Abstract

In 2001, the World Bank in co-operation with the Republika Srpska Institute of Statistics (RSIS), the Federal Institute of Statistics (FOS) and the Agency for Statistics of BiH (BHAS), carried out a Living Standards Measurement Survey (LSMS). The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows:

1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs.

2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household.

3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analyzed data.

The Department for International Development, UK (DFID) contributed funding to the LSMS and provided funding for a further two years of data collection for a panel survey, known as the Household Survey Panel Series (HSPS). Birks Sinclair & Associates Ltd. were responsible for the management of the HSPS with technical advice and support provided by the Institute for Social and Economic Research (ISER), University of Essex, UK. The panel survey provides longitudinal data through re-interviewing approximately half the LSMS respondents for two years following the LSMS, in the autumn of 2002 and 2003. The LSMS constitutes Wave 1 of the panel survey so there are three years of panel data available for analysis. For the purposes of this documentation we are using the following convention to describe the different rounds of the panel survey: - Wave 1 LSMS conducted in 2001 forms the baseline survey for the panel
- Wave 2 Second interview of 50% of LSMS respondents in Autumn/ Winter 2002 - Wave 3 Third interview with sub-sample respondents in Autumn/ Winter 2003

The panel data allows the analysis of key transitions and events over this period such as labour market or geographical mobility and observe the consequent outcomes for the well-being of individuals and households in the survey. The panel data provides information on income and labour market dynamics within FBiH and RS. A key policy area is developing strategies for the reduction of poverty within FBiH and RS. The panel will provide information on the extent to which continuous poverty is experienced by different types of households and individuals over the three year period. And most importantly, the co-variates associated with moves into and out of poverty and the relative risks of poverty for different people can be assessed. As such, the panel aims to provide data, which will inform the policy debates within FBiH and RS at a time of social reform and rapid change.

Geographic coverage

National coverage. Domains: Urban/rural/mixed; Federation; Republic

Kind of data

Sample survey data [ssd]

Sampling procedure

The Wave 3 sample consisted of 2878 households who had been interviewed at Wave 2 and a further 73 households who were interviewed at Wave 1 but were non-contact at Wave 2 were issued. A total of 2951 households (1301 in the RS and 1650 in FBiH) were issued for Wave 3. As at Wave 2, the sample could not be replaced with any other households.

Panel design

Eligibility for inclusion

The household and household membership definitions are the same standard definitions as a Wave 2. While the sample membership status and eligibility for interview are as follows: i) All members of households interviewed at Wave 2 have been designated as original sample members (OSMs). OSMs include children within households even if they are too young for interview. ii) Any new members joining a household containing at least one OSM, are eligible for inclusion and are designated as new sample members (NSMs). iii) At each wave, all OSMs and NSMs are eligible for inclusion, apart from those who move outof-scope (see discussion below). iv) All household members aged 15 or over are eligible for interview, including OSMs and NSMs.

Following rules

The panel design means that sample members who move from their previous wave address must be traced and followed to their new address for interview. In some cases the whole household will move together but in others an individual member may move away from their previous wave household and form a new split-off household of their own. All sample members, OSMs and NSMs, are followed at each wave and an interview attempted. This method has the benefit of maintaining the maximum number of respondents within the panel and being relatively straightforward to implement in the field.

Definition of 'out-of-scope'

It is important to maintain movers within the sample to maintain sample sizes and reduce attrition and also for substantive research on patterns of geographical mobility and migration. The rules for determining when a respondent is 'out-of-scope' are as follows:

i. Movers out of the country altogether i.e. outside FBiH and RS. This category of mover is clear. Sample members moving to another country outside FBiH and RS will be out-of-scope for that year of the survey and not eligible for interview.

ii. Movers between entities Respondents moving between entities are followed for interview. The personal details of the respondent are passed between the statistical institutes and a new interviewer assigned in that entity.

iii. Movers into institutions Although institutional addresses were not included in the original LSMS sample, Wave 3 individuals who have subsequently moved into some institutions are followed. The definitions for which institutions are included are found in the Supervisor Instructions.

iv. Movers into the district of Brcko are followed for interview. When coding entity Brcko is treated as the entity from which the household who moved into Brcko originated.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaire design

Approximately 90% of the questionnaire (Annex B) is based on the Wave 2 questionnaire, carrying forward core measures that are needed to measure change over time. The questionnaire was widely circulated and changes were made as a result of comments received.

Pretesting

In order to undertake a longitudinal test the Wave 2 pretest sample was used. The Control Forms and Advance letters were generated from an Access database containing details of ten households in Sarajevo and fourteen in Banja Luka. The pretest was undertaken from March 24-April 4 and resulted in 24 households (51 individuals) successfully interviewed. One mover household was successfully traced and interviewed.
In order to test the questionnaire under the hardest circumstances a briefing was not held. A list of the main questionnaire changes was given to experienced interviewers.

Issues arising from the pretest

Interviewers were asked to complete a Debriefing and Rating form. The debriefing form captured opinions on the following three issues:

1. General reaction to being re-interviewed. In some cases there was a wariness of being asked to participate again, some individuals asking “Why Me?” Interviewers did a good job of persuading people to take part, only one household refused and another asked to be removed from the sample next year. Having the same interviewer return to the same households was considered an advantage. Most respondents asked what was the benefit to them of taking part in the survey. This aspect was reemphasised in the Advance Letter, Respondent Report and training of the Wave 3 interviewers.

