The PCI and PCIe Image Capture Card market plays a crucial role in various industries, ranging from gaming and streaming to medical imaging and security surveillance. These cards are integral for converting raw video and imaging data from high-definition sources into digital formats that can be processed, recorded,

The GIS shapefile "Census summary of southern sea otter 2017" provides a standardized tool for examining spatial patterns in abundance and demographic trends of the southern sea otter (Enhydra lutris nereis), based on data collected during the spring 2017 range-wide census. The USGS range-wide sea otter census has been undertaken twice a year since 1982, once in May and once in October, using consistent methodology involving both ground-based and aerial-based counts. The spring census is considered more accurate than the fall count, and provides the primary basis for gauging population trends by State and Federal management agencies. This Shape file includes a series of summary statistics derived from the raw census data, including sea otter density (otters per square km of habitat), linear density (otters per km of coastline), relative pup abundance (ratio of pups to independent animals) and 5-year population trend (calculated as exponential rate of change). All statistics are calculated and plotted for small sections of habitat in order to illustrate local variation in these statistics across the entire mainland distribution of sea otters in California (as of 2017). Sea otter habitat is considered to extend offshore from the mean low tide line and out to the 60m isobath: this depth range includes over 99% of sea otter feeding dives, based on dive-depth data from radio tagged sea otters (Tinker et al 2006, 2007). Sea otter distribution in California (the mainland range) is considered to comprise this band of potential habitat stretching along the coast of California, and bounded to the north and south by range limits defined as "the points farthest from the range center at which 5 or more otters are counted within a 10km contiguous stretch of coastline (as measured along the 10m bathymetric contour) during the two most recent spring censuses, or at which these same criteria were met in the previous year". The polygon corresponding to the range definition was then sub-divided into onshore/offshore strips roughly 500 meters in width. The boundaries between these strips correspond to ATOS (As-The-Otter-Swims) points, which are arbitrary locations established approximately every 500 meters along a smoothed 5 fathom bathymetric contour (line) offshore of the State of California. References: Tinker, M. T., Doak, D. F., Estes, J. A., Hatfield, B. B., Staedler, M. M. and Bodkin, J. L. (2006), INCORPORATING DIVERSE DATA AND REALISTIC COMPLEXITY INTO DEMOGRAPHIC ESTIMATION PROCEDURES FOR SEA OTTERS. Ecological Applications, 16: 2293–2312, https://doi.org/10.1890/1051-0761(2006)016[2293:IDDARC]2.0.CO;2 Tinker, M. T. , D. P. Costa , J. A. Estes , and N. Wieringa . 2007. Individual dietary specialization and dive behaviour in the California sea otter: using archival time–depth data to detect alternative foraging strategies. Deep Sea Research II 54: 330–342, https://doi.org/10.1016/j.dsr2.2006.11.012

the dataset contains statistics for significant wave height (hs), mean wave period (tm), peak wave period (tp) and mean wave direction (θm) for hindcast (1979-2005) and a multi-model ensemble of 17 euro-cordex gcm-rcms projections for the following periods: baseline (1979-2005), mid-century (2034-2060) for rcp 8.5, and end-of-century (2074-2100) for rcp 8.5.the following statistics are included providing seasonal and monthly means:significant wave height:for hindcast, raw gcm-rcms and bias-adjusted gcm-rcms average significant wave height 10, 50, 90, 95 and 99th maximum significant wave height average hs when hs>90 and 95th percentiles of the hindcast number of sea states when hs>90 and 95th percentiles of the hindcast percentage of sea states when hs>90 and 95th percentiles of the hindcast average hs when hs>1.25, 2.5 and 4 m number of sea states when hs>1.25, 2.5 and 4 m percentage of sea states when hs>1.25, 2.5 and 4 m number of days with at least two consecutive days when daily-max hs>90th and 95th percentiles of the hindcast percentage of days with at least two consecutive days when daily-max hs>90th and 95th percentiles of the hindcastmean wave period: for hindcast and raw gcm-rcms average mean wave period 10, 50, 90, 95 and 99th percentiles maximum mean wave periodpeak wave period:for hindcast and raw gcm-rcms average peak wave period 10, 50, 90, 95 and 99th percentiles maximum peak wave periodmean wave direction:for hindcast and raw gcm-rcms mean circular mean circular standard deviationtime span: hindcast/baseline: 1979-01-01 – 2005-12-31 mid-century: 2034-01-01 – 2060-12-31 end-of-century: 2074-01-01 – 2100-12-31

