Use this summary report to properly interpret 2022 NSDUH estimates related to substance use, mental health, and treatment. The report accompanies theannual detailed tablesand covers overall methodology, key definitions for measures and terms used in 2022 NSDUH reports and tables, and selected analyses of the measures and how they should be interpreted.The report is organized into five chapters:Introduction.Description of the survey, including information about the sample design, data collection procedures and questionnaire changes, and key aspects of data processing such as development of the analysis weights.Technical details on the statistical methods and measurement, such as suppression criteria for unreliable estimates, statistical testing procedures, revised estimates for 2021 to account for data collection mode, and issues around selected substance use and mental health measures.Special topics related to prescription psychotherapeutic drugs.Description of other sources of data on substance use and mental health issues in the United States, including data sources for populations outside the NSDUH target population.An appendix covers key definitions used in NSDUH reports and tables.

The dataset includes all measured experimental data for plotting the figures in the paper "High power, electronically-controlled, source of user-defined vortex and vector light beams based on a few-mode fiber amplifier" in Photonics Research.

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from BILO Gridded Climate Data data provided by the CSIRO. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

Various climate variable summary for all 15 subregions. Including:

1. Time series mean annual Bureau of Meteorology Australian Water Availability Project (BAWAP) rainfall from 1900 - 2012.

2. Long term average BAWAP rainfall and Penman Potential Evapotranspiration (PET) from Jan 1981 - Dec 2012 for each month

3. Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg (average temperature); (iv) Tmax (maximum temperature); (v) Tmin (minimum temperature); (vi) VPD (Vapour Pressure Deficit); (vii) Rn (net Radiation); and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend.

4. Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). All data used in this analysis came directly from James Risbey, CSIRO Marine and Atmospheric Research (CMAR), Hobart. As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

Dataset History

Dataset was generated using various source data:

1. annual BAWAP rainfall

annual BAWAPrainfall

1. long term average BAWAP rainfall and Penman PET for each month

Monthly BAWAP rainfall

Monthly Penman PET

1. climate varaible trends

Monthly BAWAP rainfall

Monthly Penman PET

Monthly BAWAP Tair

Monthly BAWAP Tmax

Monthly BAWAP Tmin

Monthly VPD

Actual vapour measured at 9:00am, the saturated vapour is calculated from Tmax and Tmin.

Monthly Rn

Monthly Wind

This dataset is created by CLW Ecohydrological Time Series Remote Sensing Team. See http://www-data.iwis.csiro.au/ts/climate/wind/mcvicar_etal_grl2008/.

1. Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers

Dataset Citation

Bioregional Assessment Programme (2013) Mean climate variables for all subregions. Bioregional Assessment Derived Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/3f568840-0c77-4f74-bbf3-6f82d189a1fc.

Dataset Ancestors

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

Dataset of B-mode fatty liver ultrasound images

Byra, Michal; Styczynski, Grzegorz; Szmigielski, Cezary; Kalinowski, Piotr; Michalowski, Lukasz; Paluszkiewicz, Rafal; Ziarkiewicz-Wroblewska, Bogna; Zieniewicz, Krzysztof; Sobieraj, Piotr; Nowicki, Andrzej

The dataset used and described in: M. Byra, G. Styczynski, C. Szmigielski, P. Kalinowski. Ł. Michałowski4. R. Paluszkiewicz. B. Ziarkiewicz-Wróblewska, K. Zieniewicz. P. Sobieraj, A. Nowicki. Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. International Journal of Computer Assisted Radiology and Surgery, 2018. DOI: 10.1007/s11548-018-1843-2.

Abstract

Purpose

The nonalcoholic fatty liver disease is the most common liver abnormality. Up to date, liver biopsy is the reference standard for direct liver steatosis quantification in hepatic tissue samples. In this paper we propose a neural network-based approach for nonalcoholic fatty liver disease assessment in ultrasound.

Methods

We used the Inception-ResNet-v2 deep convolutional neural network pre-trained on the ImageNet dataset to extract high-level features in liver B-mode ultrasound image sequences. The steatosis level of each liver was graded by wedge biopsy. The proposed approach was compared with the hepatorenal index technique and the gray-level co-occurrence matrix algorithm. After the feature extraction, we applied the support vector machine algorithm to classify images containing fatty liver. Based on liver biopsy, the fatty liver was defined to have more than 5% of hepatocytes with steatosis. Next, we used the features and the Lasso regression method to assess the steatosis level.

Results

The area under the receiver operating characteristics curve obtained using the proposed approach was equal to 0.977, being higher than the one obtained with the hepatorenal index method, 0.959, and much higher than in the case of the gray-level co-occurrence matrix algorithm, 0.893. For regression the Spearman correlation coefficients between the steatosis level and the proposed approach, the hepatorenal index and the gray-level co-occurrence matrix algorithm were equal to 0.78, 0.80 and 0.39, respectively.

Conclusions

The proposed approach may help the sonographers automatically diagnose the amount of fat in the liver. The presented app

Please refer to the above work if you use the dataset in your research.

Contact: Michal Byra Department of Ultrasound Institute of Fundamental Technological Research Polish Academy of Sciences, Warsaw, Poland mbyra@ippt.pan.pl byra.michal@gmail.com

Abstract

The objective of this three-year panel survey is to provide the Government of Nepal with empirical evidence on the patterns of exposure to shocks at the household level and on the vulnerability of households’ welfare to these shocks. It covers 6,000 households in non-metropolitan areas of Nepal, which were interviewed in mid 2016. Being a relatively comprehensive and representative (rural) sample household survey, it can also be used for other research into living conditions of Nepali households in rural areas. This is the entire dataset for the first wave of the survey. The same households will be reinterviewed in mid 2017 and mid 2018. The survey dataset contains a multi-topic survey which was completed for each of the 6,000 households, and a community survey fielded to a senior community representative at the village development committee (VDC) level in each of the 400 PSUs.

Geographic coverage

All non-metropolitan areas in Nepal. Non-metropolitan areas are as defined by the 2010 Census.

Analysis unit

Household, following the NLSS definition.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample frame was all households in non-metropolitan areas per the 2010 Census definition, excluding households in the Kathmandu valley (Kathmandu, Lalitpur and Bhaktapur districts). The country was segmented into 11 analytical strata, defined to correspond to those used in the NLSS III (excluding the three urban strata used there). To increase the concentration of sampled households, 50 of the 75 districts in Nepal were selected with probability proportional to size (the measure of size being the number of households). PSUs were selected with probability proportional to size from the entire list of wards in the 50 selected districts, one stratum at a time. The number of PSUs per stratum is proportional to the stratum's population share, and corresponds closely to the allocations used in the LFS-II and NLSS-III (adjusted for different overall numbers of PSUs in those surveys).

