Data Set Overview The following describes the nature of instrument operation during the various periods for which IES data are available in this submission. In all cases the data are ion and electron density, temperature, and velocity moments. Rosetta Comet Escort 1 (ESC 1) covers the period from 2014November20 to 2015March10. IES measured electrons generally below 500 eV. There are brief moments in which the electron detection is at all energies. IES electron detector observed ICA interference as a repetitive Morse code pattern. IES measured solar wind plasma continuously throughout this phase. IES detected solar wind alpha particles during most of this phase. Processing All Rosetta Plasma Consortium (RPC) data packets are transmitted together during downlinks with Rosetta. RPC data are retrieved from the Data Distribution System (DDS) at European Space Operations Centre (ESOC) to a central RPC data server at Imperial College London. Data for IES is copied from the RPC central data server by IESGS at Southwest Research Institute. The pipeline processing software is the IES Ground System (IESGS). IESGS extracts IES CCSDS packets from the RPC collective data files stored on the RPC central data server at Imperial College. These packets are used to build ion and electron data products. The data products are grouped by date and written out to PDS compliant archive data files. One data file is created for each day. IESGS also generates the labels for the archive data files. IES science products, archive and label files, and limited spectrograms are available to team scientists on the IESGS website. For information on how the derived data files are created from calibrated data files, please see DOCUMENTMOMENTS_CALCULATION MOMENTS_CALCULATION.PDF. Coordinate System In order to determine IES pointing, attitude data for the Rosetta spacecraft is obtained through SPICE kernel truncated!, Please see actual data for full tex [truncated!, Please see actual data for full text]

The International Energy System (IES) from EIA.gov has production, reserves, consumption, capacity, storage, imports, exports, and emissions time series by country for electricity, petroleum, natural gas, coal, nuclear, and renewable energy.

106 Global export shipment records of Ies Ethernet Switch with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Germany Income & Expenditure Survey (IES): Households Covered data was reported at 52,782.000 Unit in 2018. This records a decrease from the previous number of 53,490.000 Unit for 2013. Germany Income & Expenditure Survey (IES): Households Covered data is updated yearly, averaging 53,490.000 Unit from Dec 1998 (Median) to 2018, with 5 observations. The data reached an all-time high of 62,150.000 Unit in 1998 and a record low of 52,782.000 Unit in 2018. Germany Income & Expenditure Survey (IES): Households Covered data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.H023: Household Income and Expenditure Survey.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

PEELS is a study that is part of the Pre-Elementary Education Longitudinal Study. PEELS (https://ies.ed.gov/ncser/projects/peels/) is a longitudinal survey that is designed to describe the characteristics of children receiving preschool special education, their educational programs and services, and their transitions from preschool programs to elementary schools. The study was conducted using CATI, paper questionnaires, and child assessments. The study followed a nationally representative sample of children with disabilities who were 3 to 5 years old at the start of the study in 2003 through 2009, examining the achievement of students with disabilities in preschool, kindergarten, and elementary school and determining the factors associated with this achievement. Key statistics produced from PEELS are characteristics of children and their families; characteristics of educational services and providers; transitions from early intervention to preschool, and preschool to elementary school; and school-related readiness and behavior.

Germany IES: Projected Households data was reported at 40,683.000 Unit th in 2018. This records an increase from the previous number of 39,326.000 Unit th for 2013. Germany IES: Projected Households data is updated yearly, averaging 39,326.000 Unit th from Dec 1998 (Median) to 2018, with 5 observations. The data reached an all-time high of 40,683.000 Unit th in 2018 and a record low of 36,780.000 Unit th in 1998. Germany IES: Projected Households data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.H023: Household Income and Expenditure Survey.

Germany IES: AMHI: Property Revenue data was reported at 458.000 EUR in 2018. This records an increase from the previous number of 415.000 EUR for 2013. Germany IES: AMHI: Property Revenue data is updated yearly, averaging 399.000 EUR from Dec 1998 (Median) to 2018, with 5 observations. The data reached an all-time high of 458.000 EUR in 2018 and a record low of 385.000 EUR in 2008. Germany IES: AMHI: Property Revenue data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.H023: Household Income and Expenditure Survey.

