FIES

## Overview

FIES is a dataset for object detection tasks - it contains Sports_ball annotations for 1,273 images.

## Getting Started

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

  ## License

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

The Medical Expenditure Panel Survey (MEPS) Household Component (HC) collects data from a sample of families and individuals in selected communities across the United States, drawn from a nationally representative subsample of households that participated in the prior year's National Health Interview Survey (conducted by the National Center for Health Statistics). During the household interviews, MEPS collects detailed information for each person in the household on the following: demographic characteristics, health conditions, health status, use of medical services, charges and source of payments, access to care, satisfaction with care, health insurance coverage, income, and employment. The panel design of the survey, which features several rounds of interviewing, makes it possible to determine how changes in respondents' health status, income, employment, eligibility for public and private insurance coverage, use of services, and payment for care are related. Public Use Files for Household data are available on the MEPS website.

analyze the consumer expenditure survey (ce) with r the consumer expenditure survey (ce) is the primo data source to understand how americans spend money. participating households keep a running diary about every little purchase over the year. those diaries are then summed up into precise expenditure categories. how else are you gonna know that the average american household spent $34 (±2) on bacon, $826 (±17) on cellular phones, and $13 (±2) on digital e-readers in 2011? an integral component of the market basket calculation in the consumer price index, this survey recently became available as public-use microdata and they're slowly releasing historical files back to 1996. hooray! for a t aste of what's possible with ce data, look at the quick tables listed on their main page - these tables contain approximately a bazillion different expenditure categories broken down by demographic groups. guess what? i just learned that americans living in households with $5,000 to $9,999 of annual income spent an average of $283 (±90) on pets, toys, hobbies, and playground equipment (pdf page 3). you can often get close to your statistic of interest from these web tables. but say you wanted to look at domestic pet expenditure among only households with children between 12 and 17 years old. another one of the thirteen web tables - the consumer unit composition table - shows a few different breakouts of households with kids, but none matching that exact population of interest. the bureau of labor statistics (bls) (the survey's designers) and the census bureau (the survey's administrators) have provided plenty of the major statistics and breakouts for you, but they're not psychic. if you want to comb through this data for specific expenditure categories broken out by a you-defined segment of the united states' population, then let a little r into your life. fun starts now. fair warning: only analyze t he consumer expenditure survey if you are nerd to the core. the microdata ship with two different survey types (interview and diary), each containing five or six quarterly table formats that need to be stacked, merged, and manipulated prior to a methodologically-correct analysis. the scripts in this repository contain examples to prepare 'em all, just be advised that magnificent data like this will never be no-assembly-required. the folks at bls have posted an excellent summary of what's av ailable - read it before anything else. after that, read the getting started guide. don't skim. a few of the descriptions below refer to sas programs provided by the bureau of labor statistics. you'll find these in the C:\My Directory\CES\2011\docs directory after you run the download program. this new github repository contains three scripts: 2010-2011 - download all microdata.R lo op through every year and download every file hosted on the bls's ce ftp site import each of the comma-separated value files into r with read.csv depending on user-settings, save each table as an r data file (.rda) or stat a-readable file (.dta) 2011 fmly intrvw - analysis examples.R load the r data files (.rda) necessary to create the 'fmly' table shown in the ce macros program documentation.doc file construct that 'fmly' table, using five quarters of interviews (q1 2011 thru q1 2012) initiate a replicate-weighted survey design object perform some lovely li'l analysis examples replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using unimputed variables replicate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t -tests using unimputed variables create an rsqlite database (to minimize ram usage) containing the five imputed variable files, after identifying which variables were imputed based on pdf page 3 of the user's guide to income imputation initiate a replicate-weighted, database-backed, multiply-imputed survey design object perform a few additional analyses that highlight the modified syntax required for multiply-imputed survey designs replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using imputed variables repl icate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t-tests using imputed variables replicate the %proc_reg() and %proc_logistic() macros found in "ce macros.sas" and provide some examples of regressions and logistic regressions using both unimputed and imputed variables replicate integrated mean and se.R match each step in the bls-provided sas program "integr ated mean and se.sas" but with r instead of sas create an rsqlite database when the expenditure table gets too large for older computers to handle in ram export a table "2011 integrated mean and se.csv" that exactly matches the contents of the sas-produced "2011 integrated mean and se.lst" text file click here to view these three scripts for...

Abstract

Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/.

The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2). 2. The proportion of the population experiencing severe food insecurity.

These data were collected by FAO through GeoPoll. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the documentations tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

Individuals of 15 years or older.

Kind of data

Sample survey data [ssd]

Sampling procedure

A sampling quota of at least 200 observations per each Administrative 1 areas is set Exclusions: NA Design effect: NA

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

Sampling error estimates

The margin of error is estimated as NA. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

Data appraisal

Since the population with access to mobile telephones is likely to differ from the rest of the population with respect to their access to food, post-hoc adjustments were made to control for the potential resulting bias. Post-stratification weights were built to adjust the sample distribution by gender and education of the respondent at admin-1 level, to match the same distribution in the total population. However, an additional step was needed to try to ascertain the food insecurity condition of those with access to phones compared to that of the total population.