2. Length of the questionnaire. The average time of interview was 30 minutes. No problems were mentioned in relation to the timing, though interviewers noted that some respondents, particularly the elderly, tended to wonder off the point and that control was needed to bring them back to the questions in the questionnaire. One interviewer noted that the economic situation of many respondents seems to have got worse from the previous year and it was necessary to listen to respondents “stories” during the interview.

3. Confidentiality. No problems were mentioned in relation to confidentiality. Though interviewers mentioned it might be worth mentioning the new Statistics Law in the Advance letter. The Rating Form asked for details of specific questions that were unclear. These are described below with a description of the changes made.

• Module 3. Q29-31 have been added to capture funds received for education, scholarships etc.

• Module 4. Pretest respondents complained that the 6 questions on "Has your health limited you..." and the 16 on "in the last 7 days have you felt depressed” etc were too many. These were reduced by half (Q38-Q48). The LSMS data was examined and those questions where variability between the answers was widest were chosen.

• Module 5. The new employment questions (Q42-Q44) worked well and have been kept in the main questionnaire.

• Module 7. There were no problems reported with adding the credit questions (Q28-Q36)

• Module 9. SIG recommended that some of Questions 1-12 were relevant only to those aged over 18 so additional skips have been added. Some respondents complained the questionnaire was boring. To try and overcome

HTMLmetadata Text files in HTML-format containing metadata about samples and spectra. Also included in the zip file are folders containing information linked to from the HTML files, including: - README: contains a HTML version of the USGS Data Series publication, linked to this data release, that describes this spectral library (Kokaly and others, 2017). The folder also contains an HTML version of the release notes. - photo_images: contains full resolution images of photos of samples and field sites. - photo_thumbs: contains low-resolution thumbnail versions of photos of samples and field sites. GENERAL LIBRARY DESCRIPTION This data release provides the U.S. Geological Survey (USGS) Spectral Library Version 7 and all related documents. The library contains spectra measured with laboratory, field, and airborne spectrometers. The instruments used cover wavelengths from the ultraviolet to the far infrared (0.2 to 200 microns). Laboratory samples of specific minerals, plants, chemical compounds, and man-made materials were measured. In many cases, samples were purified, so that unique spectral features of a material can be related to its chemical structure. These spectro-chemical links are important for interpreting remotely sensed data collected in the field or from an aircraft or spacecraft. This library also contains physically-constructed as well as mathematically-computed mixtures. Measurements of rocks, soils, and natural mixtures of minerals have also been made with laboratory and field spectrometers. Spectra of plant components and vegetation plots, comprising many plant types and species with varying backgrounds, are also in this library. Measurements by airborne spectrometers are included for forested vegetation plots, in which the trees are too tall for measurement by a field spectrometer. The related U.S. Geological Survey Data Series publication, "USGS Spectral Library Version 7", describes the instruments used, metadata descriptions of spectra and samples, and possible artifacts in the spectral measurements (Kokaly and others, 2017). Four different spectrometer types were used to measure spectra in the library: (1) Beckman™ 5270 covering the spectral range 0.2 to 3 µm, (2) standard, high resolution (hi-res), and high-resolution Next Generation (hi-resNG) models of ASD field portable spectrometers covering the range from 0.35 to 2.5 µm, (3) Nicolet™ Fourier Transform Infra-Red (FTIR) interferometer spectrometers covering the range from about 1.12 to 216 µm, and (4) the NASA Airborne Visible/Infra-Red Imaging Spectrometer AVIRIS, covering the range 0.37 to 2.5 µm. Two fundamental spectrometer characteristics significant for interpreting and utilizing spectral measurements are sampling position (the wavelength position of each spectrometer channel) and bandpass (a parameter describing the wavelength interval over which each channel in a spectrometer is sensitive). Bandpass is typically reported as the Full Width at Half Maximum (FWHM) response at each channel (in wavelength units, for example nm or micron). The linked publication (Kokaly and others, 2017), includes a comparison plot of the various spectrometers used to measure the data in this release. Data for the sampling positions and the bandpass values (for each channel in the spectrometers) are included in this data release. These data are in the SPECPR files, as separate data records, and in the American Standard Code for Information Interchange (ASCII) text files, as separate files for wavelength and bandpass. Spectra are provided in files of ASCII text format (files with a .txt file extension). In the ASCII files, deleted channels (bad bands) are indicated by a value of -1.23e34. Metadata descriptions of samples, field areas, spectral measurements, and results from supporting material analyses – such as XRD – are provided in HyperText Markup Language HTML formatted ASCII text files (files with .html file extension). In addition, Graphics Interchange Format (GIF) images of plots of spectra are provided. For each spectrum a plot with wavelength in microns on the x-axis is provided. For spectra measured on the Nicolet spectrometer, an additional GIF image with wavenumber on the x-axis is provided. Data are also provided in SPECtrum Processing Routines (SPECPR) format (Clark, 1993) which packages spectra and associated metadata descriptions into a single file (see the linked publication, Kokaly and others, 2017, for additional details on the SPECPR format and freely-available software than can be used to read files in SPECPR format). The data measured on the source spectrometers are denoted by the “splib07a” tag in filenames. In addition to providing the original measurements, the spectra have been convolved and resampled to different spectrometer and multispectral sensor characteristics. The following list specifies the identifying tag for the measured and convolved libraries and gives brief descriptions of the sensors. splib07a – this is the name of the SPECPR file containing the spectra measured on the Beckman, ASD, Nicolet and AVIRIS spectrometers. The data are provided with their original sampling positions (wavelengths) and bandpass values. The prefix “splib07a_” is at the beginning of the ASCII and GIF files pertaining to the measured spectra. splib07b – this is the name of the SPECPR file containing a modified version of the original measurements. The results from using spectral convolution to convert measurements to other spectrometer characteristics can be improved by oversampling (increasing sample density). Thus, splib07b is an oversampled version of the library, computed using simple cubic-spline interpolation to produce spectra with fine sampling interval (therefore a higher number of channels) for Beckman and AVIRIS measurements. The spectra in this version of the library are the data used to create the convolved and resampled versions of the library. The prefix “splib07b_” is at the beginning of the ASCII and GIF files pertaining to the oversampled spectra. s07_ASD – this is the name of the SPECPR file containing the spectral library measurements convolved to standard resolution ASD full range spectrometer characteristics. The standard reported wavelengths of the ASD spectrometers used by the USGS were used (2151 channels with wavelength positions starting at 350 nm and increasing in 1 nm increments). The bandpass values of each channel were determined by comparing measurements of reference materials made on ASD spectrometers in comparison to measurements made of the same materials on higher resolution spectrometers (the procedure is described in Kokaly, 2011, and discussed in Kokaly and Skidmore, 2015, and Kokaly and others, 2017). The prefix “s07ASD_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV95 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1995 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV95_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV96 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1996 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV96_” is at the beginning of the ASCII, and GIF files. s07_AV97 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1997 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV97_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV98 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1998 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV98_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV99 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1999 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV99_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV00 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2000 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV00_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV01 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2001 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV01_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV05 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2005 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV05_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV06 – this is the name of the SPECPR file containing the spectral library measurements convolved to