Introduction

The 802.11 standard includes several management features and corresponding frame types. One of them are probe requests (PR). They are sent by mobile devices in the unassociated state to search the nearby area for existing wireless networks. The frame part of PRs consists of variable length fields called information elements (IE). IE fields represent the capabilities of a mobile device, such as data rates.
The dataset includes PRs collected in a controlled rural environment and in a semi-controlled indoor environment under different measurement scenarios.
It can be used for various use cases, e.g., analysing MAC randomization, determining the number of people in a given location at a given time or in different time periods, analysing trends in population movement (streets, shopping malls, etc.) in different time periods, etc.

Measurement setup

The system for collecting PRs consists of a Raspberry Pi 4 (RPi) with an additional WiFi dongle to capture Wi-Fi signal traffic in monitoring mode. Passive PR monitoring is performed by listening to 802.11 traffic and filtering out PR packets on a single WiFi channel.
The following information about each PR received is collected: MAC address, Supported data rates, extended supported rates, HT capabilities, extended capabilities, data under extended tag and vendor specific tag, interworking, VHT capabilities, RSSI, SSID and timestamp when PR was received.
The collected data was forwarded to a remote database via a secure VPN connection. A Python script was written using the Pyshark package for data collection, preprocessing and transmission.

Data preprocessing

The gateway collects PRs for each consecutive predefined scan interval (10 seconds). During this time interval, the data are preprocessed before being transmitted to the database.
For each detected PR in the scan interval, IEs fields are saved in the following JSON structure:
PR_IE_data =
{
'DATA_RTS': {'SUPP': DATA_supp , 'EXT': DATA_ext},
'HT_CAP': DATA_htcap,
'EXT_CAP': {'length': DATA_len, 'data': DATA_extcap},
'VHT_CAP': DATA_vhtcap,
'INTERWORKING': DATA_inter,
'EXT_TAG': {'ID_1': DATA_1_ext, 'ID_2': DATA_2_ext ...},
'VENDOR_SPEC': {VENDOR_1:{
'ID_1': DATA_1_vendor1,
'ID_2': DATA_2_vendor1
...},
VENDOR_2:{
'ID_1': DATA_1_vendor2,
'ID_2': DATA_2_vendor2
...}
...}
}

Supported data rates and extended supported rates are represented as arrays of values that encode information about the rates supported by a mobile device. The rest of the IEs data is represented in hexadecimal format. Vendor Specific Tag is structured differently than the other IEs. This field can contain multiple vendor IDs with multiple data IDs with corresponding data. Similarly, the extended tag can contain multiple data IDs with corresponding data.
Missing IE fields in the captured PR are not included in PR_IE_DATA.

When a new MAC address is detected in the current scan time interval, the data from PR is stored in the following structure:

{'MAC': MAC_address, 'SSIDs': [ SSID ], 'PROBE_REQs': [PR_data] },

where PR_data is structured as follows:
{
'TIME': [ DATA_time ],
'RSSI': [ DATA_rssi ],
'DATA': PR_IE_data
}.

This data structure allows storing only TOA and RSSI for all PRs originating from the same MAC address and containing the same PR_IE_data. All SSIDs from the same MAC address are also stored.
The data of the newly detected PR is compared with the already stored data of the same MAC in the current scan time interval.
If identical PR's IE data from the same MAC address is already stored, then only data for the keys TIME and RSSI are appended.
If no identical PR's IE data has yet been received from the same MAC address, then PR_data structure of the new PR for that MAC address is appended to PROBE_REQs key.
The preprocessing procedure is shown in Figure ./Figures/Preprocessing_procedure.png
At the end of each scan time interval, all processed data is sent to the database along with additional metadata about the collected data e.g. wireless gateway serial number and scan start and end timestamps. For an example of a single PR captured, see the ./Single_PR_capture_example.json file.

Environments description

We performed measurements in a controlled rural outdoor environment and in a semi-controlled indoor environment of the Jozef Stefan Institute.
See the Excel spreadsheet Measurement_informations.xlsx for a list of mobile devices tested.

Indoor environment

We used 3 RPi's for the acquisition of PRs in the Jozef Stefan Institute. They were placed indoors in the hallways as shown in the ./Figures/RPi_locations_JSI.png. Measurements were performed on weekend to minimize additional uncontrolled traffic from users' mobile devices. While there is some overlap in WiFi coverage between the devices at the location 2 and 3, the device at location 1 has no overlap with the other two devices.

