshipsindustry: including industry, industry class and industry group for main, second and last job, and industry made redundant fromoccupation: including 5-digit industry subclass and 4-digit SOC for main, second and last job and job made redundant fromsystem variables: including week number when interview took place and number of households at addressother additional detailed variables may also be included.The Secure Access datasets (SNs 6727 and 7674) have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Latest edition informationFor the fifth edition (June 2022), 2022 weighting variable PWT22 was added to the study, and the 2020 weight removed. Main Topics:The QLFS questionnaire comprises a 'core' of questions which are included in every survey, together with some 'non-core' questions which vary from quarter to quarter.The questionnaire can be split into two main parts. The first part contains questions on the respondent's household, family structure, basic housing information and demographic details of household members. The second part contains questions covering economic activity, education and health, and also may include a few questions asked on behalf of other government departments (for example the Department for Work and Pensions and the Home Office). Until 1997, the questions on health covered mainly problems which affected the respondent's work. From that quarter onwards, the questions cover all health problems. Detailed questions on income have also been included in each quarter since 1993. The basic questionnaire is revised each year, and a new version published, along with a transitional version that details changes from the previous year's questionnaire. Four sampling frames are used. See documentation for details.

A yearly estimated number of insured workers, by insured status.

These data were compiled here to fit various versions of Bayesian population models and compare their performance, primarily the time required to make inferences using different softwares and versions of code. The humpback chub data were collected by US Geological Survey and US Fish and Wildlife service in the Colorado and Little Colorado Rivers from April 2009 to October 2017. Adult fish were captured using hoop nets and electro-fishing, measured for total length and given individual marks using passive integrated transponders that were scanned when fish were recaptured. The other three datasets were collected by US Forest Service. Owl data for the N-occupancy model was collected between 1990 and 2015. Owl data for the two-species example was collected between 1990 and 2011. Both owl data sets were collected in a ~1000 km2 area in the Roseburg District of the Bureau of Land Management in western Oregon, USA. Owl vocalizations (vocal lures) were used to detect barred owl or spotted owl pairs in 158 survey polygons spread throughout the study area. The avian community occupancy data were collected from 1991 to 1995 across 92 sites in the Chiricahua Mountains of southeastern Arizona, USA. 149 species were detected through repeated point counts in each year.

A simple susceptible–infectious–removed epidemic model for smallpox, with birth and death rates based on historical data, produces oscillatory dynamics with remarkably accurate periodicity. Stochastic population data cause oscillations to be sustained rather than damped, and data analysis regarding the oscillations provides insights into the same set of population data. Notably, oscillations arise naturally from the model, instead of from a periodic forcing term or other exogenous mechanism that guarantees oscillation: the model has no such mechanism. These emergent natural oscillations display appropriate periodicity for smallpox, even when the model is applied to different locations and populations. The model and datasets, in turn, offer new observations about disease dynamics and solution trajectories. These results call for renewed attention to relatively simple models, in combination with datasets from real outbreaks.

The dataset provided by Tarta.ai, created in February 2023, contains information on the number of jobs by company and city in California. The data provides a comprehensive view of the job market, highlighting the companies and cities that have the highest number of job opportunities.

The dataset includes a list of companies and the number of jobs they offer in different cities.

The dataset provides valuable insights for job seekers, employers, and policymakers. It can help job seekers to identify companies and cities with the highest job opportunities in their preferred industry and location. Employers can use the data to understand the competitive landscape and adjust their recruitment strategies accordingly. Policymakers can leverage the information to develop policies that promote job growth and economic development in different regions.

Overall, the Tarta.ai dataset is a valuable resource for anyone interested in the job market and provides a comprehensive view of the employment landscape across different industries and regions.

Dataset Columns:

1. Company name
2. City
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Madagascar Exports of drawing, marking-out/mathematical calculating instruments to France was US$26 during 2022, according to the United Nations COMTRADE database on international trade. Madagascar Exports of drawing, marking-out/mathematical calculating instruments to France - data, historical chart and statistics - was last updated on March of 2025.