In each of the selected PSUs (administrative wards), survey teams compiled a list of households in the ward based on existing administrative records, and cross-checked with local leaders. The number of households shown in the list was compared to the ward population in the 2010 Census, adjusted for likely population growth. Where the listed population deviated by more than 10% from the projected population based on the Census data, the team conducted a full listing of households in the ward. 15 households were selected at random from the ward list for interviewing, and a further 5 households were selected as potential replacements.

Sampling deviation

During the fieldwork, one PSU in Lapu VDC was inaccessible due to weather, and was replaced by a ward in Hastichaur VDC using PPS sampling on that stratum (excluding the already selected PSUs). All other sampled PSUs were reached, and a full sample of 6,000 households was interviewed in the first wave.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The household questionnaire contained 16 modules: the household roster; education; health; housing and access to facilities; food expenses and home production; non-food expenditures and inventory of durable goods; jobs and time use; wage jobs; farming and livestock; non-agriculture enterprises/activities; migration; credit, savings, and financial assets; private assistance; public assistance; shocks; and anthropometrics (for children less than 5 years). Where possible, the style of questions was kept similar to those used in the NLSS-III questionnaire for comparability reasons. In some cases, new modules needed to be developed. The shocks questionnaire was developed by the World Bank team. A food security module was added based on the design recommended by USAID, and a psychosocial questionnaire was also developed by social development specialists in the World Bank. The section on government and other assistance was also redesigned to cover a broader range of programs and elicit information on details such as experience with enrollment and frequency of payment.

The community questionnaire was fielded to a senior community representative at the VDC level in each of the 400 PSUs. The purpose of the community questionnaire was to obtain further details on access to services in each PSU, to gather information on shocks at the community level, and to collect market price data. The questionnaire had six modules: respondent details; community characteristics; access to facilities; educational facilities; community shocks, household shocks; and market price.

Cleaning operations

These are the raw data entered and checked by the survey firm, formatted to conform to the original questionnaire numbering system and confidentialized. The data were cleaned for spelling errors and translation of Nepali phrases, and suspicious values were checked by calling respondents. No other transformations have taken place.

Response rate

Of the 6,000 originally sampled households, 5,191 agreed to be interviewed. Of the 13.5% of households that were not interviewed, 11.1% were resident but could not be located by the team after two attempts, 0.9% were found to have outmigrated, and 1.4% refused. The 809 replacement households were drawn in order from the randomized list created during sampling (see above).

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Various climate variables summary for all 15 subregions based on Bureau of Meteorology Australian Water Availability Project (BAWAP) climate grids. Including

1. Time series mean annual BAWAP rainfall from 1900 - 2012.

2. Long term average BAWAP rainfall and Penman Potentail Evapotranspiration (PET) from Jan 1981 - Dec 2012 for each month

3. Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P (precipitation); (ii) Penman ETp; (iii) Tavg (average temperature); (iv) Tmax (maximum temperature); (v) Tmin (minimum temperature); (vi) VPD (Vapour Pressure Deficit); (vii) Rn (net radiation); and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend.

4. Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009).

As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

There are 4 csv files here:

BAWAP_P_annual_BA_SYB_GLO.csv

Desc: Time series mean annual BAWAP rainfall from 1900 - 2012.

Source data: annual BILO rainfall

P_PET_monthly_BA_SYB_GLO.csv

long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month

Climatology_Trend_BA_SYB_GLO.csv

Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend

Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv

Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

Dataset History

Dataset was created from various BAWAP source data, including Monthly BAWAP rainfall, Tmax, Tmin, VPD, etc, and other source data including monthly Penman PET, Correlation coefficient data. Data were extracted from national datasets for the GLO subregion.

BAWAP_P_annual_BA_SYB_GLO.csv

Desc: Time series mean annual BAWAP rainfall from 1900 - 2012.

Source data: annual BILO rainfall

P_PET_monthly_BA_SYB_GLO.csv

long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month

Climatology_Trend_BA_SYB_GLO.csv

Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend

Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv

Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

Dataset Citation

Bioregional Assessment Programme (2014) GLO climate data stats summary. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/afed85e0-7819-493d-a847-ec00a318e657.

Dataset Ancestors

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

Abstract \r

\r The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.\r \r \r \r There are 4 csv files here:\r \r BAWAP_P_annual_BA_SYB_GLO.csv\r \r Desc: Time series mean annual BAWAP rainfall from 1900 - 2012.\r \r Source data: annual BILO rainfall on \\wron\Project\BA\BA_N_Sydney\Working\li036_Lingtao_LI\Grids\BILO_Rain_Ann\\r \r \r \r P_PET_monthly_BA_SYB_GLO.csv\r \r long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month\r \r \r \r Climatology_Trend_BA_SYB_GLO.csv\r \r Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend\r \r \r \r Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv\r \r Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). All data used in this analysis came directly from James Risbey, CMAR, Hobart. As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).\r \r

Dataset History \r

\r Dataset was created from various BILO source data, including Monthly BILO rainfall, Tmax, Tmin, VPD, etc, and other source data including monthly Penman PET (calculated by Randall Donohue), Correlation coefficient data from James Risbey\r \r

Dataset Citation \r

\r Bioregional Assessment Programme (XXXX) SYD ALL climate data statistics summary. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/b0a6ccf1-395d-430e-adf1-5068f8371dea.\r \r