The GIS layer "Census_sum_15" provides a standardized tool for examining spatial patterns in abundance and demographic trends of the southern sea otter (Enhydra lutris nereis), based on data collected during the spring 2015 range-wide census. The USGS range-wide sea otter census has been undertaken twice a year since 1982, once in May and once in October, using consistent methodology involving both ground-based and aerial-based counts. The spring census is considered more accurate than the fall count, and provides the primary basis for gauging population trends by State and Federal management agencies. This Shape file includes a series of summary statistics derived from the raw census data, including sea otter density (otters per square km of habitat), linear density (otters per km of coastline), relative pup abundance (ratio of pups to independent animals) and 5-year population trend (calculated as exponential rate of change). All statistics are calculated and plotted for small sections of habitat in order to illustrate local variation in these statistics across the entire mainland distribution of sea otters in California (as of 2015). Sea otter habitat is considered to extend offshore from the mean low tide line and out to the 60m isobath: this depth range includes over 99% of sea otter feeding dives, based on dive-depth data from radio tagged sea otters (Tinker et al 2006, 2007). Sea otter distribution in California (the mainland range) is considered to comprise this band of potential habitat stretching along the coast of California, and bounded to the north and south by range limits defined as "the points farthest from the range center at which 5 or more otters are counted within a 10km contiguous stretch of coastline (as measured along the 10m bathymetric contour) during the two most recent spring censuses, or at which these same criteria were met in the previous year". The polygon corresponding to the range definition was then sub-divided into onshore/offshore strips roughly 500 meters in width. The boundaries between these strips correspond to ATOS (As-The-Otter-Swims) points, which are arbitrary locations established approximately every 500 meters along a smoothed 5 fathom bathymetric contour (line) offshore of the State of California.

Data These data relate to jobseekers registered on average during the quarter at Pôle emploi in categories A, B, C by sex, by age group and by municipality (based on the Official Geographic Code, as of 1 January 2022), for the 4th quarters over a rolling 10-year period. They are raw and rounded to a multiple of 5. There may therefore be slight differences between the sum of the disaggregated data and the aggregated series. Annual developments should be taken with caution: on small municipalities, the effect of rounding can be significant and the annual evolution is then very impacted. For example, a municipality that sees an increase in the number of jobseekers from 17 to 18 (an increase of 6 %) will have staff numbers rounded up to 15 and 20, i.e. an increase of 33 %. The ages used for the different series are the ages at the end of the month (age that the job seeker will have at the end of the month in question). For each of the communes, the region and the department to which they belong are specified. ### Definition Full documentation on data on registered jobseekers and vacancies collected by France Travail can be found on the Dares website (see document Methodological documentation - Jobseekers). Information on the Official Geographic Code is available on the INSEE website. ### Field * the geographical grouping ‘**** Metropolitan France’ includes all the French territories on the European continent (96 departments); * The geographical grouping ‘France’ includes metropolitan France and the overseas departments/regions (DROM), with the exception of Mayotte. ### Source The data are taken from the files of the Monthly Labour Market Statistics (STMT) of Dares and France Travail. ### Warnings In addition to labour market developments, data on jobseekers can be affected by a number of factors: changes to the rules on compensation or support for jobseekers, procedural changes, incidents. A document presents the main procedural changes and incidents affecting the statistics of jobseekers since 2011. The municipalities of Sannerville (14666) and Troarn (14712) were merged into the new municipality of Saline (14712) from 2017 to 2019, and were then re-established on 1 January 2020. The information for these two municipalities over this period should therefore be considered with caution.