Using FIES data collected by FAO through the GWP between 2014 and 2019, and a variable on access to mobile telephones that was also in the dataset, it was possible to compare the prevalence of food insecurity at moderate or severe level, and severe level only, of respondents with access to a mobile phone to that of the total population at national level.

NCHS has linked data from various surveys with death certificate records from the National Death Index (NDI). Linkage of the NCHS survey participant data with the NDI mortality data provides the opportunity to conduct a vast array of outcome studies designed to investigate the association of a wide variety of health factors with mortality. The Linked Mortality Files (LMF) have been updated with mortality follow-up data through December 31, 2019. Public-use Linked Mortality Files (LMF) are available for 1986-2018 NHIS, 1999-2018 NHANES, and NHANES III. The files include a limited set of mortality variables for adult participants only. The public-use versions of the NCHS Linked Mortality Files were subjected to data perturbation techniques to reduce the risk of participant re-identification. For select records, synthetic data were substituted for follow-up time or underlying cause of death. Information regarding vital status was not perturbed.

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

Abstract copyright UK Data Service and data collection copyright owner.The European State Finance Database (ESFD) is an international collaborative research project for the collection of data in European fiscal history. There are no strict geographical or chronological boundaries to the collection, although data for this collection comprise the period between c.1200 to c.1815. The purpose of the ESFD was to establish a significant database of European financial and fiscal records. The data are drawn from the main extant sources of a number of European countries, as the evidence and the state of scholarship permit. The aim was to collect the data made available by scholars, whether drawing upon their published or unpublished archival research, or from other published material. The ESFD project at the University of Leicester serves also to assist scholars working with the data by providing statistical manipulations of data and high quality graphical outputs for publication. The broad aim of the project was to act as a facilitator for a general methodological and statistical advance in the area of European fiscal history, with data capture and the interpretation of data in key publications as the measurable indicators of that advance. The data were originally deposited at the UK Data Archive in SAS transport format and as ASCII files; however, data files in this new edition have been saved as tab delimited files. Furthermore, this new edition features documentation in the form of a single file containing essential data file metadata, source details and notes of interest for particular files. Main Topics: The files in this dataset relate to the datafiles held in the Leicester database in the directory /rjb/malet/., excluding the derived datafiles, which are held in SN 3096. These data on seventeenth century French revenues and expenditure supplied by the Project Director, Professor Richard Bonney, draw upon J.R. Malet, Comptes rendus de l'administration des finances du royaume de France (London, 1789). For a discussion of this source in English, consult Bonney, R.J., 'Jean Roland Malet: historian of the finances of the French monarchy', French History, 5 (1991), 180-233. File Information: g068md01. Malet's figures for royal expenditure in France, 1600-10 g068md02. Malet's figures for royal expenditure in France, 1611-42 g068md03. Malet's figures for royal expenditure in France, 1643-56 g068md04. Malet's figures for royal expenditure in France, 1661-88 g068md05. Malet's figures for royal expenditure in France, 1689-95 g068md06. Malet's figures for receipts from the pays d'elections, 1600-10 g068md07. Malet's figures for receipts from the pays d'elections, 1611-42 g068md08. Malet's figures for receipts from the pays d'elections, 1643-56 g068md09. Malet's figures for receipts from the pays d'elections, 1661-88 g068md10. Malet's figures for receipts from the pays d'elections, 1661-88 (charges) g068md11. Malet's figures for receipts from the pays d'elections, 1661-88 (net to Treasury) g068md12. Malet's figures for receipts from the pays d'elections, 1689-95 g068md13. Malet's figures for receipts from the pays d'elections, 1689-95 (charges) g068md14. Malet's figures for receipts from the pays d'elections, 1689-95 (net to Treasury) g068md15. Malet's figures for receipts from the pays d'etats, 1600-10 g068md16. Malet's figures for receipts from the pays d'etats, 1611-42 