Rural environment outdoors

The three RPi's used to collect PRs were placed at three different locations with non-overlapping WiFi coverage, as shown in ./Figures/RPi_locations_rural_env.png. Before starting the measurement campaign, all measured devices were turned off and the environment was checked for active WiFi devices. We did not detect any unknown active devices sending WiFi packets in the RPi's coverage area, so the deployment can be considered fully controlled.
All known WiFi enabled devices that were used to collect and send data to the database used a global MAC address, so they can be easily excluded in the preprocessing phase. MAC addresses of these devices can be found in the ./Measurement_informations.xlsx spreadsheet.
Note: The Huawei P20 device with ID 4.3 was not included in the test in this environment.

Scenarios description

We performed three different scenarios of measurements.

Individual device measurements

For each device, we collected PRs for one minute with the screen on, followed by PRs collected for one minute with the screen off. In the indoor environment the WiFi interfaces of the other devices not being tested were disabled. In rural environment other devices were turned off. Start and end timestamps of the recorded data for each device can be found in the ./Measurement_informations.xlsx spreadsheet under the Indoor environment of Jozef Stefan Institute sheet and the Rural environment sheet.

Three groups test

In this measurement scenario, the devices were divided into three groups. The first group contained devices from different manufacturers. The second group contained devices from only one manufacturer (Samsung). Half of the third group consisted of devices from the same manufacturer (Huawei), and the other half of devices from different manufacturers. The distribution of devices among the groups can be found in the ./Measurement_informations.xlsx spreadsheet.

The same data collection procedure was used for all three groups. Data for each group were collected in both environments at three different RPis locations, as shown in ./Figures/RPi_locations_JSI.png and ./Figures/RPi_locations_rural_env.png.
At each location, PRs were collected from each group for 10 minutes with the screen on. Then all three groups switched locations and the process was repeated. Thus, the dataset contains measurements from all three RPi locations of all three groups of devices in both measurement environments. The group movements and the timestamps for the start and end of the collection of PRs at each loacation can be found in spreadsheet ./Measurement_informations.xlsx.

One group test

In the last measurement scenario, all devices were grouped together. In rural evironement we first collected PRs for 10 minutes while the screen was on, and then for another 10 minutes while the screen was off. In indoor environment data were collected at first location with screens on for 10 minutes. Then all devices were moved to the location of the next RPi and PRs were collected for 5 minutes with the screen on and then for another 5 minutes with the screen off.

Folder structure

The root directory contains two files in JSON format for each of the environments where the measurements took place (Data_indoor_environment.json and Data_rural_environment.json). Both files contain collected PRs for the entire day that the measurements were taken (12:00 AM to 12:00 PM) to get a sense of the behaviour of the unknown devices in each environment. The spreadsheet ./Measurement_informations.xlsx. contains three sheets. Devices description contains general information about the tested devices, RPis, and the assigned group for each device. The sheets Indoor environment of Jozef Stefan Institute and Rural environment contain the corresponding timestamps for the start and end of each measurement scenario. For the scenario where the devices were divided into groups, additional information about the movements between locations is included. The location names are based on the RPi gateway ID and may differ from those on the figures showing the

Data of IES with the IEEE14-bus system and an 8-node gas system

Germany IES: AMHI: Compulsary Social Security Contributions data was reported at 642.000 EUR in 2018. This records an increase from the previous number of 526.000 EUR for 2013. Germany IES: AMHI: Compulsary Social Security Contributions data is updated yearly, averaging 377.000 EUR from Dec 1998 (Median) to 2018, with 5 observations. The data reached an all-time high of 642.000 EUR in 2018 and a record low of 328.000 EUR in 1998. Germany IES: AMHI: Compulsary Social Security Contributions data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.H023: Household Income and Expenditure Survey.

1667 Global import shipment records of Ies Ethernet Switch with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Germany IES: AMHI: Net Income data was reported at 3,661.000 EUR in 2018. This records an increase from the previous number of 3,132.000 EUR for 2013. Germany IES: AMHI: Net Income data is updated yearly, averaging 2,914.000 EUR from Dec 1998 (Median) to 2018, with 5 observations. The data reached an all-time high of 3,661.000 EUR in 2018 and a record low of 2,615.000 EUR in 1998. Germany IES: AMHI: Net Income data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.H023: Household Income and Expenditure Survey.

Introduction

The 802.11 standard includes several management features and corresponding frame types. One of them are Probe Requests (PR), which are sent by mobile devices in an unassociated state to scan the nearby area for existing wireless networks. The frame part of PRs consists of variable-length fields, called Information Elements (IE), which represent the capabilities of a mobile device, such as supported data rates.