PurposeTo summarize and meta-analyze uveitis characteristics and multiple sclerosis (MS) phenotype of patients with multiple sclerosis-associated uveitis (MSAU) within a systematic review and meta-analysis.MethodsA comprehensive literature search was performed on January 25, 2023, utilizing PubMed, Embase, and Virtual Health Library (VHL) databases. We included studies involving patients with MSAU, such as case series with over 10 patients, cross-sectional, case-control, and cohort studies. Quality and risk of bias were assessed using CLARITY tools and validated metrics like the Hoy et al. and Hassan Murad et al. tools. The pooled analysis focused on 1) uveitis characteristics, 2) ocular complications, 3) MS phenotype, and 3) administered treatments for uveitis and MS. Gender-based subgroup analysis was conducted across continents; heterogeneity was measured using the I2 statistic. Statistical analysis was performed using R software version 4.3.1. The study was registered in PROSPERO with CRD42023453495 number.ResultsThirty-six studies were analyzed (24 with a low risk of bias, 8 with some concerns, and 4 with a high risk of bias), including 1,257 patients and 2,034 eyes with MSAU. The pooled analysis showed a mean age of 38.2 ± 12.1 years with a notable female predominance (67%, 95% CI [59%-73%]). MS before uveitis was seen in 59% of the cases (95% CI [48%-69%]), while uveitis was present before MS in 38% (95% CI [30%-48%]). The mean age for the first uveitis episode was 35.7 ± 8.3 years, predominantly affecting both eyes (77%, 95% CI [69%-83%], from 23 studies involving 452 patients). Intermediate uveitis was the most frequent anatomical location (68%, 95% CI [49%-82%], from 22 studies involving 530 patients), often following a recurrent course (63%, 95% CI [38%-83%]). Key complications included vision reduction (42%, 95% CI [19%-70%], from five articles involving 90 eyes), macular compromise (45%, 95% CI [20%-73%], from 4 studies involving 95 eyes), and cataracts (46%, 95% CI [32%-61%], from eight articles involving 230 eyes). Concerning MS phenotype, relapsing-remitting MS (RRMS) was the most common subtype (74%, 95% CI [64%-82%], from eight articles involving 134 patients), followed by secondary progressive MS (24%, 95% CI [18%-33%], from eight articles involving 125 patients). The most frequently occurring central nervous lesions were supratentorial (95%, 95% CI [70%-99%], from two articles involving 17 patients) and spinal cord (39%, 95% CI [16%-68%], from two articles involving 29 patients). The mean Expanded Disability Status Scale (EDSS) score and annual recurrence rates were 2.9 ± 0.6 and 1.07 ± 0.56, respectively. Treatment trends showed the prevalent use of Fingolimod (96%, 95% CI [17%-100%], from two articles involving 196 patients), Mycophenolate (48%, 95% CI [11%-87%], from four articles involving 51 patients), and Interferon-beta (43%, 95% CI [24%-65%], from 11 articles involving 325 patients).ConclusionMSAU primarily affects young adult females, typically presenting as bilateral intermediate uveitis with vision-related complications. The most common MS phenotype is RRMS, often associated with supratentorial and spinal cord lesions on imaging. These findings give ophthalmologists and neurologists a comprehensive clinical picture of MSAU, facilitating prompt diagnosis.

The four Excel files respectively show the raw data underpinning Figures 2, 3, 4 and 5 of the following manuscript: Dreyer, D., Adden, A., Chen, H., Frost, B.J., Mouritsen, H., Xu, J., Green, K.P., Whitehouse, M., Chahl, J., Wallace, J., Hu, G., Foster, J., Heinze, S. and Warrant, E.J. (2025). Bogong moths use a stellar compass for long-distance navigation at night.The Excel files for Figures 2 and 3 contain the raw data used to create the circular plots show in these figures. This data shows the mean directions and r-values of individual Bogong moths flown in a flight arena under the Australian starry night sky.The raw data for Figure 2, for moths flown in the open under a natural night sky, show the mean directions and r-values of 95 moths flown earlier and later in the evening under clear skies, and of 44 moths flown under overcast skies.The raw data for Figure 3, for moths flown in controlled lab conditions under a projected night sky and in a nulled geomagnetic field, show the mean directions and r-values of moths from Spring and Autumn subjected to natural, natural rotated (by 180°) and randomised starry skies (with statistics).The Excel files for Figures 4 and 5 show the raw data obtained from stellar compass neurons in the Bogong moth brain.The Excel file for Figure 4 consists of 5 sheets, one for each panel (a-e). The raw data for Figure 4a shows the frequency of action potentials (in impulses/s) of four cells responding to a rotating starry sky (clockwise CW or counterclockwise CCW) at each angular position of the sky (1°- 360°), including the standard deviation on each value (SD). The raw data for Figure 4b shows the tuning angle (φmax) at which each of our stellar compass neurons responded maximally under a rotating starry sky (in bimodal cells there were two such angles). The raw data for Figure 4c shows the variability in φmax for successive CW or CCW sky rotations in all of our individual cells (given as circular standard deviation in degrees). The raw data for Figure 4d shows φmax values for dot and bar control stimuli in a sub-set of unimodal and bimodal cells as well as for the angular difference between a cell’s φmax for dot/bar stimuli and its φmax for the starry sky. The raw data for Figure 4e shows signal-to-noise ratio values calculated for all of our individual cells derived from their responses to the Starry sky, randomised stars (Control), Dot and Bar stimuli. Response signal-to-noise ratio is the maximum response during sky rotation (i.e. at φ=φmax) divided by the standard error of the mean before rotation (calculated for each stimulus separately). The Excel file for Figure 5 consists of 2 sheets, one for Figure 5b and one for Figure 5c. The raw data for Figure 5b shows the frequency of action potentials (in impulses/s) for each of the three cells in response to a rotating starry sky and a control of rotating randomised stars (clockwise CW or counterclockwise CCW) at each angular position of the sky (1°- 360°), including the standard deviation on that value (SD). The raw data for Figure 5c shows the peak tuning directions (φmax) of the three cells following multiple CW and CCW sky rotations (the rotation angle when cellular firing frequency was maximal), and corresponding moth headings (in degrees).