g068md17. Malet's figures for receipts from the pays d'etats, 1643-56 g068md18. Malet's figures for receipts from the pays d'etats, 1661-88 g068md19. Malet's figures for receipts from the pays d'etats, 1661-88 (charges) g068md20. Malet's figures for receipts from the pays d'etats, 1661-88 (net to Treasury) g068md21. Malet's figures for receipts from the pays d'etats, 1689-95 g068md22. Malet's figures for receipts from the pays d'etats, 1689-95 (charges) g068md23. Malet's figures for receipts from the pays d'etats, 1689-95 (net to Treasury) g068md24. Malet's figures for dons gratuits from the pays d'etats, 1661-88 g068md25. Malet's figures for dons gratuits from the pays d'etats, 1661-88 (charges) g068md26. Malet's figures for dons gratuits from the pays d'etats, 1661-88 (net to Treasury) g068md27. Malet's figures for dons gratuits from the pays d'etats, 1689-95 g068md28. Malet's figures for dons gratuits from the pays d'etats, 1689-95 (charges) g068md29. Malet's figures for dons gratuits from the pays d'etats, 1689-95 (net to Treasury) g068md30. Malet's figures for receipts from the revenue farms, 1600-10 g068md31. Malet's figures for receipts from the revenue farms, 1611-42 g068md32. Malet's figures for receipts from the revenue farms, 1643-56 g068md33. Malet's figures for receipts from the revenue farms, 1661-88 g068md34. Malet's figures for receipts from the revenue farms, 1661-88 (charges) g068md35. Malet's figures for receipts from the revenue farms, 1661-88 (net to Treasury) g068md36. Malet's figures for receipts from the revenue farms, 1689-95 g068md37. Malet's figures for receipts from the revenue farms, 1689-95 (charges) g068md38. Malet's figures for receipts from the revenue farms, 1689-95 (net to Treasury) g068md39. Malet's figures for receipts from the revenue farms, 1661-88 g068md40. Malet's figures for receipts from the revenue farms, 1661-88 (charges) g068md41. Malet's figures for receipts from the revenue farms, 1661-88 (net to Treasury) g068md42. Malet's figures for receipts from the revenue farms, 1689-95 g068md43. Malet's figures for receipts from the revenue farms, 1689-95 (charges) g068md44. Malet's figures for receipts from the revenue farms, 1689-95 (net to Treasury) g068md45. Malet's figures for other receipts and deniers extraordinaires g068md46. Malet's figures for other receipts and deniers extraordinaires g068md47. Malet's figures for other receipts and deniers extraordinaires g068md48. Malet's figures for other receipts, 1661-88 g068md49. Malet's figures for other revenues, 1661-88 (charges) g068md50. Malet's figures for other revenues, 1661-88 (net to Treasury) g068md51. Malet's figures for other revenues, 1689-95 g068md52. Malet's figures for other revenues, 1689-95 (charges) g068md53. Malet's figures for other revenues, 1689-95 (net to Treasury) g068md54. Malet's recapitulation table for revenues, 1600-10 g068md55. Malet's recapitulation table for revenues, 1611-42 g068md56. Malet's recapitulation table for revenues, 1643-56 g068md57. Malet's recapitulation table for revenues, 1661-88 g068md58. Malet's recapitulation table for revenues, 1661-88 (charges) g068md59. Malet's recapitulation table for revenues, 1661-88 (net to Treasury) g068md60. Malet's recapitulation table for revenues, 1689-95 g068md61. Malet's recapitulation table for revenues, 1689-95 (charges) g068md62. Malet's recapitulation table for revenues, 1689-95 (net to Treasury) g068md63. Ordinary revenues and expenses of the French monarchy, 1600-10 g068md64. Ordinary revenues and expenses of the French monarchy, 1611-42 g068md65. Ordinary revenues and expenses of the French monarchy, 1643-56 g068md66. Ordinary revenues and expenses of the French monarchy, 1661-88 g068md67. Ordinary revenues and expenses of the French monarchy, 1689-95 g068md68. Malet's figures for a project for expenditure in France: tresorier de la guerre, 1710 g068md69. Malet's figures for a project for expenditure in France: royal households, 1710 g068md70. Malet's figures for a project for expenditure in France: royal treasury, 1710 g068md71. Malet's figures for French royal expenditure, 1710 g068md72. Malet's figures for receipts from the pays d'elections, 1710 g068md73. Malet's figures for capitation levied on the pays d'elections, 1710 g068md74. Malet's figures for other capitations 1710 g068md75. Malet's figures for dons gratuits from the pays d'etats, 1710 g068md76. Malet's figures for receipts from the pays d'etats, 1710 g068md77. Malet's figures for capitations levied on the pays d'etats, 1710 g068md78. Malet's figures for receipts from the revenue farms, 1710 g068md79. Malet's figures for other revenues, 1710 g068md80. Malet's recapitulation table for revenues, 1710 Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research.