Let me know who is your pension scheme administrator – is it outsourced? If so to whom? Question 2 Please tell me what data you use to monitor the performance of your retirement processing. In particular I expect to see end-to-end process lead times, error rates, staffing levels and rates for both incoming complaints and complaint resolution. If you set targets for these items, please send those too. Question 3 Please send me the results of these data items over the last 2 years in a format that allows trend analysis (i.e. to assess I there are trend improvements or deteriorations). Question 4 Your board minutes of August 23 mention a “Quality Assurance KPI” which was surprisingly exceeded through out the quarter. Please send me the last 2 yrs results for this KPI and the target against which it is assessed. It is also surprising that the KPI is referred to in the singular. Please send me the same data on any other KPIs which you use to assess process performance, either generally or in the retirement process in particular. Question 5 I suspect that the overall performance of your management is poor, and would like to see any data you have on process performance over the last 2 years (so I can see trends over time). This should include elapse times for the retirement process, error rates, staffing levels and numbers or rates of complaints. This is an official request under the Freedom of information act 2000. Question 6 I would like a copy of your management structure chart so I know who is responsible for what. In particular who do the Trustees hold accountable for the performance of your Retirement processes at a senior level. This is also an official request under the Freedom of information act 2000. Your request was received on 19 April 2023, and I am dealing with it under the terms of the Freedom of Information Act 2000. Questions 5 and 6 were from FOI-01728 which was merged with FOI-01749 on 22 February 2024. Clarification relating to questions 2 to 5 was received on 6 March, and I have summarised this below: Question 2 Both please [formal and informal complaints], shown separately. Also data on how many complaints end up with the Ombudsman would be useful. Again all results should be shown categorised by time (whether weekly or monthly) so I can see trends over time. This should not be too onerous as your management should be interested in this and monitoring it routinely. Question 3 As a minimum you can send me quarterly data. I'm just interested in the processing of initial retirement claims (i.e. following the submission of an AW8) not routine monthly payments. Question 4 I'm just interested in the processing of initial retirement claims (i.e. following the submission of an AW8) not routine monthly payments. Question 5 Specifically what I want to see (and what your management should monitor) is: a) Average elapse time from the receipt of a first AW8 to the successful closure of a retirement claim (i.e. after all errors have been reworked). The average could be collated daily, weekly or monthly but should be frequent enough to show time trends so your management can identify if things are getting better or worse. This is called an end-to-end process metric and will best reflect your customers' experience. I am not generally interested in things like initial response times to communications (e.g. holding letters) or compliance against service standards unless they reflect customer experience. b) Average rates of error in processing could be measured in a variety of ways depending on your MI systems. So for example you may measure time spent by staff on activities and categorise it into say "basic work" and "rework". Or you may have error rates, say on calculations i.e. "how often did we need to calculate retirement benefits until we got it right?" I'm conscious a lot of rework is also inflicted on you by errors in the contribution record from employers, so I'd be interested in any metrics you have on the errors you find in the contribution record and what the impact is on your workloads; again time trend data would be best to demonstrate if it's getting better or worse. c) Metrics on staffing levels should be easy - something to show if you have enough staff (e.g. unfulfilled vacancies over time) and if they are overworked (e.g. overtime records, staff sickness, staff satisfaction surveys)