Dataset Ancestors \r

\r * Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012\r \r

The Interstellar Boundary Explorer, IBEX, has operated in space since 2008 updating our knowledge of the outer heliosphere and its interaction with the local interstellar medium. Start-time: 2008-12-25. There are currently 15 releases of IBEX-HI and/or IBEX-LO data covering the years from 2009 to 2018. This data set is derived from the Release 14 three-year IBEX-Hi map data with two-year overlaps of adjacent maps, 2009-2011, 2010-2012, and so forth through 2015-2017 from ram-direction fluxes with corrections for spacecraft motion, cg: Compton-Getting, but with no corrections, sp, for Energetic Neutral Atom, ENA, survival probability between 1 and 100 AU. The data set parameters include line-of-sight, LOS, integrated pressures computed separately from the Global Distributed Flux, GDF, the Ribbon Flux, and the Total Flux from summing GDF and Ribbon LOS pressures. Additionally there are signal to noise ratios for the GDF, Ribbon, and Total LOS pressures. Finally, there are power law slope values for the GDF differential flux and signal to noise ratios of the slope. The IBEX Release 14 data are archived as fully citable data. Please consult IBEX team publications and personnel for further details on production, processing, and usage of these data. The data consist of ram-direction sky maps in Solar Ecliptic Longitude, east and west, and Latitude angles for the above parameters. Details of the data and enabled science from Release 14 are given in the following journal publication: Schwadron, N. A., et al. 2018, Time Dependence of the IBEX Ribbon and the Globally Distributed Energetic Neutral Atom Flux Using the First 9 Years of Observations, DOI: 10.3847/1538-4365/aae48e. The following codes are used to define data set types in the multiple IBEX data releases: +-----------------------------------------------------------------------------------------------------------------------------------------------------------------+ Code Code definition --------- ------------------------------------------------------------------------------------------------------------------------------------------------------- cg Compton-Getting corrections have been applied to the data to account for the speed of the spacecraft relative to the direction of arrival of the ENAs nocg no Compton-Getting corrections --------- ------------------------------------------------------------------------------------------------------------------------------------------------------- sp survival probability corrections have been applied to the data to account for the loss of ENAs due to radiation pressure, photoionization and ionization via charge exchange with solar wind protons as they stream through the heliosphere. This correction scales the data out from IBEX at 1 AU to approximately 100 AU. In the original data this mode is denoted as Tabular. noSP no survival probability corrections have been applied to the data --------- ------------------------------------------------------------------------------------------------------------------------------------------------------- omni data from all directions ram data was collected when the spacecraft was ramming into the incoming ENAs antiram data was collected when the spacecraft was moving away from the incoming ENAs +-----------------------------------------------------------------------------------------------------------------------------------------------------------------+ This particular data set denoted in the original ASCII files as: +------------------------------------------------------------------------------------------------------------------------------------------------------------+ Directory Name File Content Description +---------------- -------------------------------------------------------------------------------------------------------------------------------------------+ GDFPressure Globally Distributed Flux Line-of-Sight Integrated Pressure in pdyne-au/cm^2 GDFSlope Power Law Slope of the differential flux spectrum for the Globally Distributed Flux GDFSlopeSN Signal/Noise ratio of the GDF differential flux power law slope where noise represents uncertainty GDFSN Globally Distributed Flux Signal/Noise, where Noise is defined as the uncertainty and the Signal is GDF Line-of-Sight integrated pressure RibbonPressure Ribbon Line-of-Sight Integrated Pressure in pdyne-au/cm^2 RibbonSN Ribbon Signal/Noise, where Noise is defined as the uncertainty and the Signal is GDF Line-of-Sight integrated pressure TotPressure Total Pressure in ENA maps including both the GDF and Ribbon. Line-of-Sight Integrated Pressure in pdyne-au/cm^2 TotSN Total Pressure Signal-to-Noise where noise represents uncertainty and signal represents the Total LOS integrated pressure +------------------------------------------------------------------------------------------------------------------------------------------------------------+

Abstract

The agricultural census and the survey on agricultural production methods were conducted jointly, i.e. within the same organisational structure, at the same time, and using a single electronic questionnaire and the same methods of data collection and processing. The agricultural census covered about 1.8 million of agricultural holdings. At all farms participating in the census, respondents were asked about the "other gainful activities carried out by the labour force" (OGA). The frame for the full survey was prepared on the basis of the list of holdings prepared for the census. When creating the list, an object-oriented approach was adopted for the first time, which meant that at the first stage the holdings (objects) were identified, their coordinates defined (they were located spatially) and their holders were identified on the basis of data from administrative sources. For domestic purposes, the farms with the smallest area, as well as those of little economic importance (meeting very low national thresholds) were included in the sample survey carried out jointly with the census. The survey on agricultural production methods was conducted on a sample of approximately 200 thousand farms in respect of the precision requirements set out in Regulation (EC) 1166/2008. The frame prepared for the agricultural census was used as the sampling frame.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the agricultural holding, defined as "an agricultural area, including forest land, buildings or their parts, equipment and stock if they constitute or may constitute an organized economic unit as well as rights related to running the farm". Two types of holding were distinguished (i) the natural persons' holdings (to which thresholds were applied) and (ii) legal persons holdings (no threshold applied).

Kind of data

Census/enumeration data [cen]

Sampling procedure

(a) Frame The frame for the agricultural census and the survey on agricultural production methods was based on the list of agricultural holdings. In the process of the list of farms creation for the needs of AC and SAPM 2010 the objective approach was used for the first time, which meant that on the first stage of work agricultural holdings were identified, its coordinates were defined (farms were located in space), and its holder was determined according to administrative data as described below. The list creation started from identification of all land parcels used for agricultural purposes. The land parcels found in the set of the Agency for Restructuring and Modernisation of Agriculture (including the Records of holdings and Records of producers) were combined into holding and had their holders defined. For the rest of land parcels, the holders were defined from the Records of Land and Buildings, afterwards the data concerning users were updated by the set of Real Property Tax Record.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

A single electronic questionnaire was used for data collection, combining information related to both the AC 2010 and the SAPM. The census covered all 16 core items recommended in the WCA 2010.

Questionnaire:

Section 0. Identifying characters Section 1. Land use Section 2. Economic activity Section 3. Income structure Section 4. Sown and other area Section 5. Livestock Section 6. tractor, machines and equipment Section 7. Use of fertilizers Section 8. Labour force Section 9. Agricultural production methods

Cleaning operations

a. DATA PROCESSING AND ARCHIVING The data captured through the CAPI, CATI and CAWI channels were gathered in the Operational Microdata Base (OMB) built for the AC 2010 and processed there (including control and correction of data, as well as completing the file obtained in the AC with the data obtained from administrative sources, imputed units and estimation for the SAPM). The data, depersonalized and validated in the OMB, were exported to an Analytical Microdata Base (AMB) to conduct analyses, prepare the data set for transmission to Eurostat and develop multidimensional tables for internal and external users.

b. CENSUS DATA QUALITY Except for a few isolated cases, the CAPI and CATI method resulted in fully completed questionnaires. The computer applications used enabled controls for completeness and correctness of the data already at the collection stage, also facilitating the use of necessary definitions and clarifications during the questionnaire completion process. A set of detailed questionnaire completion guidelines was developed and delivered during training sessions.