EyeFi Dataset

This dataset is collected as a part of the EyeFi project at Bosch Research and Technology Center, Pittsburgh, PA, USA. The dataset contains WiFi CSI values of human motion trajectories along with ground truth location information captured through a camera. This dataset is used in the following paper "EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching" that is published in the IEEE International Conference on Distributed Computing in Sensor Systems 2020 (DCOSS '20). We also published a dataset paper titled as "Dataset: Person Tracking and Identification using Cameras and Wi-Fi Channel State Information (CSI) from Smartphones" in Data: Acquisition to Analysis 2020 (DATA '20) workshop describing details of data collection. Please check it out for more information on the dataset.

Clarification/Bug report: Please note that the order of antennas and subcarriers in .h5 files is not written clearly in the README.md file. The order of antennas and subcarriers are as follows for the 90 csi_real and csi_imag values : [subcarrier1-antenna1, subcarrier1-antenna2, subcarrier1-antenna3, subcarrier2-antenna1, subcarrier2-antenna2, subcarrier2-antenna3,… subcarrier30-antenna1, subcarrier30-antenna2, subcarrier30-antenna3]. Please see the description below. The newer version of the dataset contains this information in README.md. We are sorry for the inconvenience.

Data Collection Setup

In our experiments, we used Intel 5300 WiFi Network Interface Card (NIC) installed in an Intel NUC and Linux CSI tools [1] to extract the WiFi CSI packets. The (x,y) coordinates of the subjects are collected from Bosch Flexidome IP Panoramic 7000 panoramic camera mounted on the ceiling and Angle of Arrivals (AoAs) are derived from the (x,y) coordinates. Both the WiFi card and camera are located at the same origin coordinates but at different height, the camera is location around 2.85m from the ground and WiFi antennas are around 1.12m above the ground.

The data collection environment consists of two areas, first one is a rectangular space measured 11.8m x 8.74m, and the second space is an irregularly shaped kitchen area with maximum distances of 19.74m and 14.24m between two walls. The kitchen also has numerous obstacles and different materials that pose different RF reflection characteristics including strong reflectors such as metal refrigerators and dishwashers.

To collect the WiFi data, we used a Google Pixel 2 XL smartphone as an access point and connect the Intel 5300 NIC to it for WiFi communication. The transmission rate is about 20-25 packets per second. The same WiFi card and phone are used in both lab and kitchen area.

List of Files Here is a list of files included in the dataset:

|- 1_person |- 1_person_1.h5 |- 1_person_2.h5 |- 2_people |- 2_people_1.h5 |- 2_people_2.h5 |- 2_people_3.h5 |- 3_people |- 3_people_1.h5 |- 3_people_2.h5 |- 3_people_3.h5 |- 5_people |- 5_people_1.h5 |- 5_people_2.h5 |- 5_people_3.h5 |- 5_people_4.h5 |- 10_people |- 10_people_1.h5 |- 10_people_2.h5 |- 10_people_3.h5 |- Kitchen |- 1_person |- kitchen_1_person_1.h5 |- kitchen_1_person_2.h5 |- kitchen_1_person_3.h5 |- 3_people |- kitchen_3_people_1.h5 |- training |- shuffuled_train.h5 |- shuffuled_valid.h5 |- shuffuled_test.h5 View-Dataset-Example.ipynb README.md

In this dataset, folder 1_person/ , 2_people/ , 3_people/ , 5_people/, and 10_people/ contains data collected from the lab area whereas Kitchen/ folder contains data collected from the kitchen area. To see how the each file is structured, please see below in section Access the data.

The training folder contains the training dataset we used to train the neural network discussed in our paper. They are generated by shuffling all the data from 1_person/ folder collected in the lab area (1_person_1.h5 and 1_person_2.h5).

Why multiple files in one folder?

Each folder contains multiple files. For example, 1_person folder has two files: 1_person_1.h5 and 1_person_2.h5. Files in the same folder always have the same number of human subjects present simultaneously in the scene. However, the person who is holding the phone can be different. Also, the data could be collected through different days and/or the data collection system needs to be rebooted due to stability issue. As result, we provided different files (like 1_person_1.h5, 1_person_2.h5) to distinguish different person who is holding the phone and possible system reboot that introduces different phase offsets (see below) in the system.

Special note:

For 1_person_1.h5, this file is generated by the same person who is holding the phone, and 1_person_2.h5 contains different people holding the phone but only one person is present in the area at a time. Boths files are collected in different days as well.

Access the data To access the data, hdf5 library is needed to open the dataset. There are free HDF5 viewer available on the official website: https://www.hdfgroup.org/downloads/hdfview/. We also provide an example Python code View-Dataset-Example.ipynb to demonstrate how to access the data.

Each file is structured as (except the files under "training/" folder):

|- csi_imag |- csi_real |- nPaths_1 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_2 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_3 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_4 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- num_obj |- obj_0 |- cam_aoa |- coordinates |- obj_1 |- cam_aoa |- coordinates ... |- timestamp

The csi_real and csi_imag are the real and imagenary part of the CSI measurements. The order of antennas and subcarriers are as follows for the 90 csi_real and csi_imag values : [subcarrier1-antenna1, subcarrier1-antenna2, subcarrier1-antenna3, subcarrier2-antenna1, subcarrier2-antenna2, subcarrier2-antenna3,… subcarrier30-antenna1, subcarrier30-antenna2, subcarrier30-antenna3]. nPaths_x group are SpotFi [2] calculated WiFi Angle of Arrival (AoA) with x number of multiple paths specified during calculation. Under the nPath_x group are offset_xx subgroup where xx stands for the offset combination used to correct the phase offset during the SpotFi calculation. We measured the offsets as:

The measurement is based on the work [3], where the authors state there are two possible offsets between two antennas which we measured by booting the device multiple times. The combination of the offset are used for the offset_xx naming. For example, offset_12 is offset 1 between antenna 1 & 2 and offset 2 between antenna 1 & 3 are used in the SpotFi calculation.

The num_obj field is used to store the number of human subjects present in the scene. The obj_0 is always the subject who is holding the phone. In each file, there are num_obj of obj_x. For each obj_x1, we have the coordinates reported from the camera and cam_aoa, which is estimated AoA from the camera reported coordinates. The (x,y) coordinates and AoA listed here are chronologically ordered (except the files in the training folder) . It reflects the way the person carried the phone moved in the space (for obj_0) and everyone else walked (for other obj_y, where y > 0).

The timestamp is provided here for time reference for each WiFi packets.