There are two different views on the relationship between forward-looking disclosure (FLD) and mispricing of firm value: one view is that FLD increases the disclosure of private firm information and improves mispricing; the other view is that FLD disclosure is management-adjusted, too much FLD distorts true information and worsens mispricing. This paper examines the relationship between FLD and mispricing using a sample of 15,113 Chinese A-share listed companies. Our findings support that FLD increases transparency and mitigates mispricing. There is a serious endogeneity problem between FLD and mispricing, and our results remain robust to the instrumental variables approach. We find some evidence on an transparency mechanism between FLD and mispricing. Further analysis shows that the effect of FLD on mispricing varies across external environments. For firms with less competitive external product markets and firms with lower marketization processes, more FLD is expected to be more favorable for disclosure.

This data release provides the U.S. Geological Survey (USGS) Spectral Library Version 7 and all related documents. The library contains spectra measured with laboratory, field, and airborne spectrometers. The instruments used cover wavelengths from the ultraviolet to the far infrared (0.2 to 200 microns). Laboratory samples of specific minerals, plants, chemical compounds, and man-made materials were measured. In many cases, samples were purified, so that unique spectral features of a material can be related to its chemical structure. These spectro-chemical links are important for interpreting remotely sensed data collected in the field or from an aircraft or spacecraft. This library also contains physically-constructed as well as mathematically-computed mixtures. Measurements of rocks, soils, and natural mixtures of minerals have also been made with laboratory and field spectrometers. Spectra of plant components and vegetation plots, comprising many plant types and species with varying backgrounds, are also in this library. Measurements by airborne spectrometers are included for forested vegetation plots, in which the trees are too tall for measurement by a field spectrometer. The related U.S. Geological Survey Data Series publication, "USGS Spectral Library Version 7", describes the instruments used, metadata descriptions of spectra and samples, and possible artifacts in the spectral measurements (Kokaly and others, 2017). Four different spectrometer types were used to measure spectra in the library: (1) Beckman™ 5270 covering the spectral range 0.2 to 3 µm, (2) standard, high resolution (hi-res), and high-resolution Next Generation (hi-resNG) models of ASD field portable spectrometers covering the range from 0.35 to 2.5 µm, (3) Nicolet™ Fourier Transform Infra-Red (FTIR) interferometer spectrometers covering the range from about 1.12 to 216 µm, and (4) the NASA Airborne Visible/Infra-Red Imaging Spectrometer AVIRIS, covering the range 0.37 to 2.5 µm. Two fundamental spectrometer characteristics significant for interpreting and utilizing spectral measurements are sampling position (the wavelength position of each spectrometer channel) and bandpass (a parameter describing the wavelength interval over which each channel in a spectrometer is sensitive). Bandpass is typically reported as the Full Width at Half Maximum (FWHM) response at each channel (in wavelength units, for example nm or micron). The linked publication (Kokaly and others, 2017), includes a comparison plot of the various spectrometers used to measure the data in this release. Data for the sampling positions and the bandpass values (for each channel in the spectrometers) are included in this data release. These data are in the SPECPR files, as separate data records, and in the American Standard Code for Information Interchange (ASCII) text files, as separate files for wavelength and bandpass. Spectra are provided in files of ASCII text format (files with a .txt file extension). In the ASCII files, deleted channels (bad bands) are indicated by a value of -1.23e34. Metadata descriptions of samples, field areas, spectral measurements, and results from supporting material analyses – such as XRD – are provided in HyperText Markup Language HTML formatted ASCII text files (files with .html file extension). In addition, Graphics Interchange Format (GIF) images of plots of spectra are provided. For each spectrum a plot with wavelength in microns on the x-axis is provided. For spectra measured on the Nicolet spectrometer, an additional GIF image with wavenumber on the x-axis is provided. Data are also provided in SPECtrum Processing Routines (SPECPR) format (Clark, 1993) which packages spectra and associated metadata descriptions into a single file (see the linked publication, Kokaly and others, 2017, for additional details on the SPECPR format and freely-available software than can be used to read files in SPECPR format). The data measured on the source spectrometers are denoted by the “splib07a” tag in filenames. In addition to providing the original measurements, the spectra have been convolved and resampled to different spectrometer and multispectral sensor characteristics. The following list specifies the identifying tag for the measured and convolved libraries and gives brief descriptions of the sensors. splib07a – this is the name of the SPECPR file containing the spectra measured on the Beckman, ASD, Nicolet and AVIRIS spectrometers. The data are provided with their original sampling positions (wavelengths) and bandpass values. The prefix “splib07a_” is at the beginning of the ASCII and GIF files pertaining to the measured spectra. splib07b – this is the name of the SPECPR file containing a modified version of the original measurements. The results from using spectral convolution to convert measurements to other spectrometer characteristics can be improved by oversampling (increasing sample density). Thus, splib07b is an oversampled version of the library, computed using simple cubic-spline interpolation to produce spectra with fine sampling interval (therefore a higher number of channels) for Beckman and AVIRIS measurements. The spectra in this version of the library are the data used to create the convolved and resampled versions of the library. The prefix “splib07b_” is at the beginning of the ASCII and GIF files pertaining to the oversampled spectra. s07_ASD – this is the name of the SPECPR file containing the spectral library measurements convolved to standard resolution ASD full range spectrometer characteristics. The standard reported wavelengths of the ASD spectrometers used by the USGS were used (2151 channels with wavelength positions starting at 350 nm and increasing in 1 nm increments). The bandpass values of each channel were determined by comparing measurements of reference materials made on ASD spectrometers in comparison to measurements made of the same materials on higher resolution spectrometers (the procedure is described in Kokaly, 2011, and discussed in Kokaly and Skidmore, 2015, and Kokaly and others, 2017). The prefix “s07ASD_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV95 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1995 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV95_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV96 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1996 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV96_” is at the beginning of the ASCII, and GIF files. s07_AV97 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1997 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV97_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV98 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1998 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV98_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV99 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1999 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV99_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV00 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2000 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV00_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV01 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2001 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV01_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV05 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2005 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV05_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV06 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2006 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV06_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV09 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2009 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV09_” is at the beginning of the ASCII and GIF files pertaining to this