This dataset contains PRs collected over a seven-day period by four gateway devices in an uncontrolled urban environment in the city of Catania.

It can be used for various use cases, e.g., analyzing MAC randomization, determining the number of people in a given location at a given time or in different time periods, analyzing trends in population movement (streets, shopping malls, etc.) in different time periods, etc.

Related dataset

Same authors also produced the Labeled dataset of IEEE 802.11 probe requests with same data layout and recording equipment.

Measurement setup

The system for collecting PRs consists of a Raspberry Pi 4 (RPi) with an additional WiFi dongle to capture WiFi signal traffic in monitoring mode (gateway device).
Passive PR monitoring is performed by listening to 802.11 traffic and filtering out PR packets on a single WiFi channel.

The following information about each received PR is collected:
- MAC address
- Supported data rates
- extended supported rates
- HT capabilities
- extended capabilities
- data under extended tag and vendor specific tag
- interworking
- VHT capabilities
- RSSI
- SSID
- timestamp when PR was received.

The collected data was forwarded to a remote database via a secure VPN connection.
A Python script was written using the Pyshark package to collect, preprocess, and transmit the data.

Data preprocessing

The gateway collects PRs for each successive predefined scan interval (10 seconds). During this interval, the data is preprocessed before being transmitted to the database.
For each detected PR in the scan interval, the IEs fields are saved in the following JSON structure:

PR_IE_data =
{
  'DATA_RTS': {'SUPP': DATA_supp , 'EXT': DATA_ext},
  'HT_CAP': DATA_htcap,
  'EXT_CAP': {'length': DATA_len, 'data': DATA_extcap},
  'VHT_CAP': DATA_vhtcap,
  'INTERWORKING': DATA_inter,
  'EXT_TAG': {'ID_1': DATA_1_ext, 'ID_2': DATA_2_ext ...},
  'VENDOR_SPEC': {VENDOR_1:{
                'ID_1': DATA_1_vendor1,
                'ID_2': DATA_2_vendor1
                ...},
          VENDOR_2:{
                'ID_1': DATA_1_vendor2,
                'ID_2': DATA_2_vendor2
                ...}
          ...}
}

Supported data rates and extended supported rates are represented as arrays of values that encode information about the rates supported by a mobile device. The rest of the IEs data is represented in hexadecimal format. Vendor Specific Tag is structured differently than the other IEs. This field can contain multiple vendor IDs with multiple data IDs with corresponding data. Similarly, the extended tag can contain multiple data IDs with corresponding data.
Missing IE fields in the captured PR are not included in PR_IE_DATA.

When a new MAC address is detected in the current scan time interval, the data from PR is stored in the following structure:

{'MAC': MAC_address, 'SSIDs': [ SSID ], 'PROBE_REQs': [PR_data] },

where PR_data is structured as follows:

{
  'TIME': [ DATA_time ],
  'RSSI': [ DATA_rssi ],
  'DATA': PR_IE_data
}.

This data structure allows to store only 'TOA' and 'RSSI' for all PRs originating from the same MAC address and containing the same 'PR_IE_data'. All SSIDs from the same MAC address are also stored.
The data of the newly detected PR is compared with the already stored data of the same MAC in the current scan time interval.
If identical PR's IE data from the same MAC address is already stored, only data for the keys 'TIME' and 'RSSI' are appended.
If identical PR's IE data from the same MAC address has not yet been received, then the PR_data structure of the new PR for that MAC address is appended to the 'PROBE_REQs' key.
The preprocessing procedure is shown in Figure ./Figures/Preprocessing_procedure.png

At the end of each scan time interval, all processed data is sent to the database along with additional metadata about the collected data, such as the serial number of the wireless gateway and the timestamps for the start and end of the scan. For an example of a single PR capture, see the Single_PR_capture_example.json file.

Folder structure

For ease of processing of the data, the dataset is divided into 7 folders, each containing a 24-hour period.
Each folder contains four files, each containing samples from that device.

The folders are named after the start and end time (in UTC).
For example, the folder [2022-09-22T22-00-00_2022-09-23T22-00-00](2022-09-22T22-00-00_2022-09-23T22-00-00) contains samples collected between 23th of September 2022 00:00 local time, until 24th of September 2022 00:00 local time.