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 50% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

The Household Income, Expenditure and Consumption Survey (HIECS) is of great importance among other household surveys conducted by statistical agencies in various countries around the world. This survey provides a large amount of data to rely on in measuring the living standards of households and individuals, as well as establishing databases that serve in measuring poverty, designing social assistance programs, and providing necessary weights to compile consumer price indices, considered to be an important indicator to assess inflation.

The HIECS 2008/2009 is the tenth Household Income, Expenditure and Consumption Survey that was carried out in 2008/2009, among a long series of similar surveys that started back in 1955.

The survey main objectives are: - To identify expenditure levels and patterns of population as well as socio- economic and demographic differentials. - To estimate the quantities, values of commodities and services consumed by households during the survey period to determine the levels of consumption and estimate the current demand which is important to predict future demands. - To measure mean household and per-capita expenditure for various expenditure items along with socio-economic correlates. - To define percentage distribution of expenditure for various items used in compiling consumer price indices which is considered important indicator for measuring inflation. - To define mean household and per-capita income from different sources. - To provide data necessary to measure standard of living for households and individuals. Poverty analysis and setting up a basis for social welfare assistance are highly dependant on the results of this survey. - To provide essential data to measure elasticity which reflects the percentage change in expenditure for various commodity and service groups against the percentage change in total expenditure for the purpose of predicting the levels of expenditure and consumption for different commodity and service items in urban and rural areas. - To provide data essential for comparing change in expenditure against change in income to measure income elasticity of expenditure. - To study the relationships between demographic, geographical, housing characteristics of households and their income and expenditure for commodities and services. - To provide data necessary for national accounts especially in compiling inputs and outputs tables. - To identify consumers behavior changes among socio-economic groups in urban and rural areas. - To identify per capita food consumption and its main components of calories, proteins and fats according to its sources and the levels of expenditure in both urban and rural areas. - To identify the value of expenditure for food according to sources, either from household production or not, in addition to household expenditure for non food commodities and services. - To identify distribution of households according to the possession of some appliances and equipments such as (cars, satellites, mobiles ...) in urban and rural areas. - To identify the percentage distribution of income recipients according to some background variables such as housing conditions, size of household and characteristics of head of household.

Compared to previous surveys, the current survey experienced certain peculiarities, among which: 1- Doubling the number of area segments from 1200 in the previous survey to 2526 segments with decreasing the number of households selected from each segment to be (20) households instead of (40) in the previous survey to ensure appropriate representatives in the society. 2- Changing the survey period to 15 days instead of one month in the previous one 200412005, to lighten the respondent burden and encourage more cooperation. 3- Adding some additional questions: a- Participation or the benefits gained from pension and social security system. b- Participation in health insurance system. 4- Increasing quality control Procedures especially for fieldwork to ensure data accuracy and avoid any errors in suitable time.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

Covering a sample of urban and rural areas in all the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 50% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

The sample of HIECS, 2008-2009 is a two-stage stratified cluster sample, approximately self-weighted, of nearly 48000 households. The main elements of the sampling design are described in the following.

1- Sample Size
It has been deemed important to retain the same sample size of the previous two HIECS rounds. Thus, a sample of about 48000 households has been considered. The justification of maintaining the sample size at this level is to have estimates with levels of precision similar to those of the previous two rounds: therefore trend analysis with the previous two surveys will not be distorted by substantial changes in sampling errors from round to another. In addition, this relatively large national sample implies proportional samples of reasonable sizes for smaller governorates. Nonetheless, over-sampling has been introduced to raise the sample size of small governorates to about 1000 households As a result, reasonably precise estimates could be extracted for those governorates. The over-sampling has resulted in a slight increase in the national sample to 48658 households.