ASCIIdata Files containing spectral data in ASCII text format: - measured spectra (ASCIIdata_splib07a.zip), including wavelength positions and bandpass (Full-Width at Half-Maximum; FWHM) values of channels in the spectrometers utilized - spectra interpolated to a higher number of more finely-spaced channels (ASCIIdata_splib07b.zip) - spectra convolved to other spectrometers, including the wavelength positions and bandpass (FWHM) values of the channels in the spectrometers, for example * Analytical Spectral Devices standard resolution (ASCIIdata_splib07b_cvASD.zip) * AVIRIS-Classic 2014 characteristics (ASCIIdata_splib07b_cvAVIRISc2014.zip) * Hyperspectral Mapper 2014 characteristics (ASCIIdata_splib07b_cvHYMAP2014.zip) * and others - spectra resampled to multispectral sensors, including channel wavelength positions and sensor response functions of the sensors, for example: * ASTER (ASCIIdata_splib07b_rsASTER.zip) * and others NOTE: within each zip file the ASCII data files are organized in chapter sub-folders: - ChapterM_Minerals - ChapterS_SoilsAndMixtures - ChapterC_Coatings - ChapterL_Liquids - ChapterO_OrganicCompounds - ChapterA_ArtificialMaterials - ChapterV_Vegetation GENERAL LIBRARY DESCRIPTION This data release provides the U.S. Geological Survey (USGS) Spectral Library Version 7 and all related documents. The library contains spectra measured with laboratory, field, and airborne spectrometers. The instruments used cover wavelengths from the ultraviolet to the far infrared (0.2 to 200 microns). Laboratory samples of specific minerals, plants, chemical compounds, and man-made materials were measured. In many cases, samples were purified, so that unique spectral features of a material can be related to its chemical structure. These spectro-chemical links are important for interpreting remotely sensed data collected in the field or from an aircraft or spacecraft. This library also contains physically-constructed as well as mathematically-computed mixtures. Measurements of rocks, soils, and natural mixtures of minerals have also been made with laboratory and field spectrometers. Spectra of plant components and vegetation plots, comprising many plant types and species with varying backgrounds, are also in this library. Measurements by airborne spectrometers are included for forested vegetation plots, in which the trees are too tall for measurement by a field spectrometer. The related U.S. Geological Survey Data Series publication, "USGS Spectral Library Version 7", describes the instruments used, metadata descriptions of spectra and samples, and possible artifacts in the spectral measurements (Kokaly and others, 2017). Four different spectrometer types were used to measure spectra in the library: (1) Beckman™ 5270 covering the spectral range 0.2 to 3 µm, (2) standard, high resolution (hi-res), and high-resolution Next Generation (hi-resNG) models of ASD field portable spectrometers covering the range from 0.35 to 2.5 µm, (3) Nicolet™ Fourier Transform Infra-Red (FTIR) interferometer spectrometers covering the range from about 1.12 to 216 µm, and (4) the NASA Airborne Visible/Infra-Red Imaging Spectrometer AVIRIS, covering the range 0.37 to 2.5 µm. Two fundamental spectrometer characteristics significant for interpreting and utilizing spectral measurements are sampling position (the wavelength position of each spectrometer channel) and bandpass (a parameter describing the wavelength interval over which each channel in a spectrometer is sensitive). Bandpass is typically reported as the Full Width at Half Maximum (FWHM) response at each channel (in wavelength units, for example nm or micron). The linked publication (Kokaly and others, 2017), includes a comparison plot of the various spectrometers used to measure the data in this release. Data for the sampling positions and the bandpass values (for each channel in the spectrometers) are included in this data release. These data are in the SPECPR files, as separate data records, and in the American Standard Code for Information Interchange (ASCII) text files, as separate files for wavelength and bandpass. Spectra are provided in files of ASCII text format (files with a .txt file extension). In the ASCII files, deleted channels (bad bands) are indicated by a value of -1.23e34. Metadata descriptions of samples, field areas, spectral measurements, and results from supporting material analyses – such as XRD – are provided in HyperText Markup Language HTML formatted ASCII text files (files with .html file extension). In addition, Graphics Interchange Format (GIF) images of plots of spectra are provided. For each spectrum a plot with wavelength in microns on the x-axis is provided. For spectra measured on the Nicolet spectrometer, an additional GIF image with wavenumber on the x-axis is provided. Data are also provided in SPECtrum Processing Routines (SPECPR) format (Clark, 1993) which packages spectra and associated metadata descriptions into a single file (see the linked publication, Kokaly and others, 2017, for additional details on the SPECPR format and freely-available software than can be used to read files in SPECPR format). The data measured on the source spectrometers are denoted by the “splib07a” tag in filenames. In addition to providing the original measurements, the spectra have been convolved and resampled to different spectrometer and multispectral sensor characteristics. The following list specifies the identifying tag for the measured and convolved libraries and gives brief descriptions of the sensors. splib07a – this is the name of the SPECPR file containing the spectra measured on the Beckman, ASD, Nicolet and AVIRIS spectrometers. The data are provided with their original sampling positions (wavelengths) and bandpass values. The prefix “splib07a_” is at the beginning of the ASCII and GIF files pertaining to the measured spectra. splib07b – this is the name of the SPECPR file containing a modified version of the original measurements. The results from using spectral convolution to convert measurements to other spectrometer characteristics can be improved by oversampling (increasing sample density). Thus, splib07b is an oversampled version of the library, computed using simple cubic-spline interpolation to produce spectra with fine sampling interval (therefore a higher number of channels) for Beckman and AVIRIS measurements. The spectra in this version of the library are the data used to create the convolved and resampled versions of the library. The prefix “splib07b_” is at the beginning of the ASCII and GIF files pertaining to the oversampled spectra. s07_ASD – this is the name of the SPECPR file containing the spectral library measurements convolved to standard resolution ASD full range spectrometer characteristics. The standard reported wavelengths of the ASD spectrometers used by the USGS were used (2151 channels with wavelength positions starting at 350 nm and increasing in 1 nm increments). The bandpass values of each channel were determined by comparing measurements of reference materials made on ASD spectrometers in comparison to measurements made of the same materials on higher resolution spectrometers (the procedure is described in Kokaly, 2011, and discussed in Kokaly and Skidmore, 2015, and Kokaly and others, 2017). The prefix “s07ASD_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV95 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1995 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV95_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV96 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1996 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV96_” is at the beginning of the ASCII, and GIF files. s07_AV97 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1997 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV97_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV98 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1998 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV98_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV99 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1999 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV99_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV00 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2000 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV00_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV01 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2001 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