Files representing their location via mapping:
- 1.json -> location 1
- 2.json -> location 2
- 3.json -> location 3
- 4.json -> location 4

Environments description

The measurements were carried out in the city of Catania, in Piazza Università and Piazza del Duomo
The gateway devices (rPIs with WiFi dongle) were set up and gathering data before the start time of this dataset.
As of September 23, 2022, the devices were placed in their final configuration and personally checked for correctness of installation and data status of the entire data collection system.
Devices were connected either to a nearby Ethernet outlet or via WiFi to the access point provided.

Four Raspbery Pi-s were used:
- location 1 -> Piazza del Duomo - Chierici building (balcony near Fontana dell’Amenano)
- location 2 -> southernmost window in the building of Via Etnea near Piazza del Duomo
- location 3 -> nothernmost window in the building of Via Etnea near Piazza Università
- location 4 -> first window top the right of the entrance of the University of Catania

Locations were suggested by the authors and adjusted during deployment based on physical constraints (locations of electrical outlets or internet access)
Under ideal circumstances, the locations of the devices and their coverage area would cover both squares and the part of Via Etna between them, with a partial overlap of signal detection. The locations of the gateways are shown in Figure ./Figures/catania.png.

Known dataset shortcomings

Due to technical and physical limitations, the dataset contains some identified deficiencies.

PRs are collected and transmitted in 10-second chunks.
Due to the limited capabilites of the recording devices, some time (in the range of seconds) may not be accounted for between chunks if the transmission of the previous packet took too long or an unexpected error occurred.

Every 20 minutes the service is restarted on the recording device.
This is a workaround for undefined behavior of the USB WiFi dongle, which can no longer respond.
For this reason, up to 20 seconds of data will not be recorded in each 20-minute period.

The devices had a scheduled reboot at 4:00 each day which is shown as missing data of up to a few minutes.

Location 1 - Piazza del Duomo - Chierici

The gateway device (rPi) is located on the second floor balcony and is hardwired to the Ethernet port. This device appears to function stably throughout the data collection period.
Its location is constant and is not disturbed, dataset seems to have complete coverage.

Location 2 - Via Etnea - Piazza del Duomo

The device is located inside the building.
During working hours (approximately 9:00-17:00), the device was placed on the windowsill. However, the movement of the device cannot be confirmed.
As the device was moved back and forth, power outages and internet connection issues occurred.
The last three days in the record contain no PRs from this location.

Location 3 - Via Etnea - Piazza Università

Similar to Location 2, the device is placed on the windowsill and moved around by people working in the building.
Similar behavior is also observed, e.g., it is placed on the windowsill and moved inside a thick wall when no people are present.
This device appears to have been collecting data throughout the whole dataset period.

Location 4 - Piazza Università

This location is wirelessly connected to the access point.
The device was placed statically on a windowsill overlooking the square.
Due to physical limitations, the device had lost power several times during the deployment.
The internet connection was also interrupted sporadically.

Recognitions

The data was collected within the scope of Resiloc project with the help of City of Catania and project partners.

Germany IES: AMHE: Recreation & Culture data was reported at 304.000 EUR in 2018. This records an increase from the previous number of 261.000 EUR for 2013. Germany IES: AMHE: Recreation & Culture data is updated yearly, averaging 261.000 EUR from Dec 1998 (Median) to 2018, with 5 observations. The data reached an all-time high of 304.000 EUR in 2018 and a record low of 247.000 EUR in 1998. Germany IES: AMHE: Recreation & Culture data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.H023: Household Income and Expenditure Survey.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Data Set Overview The following describes the nature of instrument operation during the various periods for which IES data are available in this submission. In all cases the data are derived ion and electron moments. Rosetta Comet Escort 2 (ESC 2) covers the period from 2015March11 to 2015June30. IES measured electrons generally below 500 eV. There are brief moments in which the electron detection is at all energies. IES electron detector observed ICA interference as a repetitive Morse code pattern. IES measured solar wind plasma continuously throughout this phase except for the occasional periods in which S/C pointing was not optimal. There are periods in this phase when we are unable to separate solar wind protons from the pickedup ions. During these intervals, no solar wind proton moments are reported. IES detected solar wind alpha particles during this phase. There are some intervals where proton moments are not reported, but alpha moments are reported. This is due to the fact that the alpha population is at higher energies than picked up ions, which are mixed with the proton population. Throughout this phase, there are intervals with strong cometary pick up activity and energization. Processing All Rosetta Plasma Consortium (RPC) data packets are transmitted together during downlinks with Rosetta. RPC data are retrieved from the Data Distribution System (DDS) at European Space Operations Centre (ESOC) to a central RPC data server at Imperial College London. Data for IES is copied from the RPC central data server by IESGS at Southwest Research Institute. The pipeline processing software is the IES Ground System (IESGS). IESGS extracts IES CCSDS packets from the RPC collective data files stored on the RPC central data server at Imperial College. These packets are used to build ion and electron data products. The data products are grouped by date and written out to PDS compliant ar truncated!, Please see actual d [truncated!, Please see actual data for full text]