To access the data (Python):

import h5py

data = h5py.File('3_people_3.h5','r')

csi_real = data['csi_real'][()] csi_imag = data['csi_imag'][()]

cam_aoa = data['obj_0/cam_aoa'][()] cam_loc = data['obj_0/coordinates'][()]

For file inside training/ folder:

Files inside training folder has a different data structure:

|- nPath-1 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-2 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-3 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-4 |- aoa |- csi_imag |- csi_real |- spotfi

The group nPath-x is the number of multiple path specified during the SpotFi calculation. aoa is the camera generated angle of arrival (AoA) (can be considered as ground truth), csi_image and csi_real is the imaginary and real component of the CSI value. spotfi is the SpotFi calculated AoA values. The SpotFi values are chosen based on the lowest median and mean error from across 1_person_1.h5 and 1_person_2.h5. All the rows under the same nPath-x group are aligned (i.e., first row of aoa corresponds to the first row of csi_imag, csi_real, and spotfi. There is no timestamp recorded and the sequence of the data is not chronological as they are randomly shuffled from the 1_person_1.h5 and 1_person_2.h5 files.

Citation If you use the dataset, please cite our paper:

@inproceedings{eyefi2020, title={EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching}, author={Fang, Shiwei and Islam, Tamzeed and Munir, Sirajum and Nirjon, Shahriar}, booktitle={2020 IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS)},

NFDI4Chem Online Survey 2019 Dataset

In preparation of the NFDI4Chem proposal for the National Research Data Infrastructure in 2019 the NFDI4Chem team conducted a online survey with the aim to evaluate the current status of research data management in chemistry with a special focus on Germany.

Dataset NFDI4Chem Survey Datasets Period Jul 19, 2019 - October 15, 2019

Data captured with SoSci Survey https://www.soscisurvey.de/

What does the dataset include?

nfdi4chem.zip includes

• Overview_questionaire_variables.pdf
• SoSci_export_variables.csv
• SoSci_export_values.csv
• SoSci_export_data_anonym.csv

Overview_questionaire_variables.pdf

This file provides an overview of the questionaire structure, the codes for questions and answers used in the data files.

SoSci_export_values.csv

This file contains per row the codes of the answers label and the predefined values of the answer data field and their actual meaning.

SoSci_export_variables.csv

This file contains per row the question, code of the answers label, descriptive answer label and type of data field

SoSci_export_data_anonym.csv

This file contains per row the participants answers using the codes and predefined values of SoSci_export_values.xlsx

This Zenodo repository contains the main dataset for the GENEA Challenge 2023, which is based on the Talking With Hands 16.2M dataset.

Notation:

Please take note of the following nomenclature when reading this document:

main agent refers to the speaker in the dyadic interaction for which the systems generated motions.

interlocutor refers to the speaker in front of the main agent.

Contents:

The “genea2023_trn" and "genea2023_val" zip files contain audio files (in WAV format), time-aligned transcriptions (in TSV format), and motion files (in BVH format) for the training and validation datasets, respectively.

The "genea2023_test" zip file contains audio files (in WAV format) and transcriptions (in TSV format) for the test set, but no motion. The corresponding test motion is available at:

https://zenodo.org/record/8146027

Each zip file also contains a "metadata.csv" file that contains information for all files regarding the speaker ID and whether or not the motion files contain finger motion.

Note that the speech audio in the data sometimes has been replaced by silence for the purpose of anonymisation.

In the test set, files with indices from 0 to 40 correspond to "matched" interactions (the core test set), where main agent and interlocutor data come from the same conversation, whilst file indices from 41 to 69 correspond to "mismatched" interactions (the extended test set), where main agent and interlocutor data come from different conversations.

Folder structure:

main-agent/ (main agent): Encapsulates BVH, TSV, WAV data subfolders for the main agent.

interloctr/ (interlocutor): Encapsulates BVH, TSV, WAV data subfolders for the interlocutor.

bvh/ (motion): Time-aligned 3D full-body motion-capture data in BVH format from a speaking and gesticulating actor. Each file is a single person, but each data sample contains files for both the main agent and the interlocutor.

wav/ (audio): Recorded audio data in WAV format from a speaking and gesticulating actor with a close-talking microphone. Parts of the audio recordings have been muted to omit personally identifiable information.

tsv/ (text): Word-level time-aligned text transcriptions of the above audio recordings in TSV format (tab-separated values). For privacy reasons, the transcriptions do not include references to personally identifiable information, similar to the audio files.

Data processing scripts:

We provide a number of optional scripts for encoding and processing the challenge data:

Audio: Scripts for extracting basic audio features, such as spectrograms, prosodic features, and mel-frequency cepstral coefficients (MFCCs) can be found at this link.