Indonesia is now composed of 26 provinces, which are divided into 3 regions. The region criteria have been based on the accessibility of health facilities and the total health budget provided to each province. Papua, Aceh and Maluku Province were excluded from the sampling frame due to political reforms and economic crisis. From the remaining 24 provinces, 10 provinces were sampled in 3 regions. The selection was carried out by the assistance of Central Bureau of Statistic (CBS) and used the PPS (Probability Proportionate to Size) technique. The following provinces were selected: Region 1: DKI Jakarta, West Java, Central Java, East Java Region 2: North Sumatra , South Sulawesi, South Sumatra Region 3: West Nusa Tenggara, Central Kalimantan, South East Sulawesi The total sample was 10,000. More females (54.8%) than males (45.2%) were interviewed.

Due to budget and time limitation (holidays between last week of November 2000 to second week of January 2001, such as Ramadhan, Christmas, New Year, Chinese New Year) the implementation phase was divided into two period of time, before the holidays and after the holidays. Additional problems experienced included: absence of respondent from home; length of questionnaire and culturally-sensitive questions, that wasted time (and even inhuman e.g. PLM) or that were difficult and misunderstood; bad political situation (e.g. ethnic conflict, president impeachment process, increase in crimes, etc.), which almost jeopardized the implementation of the household survey in several provinces; bad economic conditions, heavy rain, flood, and land slides; geographic inaccessibility; inadequate transportation and communication; survey instrument and budget delays from the WHO. In addition, Indonesia had server problems as their server depended on the Naval American Research Unit, which was adversely affected by the deterioration in its relations with the USA.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Population mortality forecasts are widely used for allocating public health expenditures, setting research priorities, and evaluating the viability of public pensions, private pensions, and health care financing systems. In part because existing methods seem to forecast worse when based on more information, most forecasts are still based on simple linear extrapolations that ignore known biological risk factors and other prior information. We adapt a Bayesian hierarchical forecasting model capable of including more known health and demographic information than has previously been possible. This leads to the first age- and sex-specific forecasts of American mortality that simultaneously incorporate, in a formal statistical model, the effects of the recent rapid increase in obesity, the steady decline in tobacco consumption, and the well known patterns of smooth mortality age profiles and time trends. Formally including new information in forecasts can matter a great deal. For example, we estimate an increase in male life expectancy at birth from 76.2 years in 2010 to 79.9 years in 2030, which is 1.8 years greater than the U.S. Social Security Administration projection and 1.5 years more than U.S. Census projection. For females, we estimate more modest gains in life expectancy at birth over the next twenty years from 80.5 years to 81.9 years, which is virtually identical to the Social Security Administration projection and 2.0 years less than U.S. Census projections. We show that these patterns are also likely to greatly affect the aging American population structure. We offer an easy-to-use approach so that researchers can include other sources of information and potentially improve on our forecasts too. Website See also: Mortality Studies