Data appraisal

The preliminary results of the agricultural census were published in February 2011 (basic data at the national level), and then in July 2011 in the publication entitled "Report on the Results of the 2010 Agricultural Census" (in a broader thematic scope, at NUTS3 2 level). The final results of the AC 2010 were disseminated by a sequence of publications, covering the main thematic areas of the census. The reference publications were released in paper form, and are available online (www.stat.gov.pl http://www.stat.gov.pl), and on CD-ROMs.

This Dynamics Explorer 2, DE 2, data set is a combination of the Vector Electric Field Instrument, VEFI, and Magnetometer-B, MAGB, high resolution data sets in spacecraft, SC, coordinates submitted to NSSDC. The following orbit-altitude, OA, parameters have been added to the data set: 1) Model magnetic field, SC coordinates 2) Satellite altitude 3) Geographic latitude and longitude 4) Magnetic local time 5) Invariant latitudeThe VEFI data set is described in the file VEFIVOLDESC.SFD and the MAGB data set is described in the file MAGBVOLDESC.SFD, these files are portions of the Standard Format Data Unit, SFDU, metadata files submitted with the VEFI and MAGB data to NSSDC and are included in each volume of this data set. This data set consists of daily files from 1981-08-15, day of year 227, to 1983-02-16, day of year 47. Each file contains all the data available for a given day. During the merging of the data sets it was found that although VEFI and MAGB should cover the same time spans, they do not, due perhaps to the fact that the original MAGB high resolution data set was created on the DE Sigma-9 in Sigma-9 format by using the DE telemetry tapes, while the VEFI high resolution data set was created on the DE MicroVAX system using the DE telemetry data base on optical disk. In order to keep the largest amount of data possible, the merged data set includes all the available VEFI and MAGB data, for those times when VEFI data was available but MAGB was not, 6.54% of the time spanned by this data product, a fill value of 9999999. was given to the MAGB data. Likewise, for those times when MAGB data was available but VEFI was not, 6.87% of the time, the fill value was assigned to the VEFI data. Times for which both VEFI and MAGB data were fill values in the original data sets were not included in the merged data set. There were also times when certain OA parameters were fill values in the OA data base and they are therefore also fill values in this merged data set. The model magnetic field had fill values for 8.55% of the data. Statistics were not kept for the other OA parameters. Each daily file contains a record per measurement. The total number of records in each file varies depending on the amount of data available for a given day.The DE 2 spacecraft, which was the low-altitude mission component, complemented the high-altitude mission DE 1 spacecraft and was placed into an orbit with a perigee sufficiently low to permit measurements of neutral composition, temperature, and wind. The apogee was high enough to permit measurements above the interaction regions of suprathermal ions, and also plasma flow measurements at the feet of the magnetospheric field lines. The general form of the spacecraft was a short polygon 137 cm in diameter and 115 cm high. The triaxial antennas were 23 m tip-to-tip. One 6 m boom was provided for remote measurements. The spacecraft weight was 403 kg. Power was supplied by a solar cell array, which charged two 6 ampere-hour nickel-cadmium batteries. The spacecraft was three-axis stabilized with the yaw axis aligned toward the center of the Earth to within 1°. The spin axis was normal to the orbit plane within 1° with a spin rate of one revolution per orbit. A single-axis scan platform was included in order to mount the low-altitude plasma instrument (ID: 81-070B-08). The platform rotated about the spin axis. A pulse code modulation telemetry data system was used that operated in real time or in a tape recorder mode. Data were acquired on a science-problem-oriented basis, with closely coordinated operations of the various instruments, both satellites, and supportive experiments. Measurements were temporarily stored on tape recorders before transmission at an 8:1 playback-to-record ratio. Since commands were also stored in a command memory unit, spacecraft operations were not real time. Additional details can be found in R.A. Hoffman et al., Space Sci. Instrum., 5(4), 349, 1981. DE-2 reentered the atmosphere on February 19, 1983. A triaxial fluxgate magnetometer onboard DE 2, MAG-B, similar to one on board DE 1 (ID: 81-070A-01), was used to obtain the magnetic field data needed to study the magnetosphere-ionosphere-atmosphere coupling.The primary objectives of this investigation were to measure field aligned currents in the auroral oval and over the polar cap at two different altitudes using the two spacecraft, and to correlate these measurements with observations of electric fields, plasma waves, suprathermal particles, thermal particles, and auroral images obtained from investigation (ID: 81-070A-03). The magnetometer had digital compensation of the ambient field in 8000 nT increments. The instrument incorporated its own 12-bit analog-to-digital, A/D, converter, a 4-bit digital compensation register for each axis, and a system control that generated a 48-bit data word consisting of a 16-bit representation of the field measured along each of three magnetometer axes. Track and hold modules were used to obtain simultaneous samples on all three axes. The instrument bandwidth was 25 Hz. The analog range was ±62000 nT, the accuracy was ±4 nT, and the resolution was 1.5 nT. The time resolution was 16 vector samples/s. More details can be found in W.H. Farthing et al., Space Sci. Instrum., 5(4), 551, 1981. The Vector Electric Field Instrument, VEFI, used flight-proven double-probe techniques with 20 m baselines to obtain measurements of DC electric fields.This electric field investigation had the following objectives: 1) obtain accurate and comprehensive triaxial DC electric field measurements at ionospheric altitudes in order to refine the basic spatial patterns, define the large-scale time history of these patterns, and study the small-scale temporal and spatial variations within the overall patterns 2) study the degree to which and in what region the electric field projects to the equatorial plane 3) obtain measurements of extreme low frequency, ELF, and lower frequency irregularity structures* 4) perform numerous correlative studiesThe VEFI instrument consisted of six cylindrical elements 11 m long and 28 mm in diameter. Each antenna was insulated from the plasma except for the outer 2 m. The baseline, or distance between the midpoints of these 2-m active elements, was 20 m. The antennas were interlocked along the edges to prevent oscillation and to increase their rigidity against drag forces. The basic electronic system was very similar in concept to those used on IMP-8 and ISEE 1, but modified for a three-axis measurement on a nonspinning spacecraft. At the core of the system were the high-impedance (10¹² ohm) preamplifiers, whose outputs were accurately subtracted and digitized with 14-bit A/D conversion for sensitivity to about 0.1 µV/m to maintain high resolution for subsequent removal of the cross product of the electric field, V, and magnetic field, B, vectors in data processing. This provided the basic DC measurement. Other circuitry was used to aid in interpreting the DC data and to measure rapid variations in the signals detected by the antennas. The planned DC electric field range was ±1 V/m, the planned resolution was 0.1 mV/m, and the variational AC electric field was measured from 4 Hz to 1024 Hz. The DC electric field was measured at 16 samples/s. The AC electric field was measured from 1 µV/m to 10 mV/m root mean square, rms. Note that the VEFI antenna pair perpendicular to the orbit plane onboard DE 2 did not deploy. Additional details are found in N.C. Maynard et al., Space Sci. Instrum., 5(4), 523, 1981.