Data Set Overview The following describes the nature of instrument operation during the various periods for which IES data are available in this submission. In all cases the data are calibrated differential electron and ion energy flux as function of energy, azimuth (direction in the instrument symmetry plane) and ^ation (angle above or below the symmetry plane). Rosetta Extension 3 (EXT3) covers the period from 2016July01 to 2016September30. IES observed solar wind plasma continuously throughout this phase. IES also detected heavier solar wind ions, alphas and singly charged Helium populations. IES detected negative ions from the once solar wind protons population. IES observed solar wind plasma continuously throughout this phase. IES also detected heavier solar wind ions, alphas and singly charged Helium populations. IES detected negative ions from the once solar wind protons population. Processing All Rosetta Plasma Consortium (RPC) data packets are transmitted together during downlinks with Rosetta. RPC data are retrieved from the Data Distribution System (DDS) at European Space Operations Centre (ESOC) to a central RPC data server at Imperial College London. Data for IES is copied from the RPC central data server by IESGS at Southwest Research Institute. The pipeline processing software is the IES Ground System (IESGS). IESGS extracts IES CCSDS packets from the RPC collective data files stored on the RPC central data server at Imperial College. These packets are used to build ion and electron data products. The data products are grouped by date and written out to PDS compliant archive data files. One data file is created for each day. IESGS also generates the labels for the archive data files. IES science products, archive and label files, and limited spectrograms are available to team scientists on the IESGS website. For information on how the derived data files are created from truncated!, Please see actual [truncated!, Please see actual data for full text]

Scientists working within the Hawaiian Ocean Time-series (HOT) project, https://hahana.soest.hawaii.edu/hot/, have been making repeated observations of the hydrography, chemistry and biology at a station north of Hawaii since October 1988. The objective of this research is to provide a comprehensive description of the ocean at a site representative of the central North Pacific Ocean. Cruises are made approximately once a month to Station ALOHA, the HOT deep-water station (22 45'N, 158W) located about 100 km north of Oahu, Hawaii. Measurements of the thermohaline structure, water column chemistry, currents, primary production and particle sedimentation rates are made over a 72-hour period on each cruise.

        The IES data was proccessed using methods explained in:
        1991 Fields, E., K. Tracey, D.R. Watts. Inverted Echo Sounder Data
        Processing Report, GSO Technical Report No. 91-3, Graduate School of
        Oceanography,University of Rhode Island, 150pp.

        More information of this dataset can be found on the WWW at the
        following URLs:
        https://hahana.soest.hawaii.edu/hot/      (HOT home page)


        The continuous flow of these data depends on funding, and that depends
        in part on the credits that we get from the data users. If
        you use these data in your project, please contact Dr. Roger Lukas to
        avoid possible duplication of efforts. Please consider the
        benefits of possible scientific collaborations in the analysis of
        these data. If you use our data in your project, we would appreciate your
        acknowledgement of the HOT project.The following NSF grant number
        should be cited in publications: OCE-9303094. A preprint and reprint
        of the publications utilizing HOT data would be appreciated.

Germany IES: AMHI: Non Public Transfer Payments & Income from Subleasing data was reported at 272.000 EUR in 2018. This records an increase from the previous number of 198.000 EUR for 2013. Germany IES: AMHI: Non Public Transfer Payments & Income from Subleasing data is updated yearly, averaging 185.000 EUR from Dec 1998 (Median) to 2018, with 5 observations. The data reached an all-time high of 272.000 EUR in 2018 and a record low of 140.000 EUR in 1998. Germany IES: AMHI: Non Public Transfer Payments & Income from Subleasing data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.H023: Household Income and Expenditure Survey.