Schooling fish are an impressive group-level coordination in which multiple individualsmove together in a seamless way. This is possible because each individual in the groupresponds to the movement of other group members. But how many individuals doeseach fish pay attention to? Which are the influential neighbors? We need to answerthese questions if we want to understand how directional information propagates acrossa group. Our research shows that in the rummy-nose tetras species there is a limitednumber of influential neighbors which are not necessarily the closest ones.The data set here contains all the data used in our research. There are three compressed zip files for experimental data of group of 2 fish, experimental data of group of 5 fish and null model data of group of 5 fish, respectively.To use the data so as to interpret our research, let's take the group of 2 fish for an example.First, unzip the file "UturnPositionData_2fish.zip" into a folder, then you'll find 1133 RData files. Which can group into 3 categories: position_uturns_fishNo_2_uturn_i.RData with i=1,2,...,1135; uturn_info_fishNo=2.Rdata and uturn_bad_fishNo=2.Rdata.Second, now you can load the data with software R.You'll find that all the data are in dataframe. The position_uturns_fishNo_2_uturn_i.RData has the trajectory information around the U-turn events, while the uturn_info_fishNo=2.Rdata and uturn_bad_fishNo=2.Rdata contain the beginning and ending time information for the good U-turn events and the U-turn event IDs for the bad ones, respectively. The bad U-turn events are those with too much errors from the video tracking procedure thus we omitted in our analysis.Third, then you can follow the methods in our manuscript and reproduce our results.

Inflation is generally defined as the continued increase in the average prices of goods and services in a given region. Following the extremely high global inflation experienced in the 1980s and 1990s, global inflation has been relatively stable since the turn of the millennium, usually hovering between three and five percent per year. There was a sharp increase in 2008 due to the global financial crisis now known as the Great Recession, but inflation was fairly stable throughout the 2010s, before the current inflation crisis began in 2021. Recent years Despite the economic impact of the coronavirus pandemic, the global inflation rate fell to 3.26 percent in the pandemic's first year, before rising to 4.66 percent in 2021. This increase came as the impact of supply chain delays began to take more of an effect on consumer prices, before the Russia-Ukraine war exacerbated this further. A series of compounding issues such as rising energy and food prices, fiscal instability in the wake of the pandemic, and consumer insecurity have created a new global recession, and global inflation in 2024 is estimated to have reached 5.76 percent. This is the highest annual increase in inflation since 1996. Venezuela Venezuela is the country with the highest individual inflation rate in the world, forecast at around 200 percent in 2022. While this is figure is over 100 times larger than the global average in most years, it actually marks a decrease in Venezuela's inflation rate, which had peaked at over 65,000 percent in 2018. Between 2016 and 2021, Venezuela experienced hyperinflation due to the government's excessive spending and printing of money in an attempt to curve its already-high inflation rate, and the wave of migrants that left the country resulted in one of the largest refugee crises in recent years. In addition to its economic problems, political instability and foreign sanctions pose further long-term problems for Venezuela. While hyperinflation may be coming to an end, it remains to be seen how much of an impact this will have on the economy, how living standards will change, and how many refugees may return in the coming years.

The Dow Jones Industrial Average (DJIA) index dropped around ***** points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at ********* points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over ****** points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than ***** points in the week from February 21 to February 28, which was a fall of **** percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