Text: A script to encode text transcriptions to word vectors using FastText is available here: tsv2wordvectors.py

Motion: If you wish to encode the joint angles from the BVH files to and from an exponential map representation, you can use scripts by Simon Alexanderson based on the PyMo library, which are available here:

bvh2features.py

features2bvh.py

Attribution:

If you use this material, please cite our latest paper on the GENEA Challenge 2023. At the time of writing (2023-07-25) this is our ACM ICMI 2023 paper:

Taras Kucherenko, Rajmund Nagy, Youngwoo Yoon, Jieyeon Woo, Teodor Nikolov, Mihail Tsakov, and Gustav Eje Henter. 2023. The GENEA Challenge 2023: A large-scale evaluation of gesture generation models in monadic and dyadic settings. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI ’23). ACM.

Also, please cite the paper about the original dataset from Meta Research:

Gilwoo Lee, Zhiwei Deng, Shugao Ma, Takaaki Shiratori, Siddhartha S. Srinivasa, and Yaser Sheikh. 2019. Talking With Hands 16.2M: A large-scale dataset of synchronized body-finger motion and audio for conversational motion analysis and synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV ’19). IEEE, 763–772.

The motion and audio files are based on the Talking With Hands 16.2M dataset at https://github.com/facebookresearch/TalkingWithHands32M/. The material is available under a CC BY NC 4.0 Attribution-NonCommercial 4.0 International license, with the text provided in LICENSE.txt.

To find more GENEA Challenge 2023 material on the web, please see:

https://genea-workshop.github.io/2023/challenge/

If you have any questions or comments, please contact:

The GENEA Challenge organisers

We introduce a large-scale dataset of the complete texts of free/open source software (FOSS) license variants. To assemble it we have collected from the Software Heritage archive—the largest publicly available archive of FOSS source code with accompanying development history—all versions of files whose names are commonly used to convey licensing terms to software users and developers. The dataset consists of 6.5 million unique license files that can be used to conduct empirical studies on open source licensing, training of automated license classifiers, natural language processing (NLP) analyses of legal texts, as well as historical and phylogenetic studies on FOSS licensing. Additional metadata about shipped license files are also provided, making the dataset ready to use in various contexts; they include: file length measures, detected MIME type, detected SPDX license (using ScanCode), example origin (e.g., GitHub repository), oldest public commit in which the license appeared. The dataset is released as open data as an archive file containing all deduplicated license blobs, plus several portable CSV files for metadata, referencing blobs via cryptographic checksums.

For more details see the included README file and companion paper:

Stefano Zacchiroli. A Large-scale Dataset of (Open Source) License Text Variants. In proceedings of the 2022 Mining Software Repositories Conference (MSR 2022). 23-24 May 2022 Pittsburgh, Pennsylvania, United States. ACM 2022.

If you use this dataset for research purposes, please acknowledge its use by citing the above paper.

Errata: Due to a coding error, monthly files with "dma8epax" statistics were wrongly aggregated. This concerns all gridded files of this metric as well as the monthly aggregated csv files. All erroneous files were replaced with corrected versions on Jan, 16th, 2018. Each updated file contains a version label "1.1" and a brief description of the error. If you have made use of previous TOAR data files with the "dma8epax" metric, please exchange your data files.

Source: https://www.wider.unu.edu/database/wiid User Guide: https://www.wider.unu.edu/sites/default/files/WIID/PDF/WIID-User_Guide_06MAY2020.pdf

The World Income Inequality Database (WIID) contains information on income inequality in various countries and is maintained by the United Nations University-World Institute for Development Economics Research (UNU-WIDER). The database was originally compiled during 1997-99 for the research project Rising Income Inequality and Poverty Reduction, directed by Giovanni Andrea Corina. A revised and updated version of the database was published in June 2005 as part of the project Global Trends in Inequality and Poverty, directed by Tony Shorrocks and Guang Hua Wan. The database was revised in 2007 and a new version was launched in May 2008.

The database contains data on inequality in the distribution of income in various countries. The central variable in the dataset is the Gini index, a measure of income distribution in a society. In addition, the dataset contains information on income shares by quintile or decile. The database contains data for 159 countries, including some historical entities. The temporal coverage varies substantially across countries. For some countries there is only one data entry; in other cases there are over 100 data points. The earliest entry is from 1867 (United Kingdom), the latest from 2003. The majority of the data (65%) cover the years from 1980 onwards. The 2008 update (version WIID2c) includes some major updates and quality improvements, in fact leading to a reduced number of variables in the new version. The new version has 334 new observations and several revisions/ corrections made in 2007 and 2008.

Errata: On Dec 2nd, 2018, several yearly statistics files were replaced with new versions to correct an inconsistency related to the computation of the "dma8epax" statistics. As written in Schultz et al. (2017) [https://doi.org/10.1525/elementa.244], Supplement 1, Table 6: "When the aggregation period is “seasonal”, “summer”, or “annual”, the 4th highest daily 8-hour maximum of the aggregation period will be computed.". The data values for these aggregation periods are correct, however, the header information in the original files stated that the respective data column would contain "average daily maximum 8-hour ozone mixing ratio (nmol mol-1)". Therefore, the header of the seasonal, summer, and annual files has been corrected. Furthermore, the "dma8epax" column in the monthly files erroneously contained 4th highest daily maximum 8-hour average values, while it should have listed monthly average values instead. The data of this metric in the monthly files have therefore been replaced. The new column header reads "avgdma8epax". The updated files contain a version label "1.1" and a brief description of the error. If you have made use of previous TOAR data files with the "dma8epax" metric, please exchange your data files.