2- Cluster size
An important lesson learned from the previous two HIECS rounds is that the cluster size applied in both surveys is found to be too large to yield an accepted design effect estimates. The cluster size was 40 households in the 2004-2005 round, descending from 80 households in the 1999-2000 round. The estimates of the design effect (deft) for most survey measures of the latest round were extraordinary large. As a result, it has been decided to decrease the cluster size to only 19 households (20 households in urban governorates to account for anticipated non-response in those governorates: in view of past experience non-response is almost nil in rural governorates).

A more detailed description of the different sampling stages and allocation of sample across governorates is provided in the Methodology document available among the documentation materials published in both Arabic and English.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different questionnaires have been designed as following: 1- Expenditure and consumption questionnaire. 2- Diary questionnaire for expenditure and consumption. 3- Income questionnaire.

In designing the questionnaires of expenditure, consumption and income, we were taking into our consideration the following: - Using the recent concepts and definitions of International Labor Organization approved in the International Convention of Labor Statisticians held in Geneva, 2003. - Using the recent Classification of Individual Consumption according to Purpose (COICOP). - Using more than one approach of expenditure measurement to serve many purposes of the survey.

A brief description of each questionnaire is given next:

1- Expenditure and Consumption Questionnaire

This questionnaire comprises 14 tables in addition to identification and geographic data of household on the cover page. The questionnaire is divided into two main sections.

Section one: Household schedule and other information. It includes: - Demographic characteristics and basic data for all household individuals consisting of 18 questions for every person. - Members of household who are currently working abroad. - The household ration card. - The main outlets that provide food and beverage. - Domestic and foreign tourism. - The housing conditions including 15 questions. - Means of transportation used to go to work or school. - The household possession of appliances and means of transportation. - This section includes some questions which help to define the social and economic level of households which in turn, help interviewers to check the plausibility of expenditure, consumption and income data.

Section two: Expenditure and consumption data It includes 14 tables as follows: - The quantity and value of food and beverages commodities actually consumed. - The quantity and value of the actual consumption of alcoholic beverages, tobacco and narcotics. - The quantity and value of the clothing and footwear. - The household expenditure for housing. - The household expenditure for furnishings, household equipment and routine maintenance of the house. - The household expenditure for health care services. - The household expenditure for transportation. - The household

Introduction

The Free-living Food Intake Cycle (FreeFIC) dataset was created by the Multimedia Understanding Group towards the investigation of in-the-wild eating behavior. This is achieved by recording the subjects’ meals as a small part part of their everyday life, unscripted, activities. The FreeFIC dataset contains the (3D) acceleration and orientation velocity signals ((6) DoF) from (22) in-the-wild sessions provided by (12) unique subjects. All sessions were recorded using a commercial smartwatch ((6) using the Huawei Watch 2™ and the MobVoi TicWatch™ for the rest) while the participants performed their everyday activities. In addition, FreeFIC also contains the start and end moments of each meal session as reported by the participants.

Description

FreeFIC includes (22) in-the-wild sessions that belong to (12) unique subjects. Participants were instructed to wear the smartwatch to the hand of their preference well ahead before any meal and continue to wear it throughout the day until the battery is depleted. In addition, we followed a self-report labeling model, meaning that the ground truth is provided from the participant by documenting the start and end moments of their meals to the best of their abilities as well as the hand they wear the smartwatch on. The total duration of the (22) recordings sums up to (112.71) hours, with a mean duration of (5.12) hours. Additional data statistics can be obtained by executing the provided python script stats_dataset.py. Furthermore, the accompanying python script viz_dataset.py will visualize the IMU signals and ground truth intervals for each of the recordings. Information on how to execute the Python scripts can be found below.

The script(s) and the pickle file must be located in the same directory.

Tested with Python 3.6.4

Requirements: Numpy, Pickle and Matplotlib

Calculate and echo dataset statistics

$ python stats_dataset.py

Visualize signals and ground truth

$ python viz_dataset.py

FreeFIC is also tightly related to Food Intake Cycle (FIC), a dataset we created in order to investigate the in-meal eating behavior. More information about FIC can be found here and here.