Aineisto eMielipide 2019 käsittää kahdeksalla kyselykierroksella kerättyjä mielipidemittauksia ja eduskuntavaaleihin liittyviä näkemyksiä toistuvasti samoilta vastaajilta. Vuoden 2019 eduskuntavaalien ja europarlamenttivaalien aikaan kerätyssä aineistossa tarkastellaan vastaajien näkemyksiä politiikasta ja näkemyksien muuttumista niin vaaleja lähestyttäessä kuin vaalien jälkeenkin. Tehdyllä kyselyllä on pyritty hahmottamaan kansalaisten poliittista osallistumista ja äänestyskäyttäytymistä. Kyselyiden pohjalta luotiin myös Suomen täysi-ikäistä väestöä edustava verkkopaneeli nimeltä Kansalaismielipide. Verkkopaneeli on osa kansallisen yleisen mielipiteen mittaamisen infrastruktuuria (FIRIPO). Alussa kartoitettiin vastaajan äänestyskäyttäytymistä, eli aikooko vastaaja äänestää, ja jos aikoo, niin mitä puoluetta on suunnitellut äänestävänsä. Kysyttiin, saako vastaaja tietoa vaaleista esimerkiksi television, radion, lehtien vai Internetin kautta, ja miten usein vastaaja seuraa eri medioita. Osallistujilta kysyttiin poliittisista asenteista ja mielipiteistä sekä luottamuksesta erilaisia poliittisia instituutioita kohtaan. Lisäksi osallistujilta kysyttiin luottamuksesta kuvitteellisiin kansanedustajiin, mikäli media olisi uutisoinut heistä epäsuotuisalla tavalla. Taustamuuttujia aineistossa ovat vastaajan ikä, sukupuoli, koulutustaso, elämäntilanne, äidinkieli, vaalialue ja maakunta. The data cover public opinion on Finnish parliamentary elections and European parliamentary elections held in 2019, collected in eight separate collection rounds from the same respondents. The respondents' views on politics and changes in these views were charted both before and after the elections. The survey investigated political participation and voting behaviour in Finland. The surveys were also used to create an online survey panel called Citizens' Opinion that represented the adult population of Finland. The panel is part of the Finnish Research Infrastructure for Public Opinion (FIRIPO). In the first collection round, the respondents were asked about their interest in politics and how they had been following the 2019 parliamentary elections (e.g. TV programs, newspapers, online voting advice applications, social media). The respondents' views on whether following the election campaign had improved their understanding of politics and whether the differences between the political parties had become clearer during the election campaign were investigated. Past voting behaviour was examined by asking whether in the 2015 parliamentary elections and which party they had voted for then. Further questions charted whether the respondents were going to vote in the 2019 parliamentary elections and which party they considered voting for. Opinions on several political parties in Finland were also surveyed. The second round of data collection charted whether the respondents had voted in advance in the 2019 parliamentary elections and if so, which party's candidate they had voted for. Those who had not voted in advance were asked which party's candidate they intended to vote for. Those who indicated that they would not be voting at all in the parliamentary elections were asked for their reasons for doing so (e.g. 'I am not interested in politics and I don't care about voting', 'I had trouble finding a suitable party or candidate for myself', 'I did not have enough information to make a voting decision'). Additionally, the respondents were asked how much they trusted, for example, the Finnish Government, the Finnish President, and the European Union. The third round included questions on whether the respondents had already voted or intended to vote in the 2019 parliamentary elections and which party's candidate they had voted for or intended to vote for. Those who indicated that they would not be voting at all in the parliamentary elections were asked for their reasons for doing so (e.g. 'I am not interested in politics and I don't care about voting', 'I had trouble finding a suitable party or candidate for myself', 'I did not have enough information to make a voting decision'). In the fourth collection round, the respondents were asked how satisfied they were with the results of the 2019 parliamentary elections and which parties they thought should form the new coalition government. Views on current political issues were charted with a series of statements (e.g. 'Immigration is mostly a good thing for Finland', 'Finland should be much more active in the fight against climate change', 'Russia is a security threat to Finland', 'public services must be cut to balance the Finnish economy'). Additionally, the respondents were asked to what extent they had used various forms of media (e.g. online voting advice applications, election debates and interviews with party leaders on TV) to follow the parliamentary elections and to what extent the information they received from various sources (e.g. TV, friends and acquaintances, the social media pages of political parties or their candidates) had impacted their voting decision. The respondents' views on whether following the election campaign had improved their understanding of politics and whether the differences between the political parties had become clearer during the election campaign were also investigated. The fifth collection round surveyed how important the respondents considered various topics in politics (e.g. taxation, minority rights, European cooperation and EU, education) to be. The respondents' interest and intentions to vote in the 2019 European parliamentary elections were investigated. Those who indicated that they would vote in the European parliamentary elections were asked which party's candidate they would vote for. Internet use and social media use (e.g. Instagram, Facebook) were examined, and opinions on the political parties in the Finnish parliament were charted. Satisfaction with democracy in Finland in general and at the municipal level, as well as satisfaction with democracy in the EU were investigated. In the sixth round, the respondents were asked how satisfied they were with the likely government composition following the 2019 parliamentary elections. Views on the EU were charted with a series of questions. The respondents were, for example, asked how they would vote in the event of a referendum on Finland's EU membership, whether the British people had made the right choice leaving the EU, whether Brexit had been good or bad for the EU, whether Finland had benefited from EU membership, and whether the respondents were for or against the formation of an EU army in the future. A series of questions with hypothetical situations (e.g. 'Imagine that the media has revealed that a male MP of the National Coalition Party has strong ties to a neo-Nazi organisation in Helsinki. On a scale of zero to ten, to what extent do you trust this politician?') was used to investigate the respondents' attitudes in response to male or female members of different political parties being involved in scandals such as tax evasion, having an extramarital affair or having far-right ties. The seventh round examined political attitudes with a series of statements (e.g. 'Finnish Members of Parliament are competent at their jobs', 'I trust my own abilities to take part in politics', 'By voting ordinary people can have an impact on political decision-making'). Trust in other people in general and trust in specific groups of people (e.g. Russians, Estonians, Somalis, Germans) were studied and the respondents were asked whether Islam is suitable in the context of Finnish culture and democratic tradition. Opinions on the political parties in the Finnish parliament were also investigated. In addition, the respondents were asked the so-called solidarity tax, where those who earn more than 76,100 euros per year pay an extra 2 percent of income tax, should be removed. The eighth collection round charted how satisfied the respondents were with the results of the European parliamentary elections and which party's candidate they had voted for. Those who had not voted in the European parliamentary elections were asked for their reasons for doing so (e.g. 'I am not interested in politics and I don't care about voting', 'I had trouble finding a suitable party or candidate for myself', 'I did not have enough information to make a voting decision', 'EU elections are not important to me'). The respondents' trust in, for example, the Finnish Government, the Finnish President, and the European Union was examined. The respondents' trust in politicians was also surveyed with several questions such as whether politicians exaggerate the effects of climate change, whether politicians' approach to immigration is too lax or too strict, and whether political decision making on the national level is well thought out or more or less haphazard. Background variables included the respondent's age, gender, highest level of education, economic activity and occupational status, mother tongue, electoral district, and NUTS3 region of residence.