The NIHR is one of the main funders of public health research in the UK. Public health research falls within the remit of a range of NIHR Research Programmes, NIHR Centres of Excellence and Facilities, plus the NIHR Academy. NIHR awards from all NIHR Research Programmes and the NIHR Academy that were funded between January 2006 and the present extraction date are eligible for inclusion in this dataset. An agreed inclusion/exclusion criteria is used to categorise awards as public health awards (see below). Following inclusion in the dataset, public health awards are second level coded to one of the four Public Health Outcomes Framework domains. These domains are: (1) wider determinants (2) health improvement (3) health protection (4) healthcare and premature mortality.More information on the Public Health Outcomes Framework domains can be found here.This dataset is updated quarterly to include new NIHR awards categorised as public health awards. Please note that for those Public Health Research Programme projects showing an Award Budget of £0.00, the project is undertaken by an on-call team for example, PHIRST, Public Health Review Team, or Knowledge Mobilisation Team, as part of an ongoing programme of work.Inclusion criteriaThe NIHR Public Health Overview project team worked with colleagues across NIHR public health research to define the inclusion criteria for NIHR public health research awards. NIHR awards are categorised as public health awards if they are determined to be ‘investigations of interventions in, or studies of, populations that are anticipated to have an effect on health or on health inequity at a population level.’ This definition of public health is intentionally broad to capture the wide range of NIHR public health awards across prevention, health improvement, health protection, and healthcare services (both within and outside of NHS settings). This dataset does not reflect the NIHR’s total investment in public health research. The intention is to showcase a subset of the wider NIHR public health portfolio. This dataset includes NIHR awards categorised as public health awards from NIHR Research Programmes and the NIHR Academy. This dataset does not currently include public health awards or projects funded by any of the three NIHR Research Schools or any of the NIHR Centres of Excellence and Facilities. Therefore, awards from the NIHR Schools for Public Health, Primary Care and Social Care, NIHR Public Health Policy Research Unit and the NIHR Health Protection Research Units do not feature in this curated portfolio.DisclaimersUsers of this dataset should acknowledge the broad definition of public health that has been used to develop the inclusion criteria for this dataset. This caveat applies to all data within the dataset irrespective of the funding NIHR Research Programme or NIHR Academy award.Please note that this dataset is currently subject to a limited data quality review. We are working to improve our data collection methodologies. Please also note that some awards may also appear in other NIHR curated datasets. Further informationFurther information on the individual awards shown in the dataset can be found on the NIHR’s Funding & Awards website here. Further information on individual NIHR Research Programme’s decision making processes for funding health and social care research can be found here.Further information on NIHR’s investment in public health research can be found as follows: NIHR School for Public Health here. NIHR Public Health Policy Research Unit here. NIHR Health Protection Research Units here. NIHR Public Health Research Programme Health Determinants Research Collaborations (HDRC) here. NIHR Public Health Research Programme Public Health Intervention Responsive Studies Teams (PHIRST) here.

This dataset consists of 5269 reaction profiles computed in a high-throughput manner at B3LYP-D3(BJ)/def2-TZVP//B3LYP-D3(BJ)/def2-SVP level of theory with the help of autodE and Gaussian 16. Reaction IDs and SMILES, activation energies (G_act; in kcal/mol) and reaction energies (G_r; in kcal/mol) for each computed reaction profile are provided in CSV format (full_dataset.csv). XYZ-files for each reactant (both the original and stereo-constrained versions), TS and product species as well as a CSV file containing computed electronic energies and thermal corrections are available in a compressed archive file, full_dataset_profiles.tar.gz.

The files have been organized per reaction profile, identified through the reaction ID. Within each directory, reactant XYZ-files are of the form r#####.xyz, product XYZ-files are of the form p#####.xyz, and transition state XYZ-files are of the form TS_#####.xyz. If the reactant dipole conformer had to be corrected to enforce stereochemical compatibility, the latter XYZ-files are included under to form of r#####_alt.xyz. The energies for all of these species are summarized per directory in energies.csv.

Additionally, all the benchmarking data are made available in the benchmarking_data.tar.gz directory

The Savings Bond Value Files dataset is used by developers of bond pricing programs to update their systems with new redemption values for accrual savings bonds (Series E, EE, I & Savings Notes). The core data is the same as the Redemption Tables but there are differences in format, amount of data, and date range. The Savings Bonds Value Files dataset is meant for programmers and developers to read in redemption values without having to first convert PDFs.

DISCOVERAQ_Maryland_Merge_Data contains pre-generated merged data files created from measurements obtained onboard the P-3B aircraft during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.