These data sets have been generated based on the analytic models to estimate signal to the noise ratio (SNR) for spans, links, and lightpaths of a Flexible Optical Network (FON) over standard single-mode fiber (SSFM). Please see the following link to read all data sets: https://doi.org/10.5281/zenodo.6529744

Summary:

Estimated stand-off distance between ADS-B equipped aircraft and obstacles. Obstacle information was sourced from the FAA Digital Obstacle File and the FHWA National Bridge Inventory. Aircraft tracks were sourced from processed data curated from the OpenSky Network. Results are presented as histograms organized by aircraft type and distance away from runways.

Description:

For many aviation safety studies, aircraft behavior is represented using encounter models, which are statistical models of how aircraft behave during close encounters. They are used to provide a realistic representation of the range of encounter flight dynamics where an aircraft collision avoidance system would be likely to alert. These models currently and have historically have been limited to interactions between aircraft; they have not represented the specific interactions between obstacles and aircraft equipped transponders. In response, we calculated the standoff distance between obstacles and ADS-B equipped manned aircraft.

For robustness, this assessment considered two different datasets of manned aircraft tracks and two datasets of obstacles. For robustness, MIT LL calculated the standoff distance using two different datasets of aircraft tracks and two datasets of obstacles. This approach aligned with the foundational research used to support the ASTM F3442/F3442M-20 well clear criteria of 2000 feet laterally and 250 feet AGL vertically.

The two datasets of processed tracks of ADS-B equipped aircraft curated from the OpenSky Network. It is likely that rotorcraft were underrepresented in these datasets. There were also no considerations for aircraft equipped only with Mode C or not equipped with any transponders. The first dataset was used to train the v1.3 uncorrelated encounter models and referred to as the “Monday” dataset. The second dataset is referred to as the “aerodrome” dataset and was used to train the v2.0 and v3.x terminal encounter model. The Monday dataset consisted of 104 Mondays across North America. The other dataset was based on observations at least 8 nautical miles within Class B, C, D aerodromes in the United States for the first 14 days of each month from January 2019 through February 2020. Prior to any processing, the datasets required 714 and 847 Gigabytes of storage. For more details on these datasets, please refer to "Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing" and “Benchmarking the Processing of Aircraft Tracks with Triples Mode and Self-Scheduling.”

Two different datasets of obstacles were also considered. First was point obstacles defined by the FAA digital obstacle file (DOF) and consisted of point obstacle structures of antenna, lighthouse, meteorological tower (met), monument, sign, silo, spire (steeple), stack (chimney; industrial smokestack), transmission line tower (t-l tower), tank (water; fuel), tramway, utility pole (telephone pole, or pole of similar height, supporting wires), windmill (wind turbine), and windsock. Each obstacle was represented by a cylinder with the height reported by the DOF and a radius based on the report horizontal accuracy. We did not consider the actual width and height of the structure itself. Additionally, we only considered obstacles at least 50 feet tall and marked as verified in the DOF.

The other obstacle dataset, termed as “bridges,” was based on the identified bridges in the FAA DOF and additional information provided by the National Bridge Inventory. Due to the potential size and extent of bridges, it would not be appropriate to model them as point obstacles; however, the FAA DOF only provides a point location and no information about the size of the bridge. In response, we correlated the FAA DOF with the National Bridge Inventory, which provides information about the length of many bridges. Instead of sizing the simulated bridge based on horizontal accuracy, like with the point obstacles, the bridges were represented as circles with a radius of the longest, nearest bridge from the NBI. A circle representation was required because neither the FAA DOF or NBI provided sufficient information about orientation to represent bridges as rectangular cuboid. Similar to the point obstacles, the height of the obstacle was based on the height reported by the FAA DOF. Accordingly, the analysis using the bridge dataset should be viewed as risk averse and conservative. It is possible that a manned aircraft was hundreds of feet away from an obstacle in actuality but the estimated standoff distance could be significantly less. Additionally, all obstacles are represented with a fixed height, the potentially flat and low level entrances of the bridge are assumed to have the same height as the tall bridge towers. The attached figure illustrates an example simulated bridge.

It would had been extremely computational inefficient to calculate the standoff distance for all possible track points. Instead, we define an encounter between an aircraft and obstacle as when an aircraft flying 3069 feet AGL or less comes within 3000 feet laterally of any obstacle in a 60 second time interval. If the criteria were satisfied, then for that 60 second track segment we calculate the standoff distance to all nearby obstacles. Vertical separation was based on the MSL altitude of the track and the maximum MSL height of an obstacle.

For each combination of aircraft track and obstacle datasets, the results were organized seven different ways. Filtering criteria were based on aircraft type and distance away from runways. Runway data was sourced from the FAA runways of the United States, Puerto Rico, and Virgin Islands open dataset. Aircraft type was identified as part of the em-processing-opensky workflow.

• All: No filter, all observations that satisfied encounter conditions
• nearRunway: Aircraft within or at 2 nautical miles of a runway
• awayRunway: Observations more than 2 nautical miles from a runway
• glider: Observations when aircraft type is a glider
• fwme: Observations when aircraft type is a fixed-wing multi-engine
• fwse: Observations when aircraft type is a fixed-wing single engine
• rotorcraft: Observations when aircraft type is a rotorcraft

License

This dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International(CC BY-NC-ND 4.0).

This license requires that reusers give credit to the creator. It allows reusers to copy and distribute the material in any medium or format in unadapted form and for noncommercial purposes only. Only noncommercial use of your work is permitted. Noncommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. Exceptions are given for the not for profit standards organizations of ASTM International and RTCA.

MIT is releasing this dataset in good faith to promote open and transparent research of the low altitude airspace. Given the limitations of the dataset and a need for more research, a more restrictive license was warranted. Namely it is based only on only observations of ADS-B equipped aircraft, which not all aircraft in the airspace are required to employ; and observations were source from a crowdsourced network whose surveillance coverage has not been robustly characterized.

As more research is conducted and the low altitude airspace is further characterized or regulated, it is expected that a future version of this dataset may have a more permissive license.

Distribution Statement

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

© 2021 Massachusetts Institute of Technology.

Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.

This material is based upon work supported by the Federal Aviation Administration under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Federal Aviation Administration.