Publications

If you plan to use the FreeFIC dataset or any of the resources found in this page, please cite our work:

@article{kyritsis2020data,
title={A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches},
author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios},
journal={IEEE Journal of Biomedical and Health Informatics}, year={2020},
publisher={IEEE}}

@inproceedings{kyritsis2017automated, title={Detecting Meals In the Wild Using the Inertial Data of a Typical Smartwatch}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, booktitle={2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
year={2019}, organization={IEEE}}

Technical details

We provide the FreeFIC dataset as a pickle. The file can be loaded using Python in the following way:

import pickle as pkl import numpy as np

with open('./FreeFIC_FreeFIC-heldout.pkl','rb') as fh: dataset = pkl.load(fh)

The dataset variable in the snipet above is a dictionary with (5) keys. Namely:

'subject_id'

'session_id'

'signals_raw'

'signals_proc'

'meal_gt'

The contents under a specific key can be obtained by:

sub = dataset['subject_id'] # for the subject id ses = dataset['session_id'] # for the session id raw = dataset['signals_raw'] # for the raw IMU signals proc = dataset['signals_proc'] # for the processed IMU signals gt = dataset['meal_gt'] # for the meal ground truth

The sub, ses, raw, proc and gt variables in the snipet above are lists with a length equal to (22). Elements across all lists are aligned; e.g., the (3)rd element of the list under the 'session_id' key corresponds to the (3)rd element of the list under the 'signals_proc' key.

sub: list Each element of the sub list is a scalar (integer) that corresponds to the unique identifier of the subject that can take the following values: ([1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20]). It should be emphasized that the subjects with ids (15, 16, 17, 18, 19) and (20) belong to the held-out part of the FreeFIC dataset (more information can be found in ( )the publication titled "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al). Moreover, the subject identifier in FreeFIC is in-line with the subject identifier in the FIC dataset (more info here and here); i.e., FIC’s subject with id equal to (2) is the same person as FreeFIC’s subject with id equal to (2).

ses: list Each element of this list is a scalar (integer) that corresponds to the unique identifier of the session that can range between (1) and (5). It should be noted that not all subjects have the same number of sessions.

raw: list Each element of this list is dictionary with the 'acc' and 'gyr' keys. The data under the 'acc' key is a (N_{acc} \times 4) numpy.ndarray that contains the timestamps in seconds (first column) and the (3D) raw accelerometer measurements in (g) (second, third and forth columns - representing the (x, y ) and (z) axis, respectively). The data under the 'gyr' key is a (N_{gyr} \times 4) numpy.ndarray that contains the timestamps in seconds (first column) and the (3D) raw gyroscope measurements in ({degrees}/{second})(second, third and forth columns - representing the (x, y ) and (z) axis, respectively). All sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info here and here). Finally, the length of the raw accelerometer and gyroscope numpy.ndarrays is different ((N_{acc} eq N_{gyr})). This behavior is predictable and is caused by the Android platform.

proc: list Each element of this list is an (M\times7) numpy.ndarray that contains the timestamps, (3D) accelerometer and gyroscope measurements for each meal. Specifically, the first column contains the timestamps in seconds, the second, third and forth columns contain the (x,y) and (z) accelerometer values in (g) and the fifth, sixth and seventh columns contain the (x,y) and (z) gyroscope values in ({degrees}/{second}). Unlike elements in the raw list, processed measurements (in the proc list) have a constant sampling rate of (100) Hz and the accelerometer/gyroscope measurements are aligned with each other. In addition, all sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info here and here). No other preprocessing is performed on the data; e.g., the acceleration component due to the Earth's gravitational field is present at the processed acceleration measurements. The potential researcher can consult the article "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al. on how to further preprocess the IMU signals (i.e., smooth and remove the gravitational component).

meal_gt: list Each element of this list is a (K\times2) matrix. Each row represents the meal intervals for the specific in-the-wild session. The first column contains the timestamps of the meal start moments whereas the second one the timestamps of the meal end moments. All timestamps are in seconds. The number of meals (K) varies across recordings (e.g., a recording exist where a participant consumed two meals).

Ethics and funding

Informed consent, including permission for third-party access to anonymised data, was obtained from all subjects prior to their engagement in the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 727688 - BigO: Big data against childhood obesity.

Contact

Any inquiries regarding the FreeFIC dataset should be addressed to:

Dr. Konstantinos KYRITSIS

Multimedia Understanding Group (MUG) Department of Electrical & Computer Engineering Aristotle University of Thessaloniki University Campus, Building C, 3rd floor Thessaloniki, Greece, GR54124

Tel: +30 2310 996359, 996365 Fax: +30 2310 996398 E-mail: kokirits [at] mug [dot] ee [dot] auth [dot] gr