General descriptionThis dataset contains some markers of Open Science in the publications of the Chemical Biology Consortium Sweden (CBCS) between 2010 and July 2023. The sample of CBCS publications during this period consists of 188 articles. Every publication was visited manually at its DOI URL to answer the following questions.1. Is the research article an Open Access publication?2. Does the research article have a Creative Common license or a similar license?3. Does the research article contain a data availability statement?4. Did the authors submit data of their study to a repository such as EMBL, Genbank, Protein Data Bank PDB, Cambridge Crystallographic Data Centre CCDC, Dryad or a similar repository?5. Does the research article contain supplementary data?6. Do the supplementary data have a persistent identifier that makes them citable as a defined research output?VariablesThe data were compiled in a Microsoft Excel 365 document that includes the following variables.1. DOI URL of research article2. Year of publication3. Research article published with Open Access4. License for research article5. Data availability statement in article6. Supplementary data added to article7. Persistent identifier for supplementary data8. Authors submitted data to NCBI or EMBL or PDB or Dryad or CCDCVisualizationParts of the data were visualized in two figures as bar diagrams using Microsoft Excel 365. The first figure displays the number of publications during a year, the number of publications that is published with open access and the number of publications that contain a data availability statement (Figure 1). The second figure shows the number of publication sper year and how many publications contain supplementary data. This figure also shows how many of the supplementary datasets have a persistent identifier (Figure 2).File formats and softwareThe file formats used in this dataset are:.csv (Text file).docx (Microsoft Word 365 file).jpg (JPEG image file).pdf/A (Portable Document Format for archiving).png (Portable Network Graphics image file).pptx (Microsoft Power Point 365 file).txt (Text file).xlsx (Microsoft Excel 365 file)All files can be opened with Microsoft Office 365 and work likely also with the older versions Office 2019 and 2016. MD5 checksumsHere is a list of all files of this dataset and of their MD5 checksums.1. Readme.txt (MD5: 795f171be340c13d78ba8608dafb3e76)2. Manifest.txt (MD5: 46787888019a87bb9d897effdf719b71)3. Materials_and_methods.docx (MD5: 0eedaebf5c88982896bd1e0fe57849c2),4. Materials_and_methods.pdf (MD5: d314bf2bdff866f827741d7a746f063b),5. Materials_and_methods.txt (MD5: 26e7319de89285fc5c1a503d0b01d08a),6. CBCS_publications_until_date_2023_07_05.xlsx (MD5: 532fec0bd177844ac0410b98de13ca7c),7. CBCS_publications_until_date_2023_07_05.csv (MD5: 2580410623f79959c488fdfefe8b4c7b),8. Data_from_CBCS_publications_until_date_2023_07_05_obtained_by_manual_collection.xlsx (MD5: 9c67dd84a6b56a45e1f50a28419930e5),9. Data_from_CBCS_publications_until_date_2023_07_05_obtained_by_manual_collection.csv (MD5: fb3ac69476bfc57a8adc734b4d48ea2b),10. Aggregated_data_from_CBCS_publications_until_2023_07_05.xlsx (MD5: 6b6cbf3b9617fa8960ff15834869f793),11. Aggregated_data_from_CBCS_publications_until_2023_07_05.csv (MD5: b2b8dd36ba86629ed455ae5ad2489d6e),12. Figure_1_CBCS_publications_until_2023_07_05_Open_Access_and_data_availablitiy_statement.xlsx (MD5: 9c0422cf1bbd63ac0709324cb128410e),13. Figure_1.pptx (MD5: 55a1d12b2a9a81dca4bb7f333002f7fe),14. Image_of_figure_1.jpg (MD5: 5179f69297fbbf2eaaf7b641784617d7),15. Image_of_figure_1.png (MD5: 8ec94efc07417d69115200529b359698),16. Figure_2_CBCS_publications_until_2023_07_05_supplementary_data_and_PID_for_supplementary_data.xlsx (MD5: f5f0d6e4218e390169c7409870227a0a),17. Figure_2.pptx (MD5: 0fd4c622dc0474549df88cf37d0e9d72),18. Image_of_figure_2.jpg (MD5: c6c68b63b7320597b239316a1c15e00d),19. Image_of_figure_2.png (MD5: 24413cc7d292f468bec0ac60cbaa7809)

These data were collected under a cooperative agreement with the Massachusetts Office of Coastal Zone Management (CZM) and the U.S. Geological Survey (USGS), Coastal and Marine Geology Program, Woods Hole Science Center (WHSC). Initiated in 2003, the primary objective of this program is to develop regional geologic framework information for the management of coastal and marine resources. Accurate data and maps of sea-floor geology are important first steps toward protecting fish habitat, delineating marine resources, and assessing environmental changes due to natural or human impacts. The project is focused on the inshore waters (5-30 m deep) of Massachusetts between the New Hampshire border and Cape Cod Bay. Data collected for the mapping cooperative have been released in a series of USGS Open-File Reports (https://woodshole.er.usgs.gov/project-pages/coastal_mass/). This spatial dataset is from the study area located between Duxbury and Hull Massachusetts, and consists of high-resolution geophysics (bathymetry, backscatter intensity, and seismic reflection) and ground validation (sediment samples, video tracklines and bottom photographs). The data were collected during four separate surveys conducted between 2003 and 2007 (NOAA survey H10993 in 2003, USGS-WHSC survey 06012 in 2006, and USGS-WHSC surveys 07001 and 07003 in 2007) and cover more than 200 square kilometers of the inner continental shelf.