This document is derived from work done for the FAA (and possibly others); it is not the direct product of work done for the FAA. The information provided herein may include content supplied by third parties. Although the data and information contained herein has been produced or processed from sources believed to be reliable, the Federal Aviation Administration makes no warranty, expressed or implied, regarding the accuracy, adequacy, completeness, legality, reliability or usefulness of any information, conclusions or recommendations provided herein. Distribution of the information contained herein does not constitute an endorsement or warranty of the data or information provided herein by the Federal Aviation Administration or the U.S. Department of Transportation. Neither the Federal Aviation Administration nor the U.S. Department of

This data release contains the D-score (version 0.1) daily streamflow performance benchmark results for the National Water Model (NWM) Retrospective version 2.1 computed at streamgage benchmark locations (version 1) as defined by Foks and others (2022). Model error was determined by evaluating predicted daily mean streamflow (aggregated from an hourly timestep) versus observed daily mean streamflow. Using those errors, the D-score performance benchmark computes the mean squared logarithmic error (MSLE), then decomposes the overall MSLE into orthogonal components such as bias, distribution, and sequence (Hodson and others, 2021). For easier interpretation, the MSLE components can be passed through a scoring function as described in Hodson and others (2021). References: Foks, S.S., Towler, E., Hodson, T.O., Bock, A.R., Dickinson, J.E., Dugger, A.L., Dunne, K.A., Essaid, H.I., Miles, K.A., Over, T.M., Penn, C.A., Russell, A.M., Saxe, S.W., and Simeone, C.E., 2022, Streamflow benchmark locations for conterminous United States (cobalt gages): U.S. Geological Survey data release, https://doi.org/10.5066/P972P42Z. Hodson, T.O., Over, T.M., and Foks, S.S., 2021. Mean squared error, deconstructed. Journal of Advances in Modeling Earth Systems, 13, e2021MS002681. https://doi.org/10.1029/2021MS002681.

Proportion of school aged children in full time education travelling to school by the mode of travel that they usually use. Mode of transport is defined as six modes: cars, including vans and taxis, car share, public transport, walking, cycling, and other.

General

For more details and the most up-to-date information please consult our project page: https://kainmueller-lab.github.io/fisbe.

Summary

A new dataset for neuron instance segmentation in 3d multicolor light microscopy data of fruit fly brains

30 completely labeled (segmented) images

71 partly labeled images

altogether comprising ∼600 expert-labeled neuron instances (labeling a single neuron takes between 30-60 min on average, yet a difficult one can take up to 4 hours)

To the best of our knowledge, the first real-world benchmark dataset for instance segmentation of long thin filamentous objects

A set of metrics and a novel ranking score for respective meaningful method benchmarking

An evaluation of three baseline methods in terms of the above metrics and score

Abstract

Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging, thin filamentous and widely branching morphologies, multiple neurons are tightly inter-weaved, and partial volume effects, uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research, namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing, to date methods are typically benchmarked on synthetic datasets. To address this gap, we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset, the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition, we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly, we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies, and facilitate scientific discovery in basic neuroscience.

Dataset documentation:

We provide a detailed documentation of our dataset, following the Datasheet for Datasets questionnaire:

FISBe Datasheet

Our dataset originates from the FlyLight project, where the authors released a large image collection of nervous systems of ~74,000 flies, available for download under CC BY 4.0 license.

Files

fisbe_v1.0_{completely,partly}.zip

contains the image and ground truth segmentation data; there is one zarr file per sample, see below for more information on how to access zarr files.

fisbe_v1.0_mips.zip

maximum intensity projections of all samples, for convenience.

sample_list_per_split.txt

a simple list of all samples and the subset they are in, for convenience.

view_data.py

a simple python script to visualize samples, see below for more information on how to use it.

dim_neurons_val_and_test_sets.json

a list of instance ids per sample that are considered to be of low intensity/dim; can be used for extended evaluation.

Readme.md

general information

How to work with the image files

Each sample consists of a single 3d MCFO image of neurons of the fruit fly.For each image, we provide a pixel-wise instance segmentation for all separable neurons.Each sample is stored as a separate zarr file (zarr is a file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification.").The image data ("raw") and the segmentation ("gt_instances") are stored as two arrays within a single zarr file.The segmentation mask for each neuron is stored in a separate channel.The order of dimensions is CZYX.

We recommend to work in a virtual environment, e.g., by using conda:

conda create -y -n flylight-env -c conda-forge python=3.9conda activate flylight-env

How to open zarr files

Install the python zarr package:

pip install zarr

Opened a zarr file with:

import zarrraw = zarr.open(, mode='r', path="volumes/raw")seg = zarr.open(, mode='r', path="volumes/gt_instances")

optional:import numpy as npraw_np = np.array(raw)

Zarr arrays are read lazily on-demand.Many functions that expect numpy arrays also work with zarr arrays.Optionally, the arrays can also explicitly be converted to numpy arrays.

How to view zarr image files

We recommend to use napari to view the image data.

Install napari:

pip install "napari[all]"

Save the following Python script:

import zarr, sys, napari

raw = zarr.load(sys.argv[1], mode='r', path="volumes/raw")gts = zarr.load(sys.argv[1], mode='r', path="volumes/gt_instances")

viewer = napari.Viewer(ndisplay=3)for idx, gt in enumerate(gts): viewer.add_labels( gt, rendering='translucent', blending='additive', name=f'gt_{idx}')viewer.add_image(raw[0], colormap="red", name='raw_r', blending='additive')viewer.add_image(raw[1], colormap="green", name='raw_g', blending='additive')viewer.add_image(raw[2], colormap="blue", name='raw_b', blending='additive')napari.run()

Execute:

python view_data.py /R9F03-20181030_62_B5.zarr

Metrics

S: Average of avF1 and C

avF1: Average F1 Score

C: Average ground truth coverage

clDice_TP: Average true positives clDice

FS: Number of false splits

FM: Number of false merges

tp: Relative number of true positives

For more information on our selected metrics and formal definitions please see our paper.

Baseline

To showcase the FISBe dataset together with our selection of metrics, we provide evaluation results for three baseline methods, namely PatchPerPix (ppp), Flood Filling Networks (FFN) and a non-learnt application-specific color clustering from Duan et al..For detailed information on the methods and the quantitative results please see our paper.

License

The FlyLight Instance Segmentation Benchmark (FISBe) dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Citation

If you use FISBe in your research, please use the following BibTeX entry:

@misc{mais2024fisbe, title = {FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures}, author = {Lisa Mais and Peter Hirsch and Claire Managan and Ramya Kandarpa and Josef Lorenz Rumberger and Annika Reinke and Lena Maier-Hein and Gudrun Ihrke and Dagmar Kainmueller}, year = 2024, eprint = {2404.00130}, archivePrefix ={arXiv}, primaryClass = {cs.CV} }

Acknowledgments

We thank Aljoscha Nern for providing unpublished MCFO images as well as Geoffrey W. Meissner and the entire FlyLight Project Team for valuablediscussions.P.H., L.M. and D.K. were supported by the HHMI Janelia Visiting Scientist Program.This work was co-funded by Helmholtz Imaging.

Changelog

There have been no changes to the dataset so far.All future change will be listed on the changelog page.

Contributing

If you would like to contribute, have encountered any issues or have any suggestions, please open an issue for the FISBe dataset in the accompanying github repository.

All contributions are welcome!

To generate a representative dataset of real-world traffic in ISCX we defined a set of tasks, assuring that our dataset is rich enough in diversity and quantity. We created accounts for users Alice and Bob in order to use services like Skype, Facebook, etc. Below we provide the complete list of different types of traffic and applications considered in our dataset for each traffic type (VoIP, P2P, etc.)

We captured a regular session and a session over VPN, therefore we have a total of 14 traffic categories: VOIP, VPN-VOIP, P2P, VPN-P2P, etc. We also give a detailed description of the different types of traffic generated:

Browsing: Under this label we have HTTPS traffic generated by users while browsing or performing any task that includes the use of a browser. For instance, when we captured voice-calls using hangouts, even though browsing is not the main activity, we captured several browsing flows.

Email: The traffic samples generated using a Thunderbird client, and Alice and Bob Gmail accounts. The clients were configured to deliver mail through SMTP/S, and receive it using POP3/SSL in one client and IMAP/SSL in the other.

Chat: The chat label identifies instant-messaging applications. Under this label we have Facebook and Hangouts via web browsers, Skype, and IAM and ICQ using an application called pidgin [14].

Streaming: The streaming label identifies multimedia applications that require a continuous and steady stream of data. We captured traffic from Youtube (HTML5 and flash versions) and Vimeo services using Chrome and Firefox.

File Transfer: This label identifies traffic applications whose main purpose is to send or receive files and documents. For our dataset we captured Skype file transfers, FTP over SSH (SFTP) and FTP over SSL (FTPS) traffic sessions.

VoIP: The Voice over IP label groups all traffic generated by voice applications. Within this label we captured voice calls using Facebook, Hangouts and Skype.

TraP2P: This label is used to identify file-sharing protocols like Bittorrent. To generate this traffic we downloaded different .torrent files from a public a repository and captured traffic sessions using the uTorrent and Transmission applications.

The traffic was captured using Wireshark and tcpdump, generating a total amount of 28GB of data. For the VPN, we used an external VPN service provider and connected to it using OpenVPN (UDP mode). To generate SFTP and FTPS traffic we also used an external service provider and Filezilla as a client.

To facilitate the labeling process, when capturing the traffic all unnecessary services and applications were closed. (The only application executed was the objective of the capture, e.g., Skype voice-call, SFTP file transfer, etc.) We used a filter to capture only the packets with source or destination IP, the address of the local client (Alice or Bob).

The full research paper outlining the details of the dataset and its underlying principles:

Gerard Drapper Gil, Arash Habibi Lashkari, Mohammad Mamun, Ali A. Ghorbani, "Characterization of Encrypted and VPN Traffic Using Time-Related Features", In Proceedings of the 2nd International Conference on Information Systems Security and Privacy(ICISSP 2016) , pages 407-414, Rome, Italy.
ISCXFlowMeter has been written in Java for reading the pcap files and create the csv file based on selected features. The UNB ISCX Network Traffic (VPN-nonVPN) dataset consists of labeled network traffic, including full packet in pcap format and csv (flows generated by ISCXFlowMeter) also are publicly available for researchers.

For more information contact cic@unb.ca.

The UNB ISCX Network Traffic Dataset content
Traffic: Content
Web Browsing: Firefox and Chrome
Email: SMPTS, POP3S and IMAPS
Chat: ICQ, AIM, Skype, Facebook and Hangouts
Streaming: Vimeo and Youtube
File Transfer: Skype, FTPS and SFTP using Filezilla and an external service
VoIP: Facebook, Skype and Hangouts voice calls (1h duration)
P2P: uTorrent and Transmission (Bittorrent)
; cic@unb.ca.

This WORDNET TENSOR DATA consists of a collection of triplets (synset, relation_type, triplet) extracted from WordNet 3.0 (http://wordnet.princeton.edu). This data set can be seen as a 3-mode tensor depicting ternary relationships between synsets.The definitions file (wordnet-mlj12-definitions.txt) contains one synset per line with the following format: synset_id (a 8-digit unique identifier) intelligible name (word+POS_tag+sense_index), definition. The previous 3 pieces of information are separated by a tab ('\t').All wordnet-mlj12-*.txt files contain one triplet per line, with 2 synset_ids and relation type identifier in a tab separated format. The first element is the synset_id of the left hand side of the relation triple, the third one is the synset_id of the right hand side and the second element is the name of the type of relations between them.There are 40,943 synsets and 18 relation types among them. The training set contains 141,442 triplets, the validation set 5,000 and the test set 5,000.All triplets are unique and we made sure that all synsets appearing in the validation or test sets were occurring in the training set.The WN18.zip file contains the other files, with more compression than the default "download all".

Abstract

The survey was conducted in Croatia between November 2018 and November 2019. The survey was part of a joint project of the European Bank for Reconstruction and Development (EBRD), the European Investment Bank (EIB) and the World Bank Group (WBG).

The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms’ experiences and enterprises’ perception of the environment in which they operate.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

For the Croatia ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for 2019 Croatia ES was selected using stratified random sampling, following the methodology explained in the Sampling Note.

Three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in “The Croatia 2019 Enterprise Surveys Data Set” report Appendix C.

Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 3.1 codes 15-37), Retail (ISIC code 52) and Services (ISIC codes 45, 47, 50, 51, 55, 60-64, and 72).

For the Croatia ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Regional stratification for the Croatia ES was done across two regions: Kontinentalna Hrvatska and Jadranska Hrvatska.

Note: See Sections II and III of “The Croatia 2019 Enterprise Surveys Data Set” report for additional details on the sampling procedure.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires - Manufacturing amd Services were used to collect the survey data.

The questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond (-8) as a different option from don’t know (-9).

b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. For this survey there were zero non-responses for the sales variable, d2. Please, note that for this specific question, refusals were not separately identified from “Don’t know” responses.

The number of interviews per contacted establishments was 11.2%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units.

The share of rejections per contact was 63.8%.

Abstract

The survey was conducted in in Latvia between November 2018 and December 2019. The survey was part of a joint project of the European Bank for Reconstruction and Development (EBRD), the European Investment Bank (EIB) and the World Bank Group (WBG). The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms’ experiences and enterprises’ perception of the environment in which they operate.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

For the Latvia ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for 2019 Latvia ES was selected using stratified random sampling, following the methodology explained in the Sampling Note.

Three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in "The Latvia 2019 Enterprise Surveys Data Set" report, Appendix C.

Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 3.1 codes 15-37), Retail (ISIC code 52) and Other Services (ISIC codes 45, 50, 51, 55, 60-64, and 72).

For the Latvia ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Regional stratification for the Latvia ES was done across three regions: Riga & Pieriga, Kurzeme & Zemgale and Vidzeme & Latgale.

Note: See Sections II and III of “The Latvia 2019 Enterprise Surveys Data Set” report for additional details on the sampling procedure.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two questionnaires - Manufacturing amd Services were used to collect the survey data.

The Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module).

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond (-8) as a different option from don’t know (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response. Please, note that for this specific question, refusals were not separately identified from “Don’t know” responses.

The number of interviews per contacted establishments was 14.6%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units.

The share of rejections per contact was 59.2%.

A large database of natural electromagnetic emissions of lower band whistler mode chorus and exohiss within the Earth's magnetosphere. It is based on more than 124 million selected survey measurements of the magnetic fluctuations, recorded between 2001 and 2020 by the two NASA Van Allen Probes and four ESA Cluster spacecraft. The database provides a comprehensive view of amplitudes of these important electromagnetic emissions in the audible frequency range. We carefully condition the data to minimize the influence of instrumental artefacts. We also remove all data points which may be contaminated by instrumental noise using a newly developed method to define detection thresholds as a function of frequency, time, and instrument settings. The database can serve as a valuable resource for a broad range of scientists studying space weather, magnetospheric physics, and radiation belt dynamics.NOTE ON UPDATE TO V2:After the publication of the paper and the dataset, we discovered lines with errors in universal time (UTC) in the Data records 8-12. These lines represent a very small fraction of the total data volume (0.07%).The problem originated in the source datasets: in the Survey dataset of the Van Allen Probe B EMFISIS Waves instrument, and in the Normal mode dataset of the Cluster 1-4 STAFF-SA instruments (described in the Methods section). In the Survey dataset of the Van Allen Probe B EMFISIS Waves instrument, last 11 measurements from the source file for 2018-06-14 are by mistake repeated in the beginning of the file for 2018-06-15. In the Normal mode dataset of the Cluster 1-4 STAFF-SA instruments, the same UTC is used twice for different measurements during several brief intervals between 2003 and 2005. These errors then propagated through the processing chain shown in Figure 1 down to the resulting Data records of frequency integrated root-mean-square amplitudes of lower-band chorus/exohiss emissions for Van Allen Probe B and Cluster 1-4.We fixed the problem by removing the lines with these errors from Data records 8-12. In Data record 8, we removed one instance of the duplicated lines. In Data records 9-12 we removed both sequences of lines with duplicated UTC but different measurements. As the number of removed lines is negligible with respect to the total data volume, the paper still accurately describes these modified Data records. Each Data record now has continuously increasing UTC, without duplicated lines nor back steps in time.Specifically, the following lines and UTC intervals were removed:Data record 8, file VB_LBall_Bw_2025Mar28_133831.txtlines 20867172 - 20867182, UTC 2018-06-14 23:58:55 - 23:59:55Data record 9, file C1_LBall_Bw_2025Mar28_133831.txtlines 1111109 - 1113716, UTC 2003-10-08 02:21:53 - 03:56:01lines 1672036 - 1674583, UTC 2004-08-28 07:42:08 - 09:26:01lines 1859134 - 1859531, UTC 2004-12-09 00:00:07 - 00:13:47lines 1871070 - 1873155, UTC 2004-12-15 16:58:57 - 18:26:03lines 2026082 - 2027821, UTC 2005-03-14 00:51:04 - 01:59:09lines 2421846 - 2432723, UTC 2005-10-05 02:27:24 - 12:52:15lines 2526624 - 2528439, UTC 2005-11-19 07:39:07 - 08:56:01Data record 10, file C2_LBall_Bw_2025Mar28_133831.txtlines 1053628 - 1056171, UTC 2003-10-08 02:24:13 - 03:56:01lines 1607403 - 1609662, UTC 2004-08-28 07:53:09 - 17:58:48lines 1795057 - 1795556, UTC 2004-12-09 00:00:03 - 00:17:15lines 1806724 - 1808375, UTC 2004-12-15 17:14:29 - 18:25:59lines 1962990 - 1964733, UTC 2005-03-14 00:51:07 - 01:59:57lines 2337501 - 2347088, UTC 2005-10-05 01:36:32 - 11:51:55lines 2430147 - 2432910, UTC 2005-11-19 07:04:51 - 08:56:01Data record 11, file C3_LBall_Bw_2025Mar28_133831.txtlines 1038967 - 1041518, UTC 2003-10-08 02:23:57 - 03:56:01lines 1580806 - 1583117, UTC 2004-08-28 18:07:55 - 08:00:41lines 1761603 - 1762338, UTC 2004-12-09 00:25:31 - 00:00:07lines 1773586 - 1775065, UTC 2004-12-15 17:22:19 - 18:25:59lines 1933082 - 1934775, UTC 2005-03-14 00:51:03 - 02:01:01lines 2348843 - 2360410, UTC 2005-10-05 04:28:25 - 15:13:43lines 2459352 - 2461101, UTC 2005-11-19 07:41:28 - 08:56:01Data record 12, file C4_LBall_Bw_2025Mar28_133831.txtlines 974887 - 977372, UTC 2003-10-08 02:26:17 - 03:56:01lines 1517044 - 1519467, UTC 2004-08-28 07:46:36 - 09:26:01lines 1694641 - 1694884, UTC 2004-12-09 00:00:07 - 00:08:51lines 1705760 - 1707619, UTC 2004-12-15 17:05:17 - 18:26:02lines 1861098 - 1862725, UTC 2005-03-14 00:51:07 - 01:53:33lines 2267556 - 2279119, UTC 2005-10-05 04:41:05 - 15:26:58