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. KIND OF DATA

Geographic coverage

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

Analysis unit

Households

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]

Cleaning operations

Data entry

As at Wave 2 CSPro was the chosen data entry software. The CSPro program consists of two main features to reduce to number of keying errors and to reduce the editing required following data entry: - Data entry screens that included all skip patterns. - Range checks for each question (allowing three exceptions for inappropriate, don't know and missing codes). The Wave 3 data entry program had more checks than at Wave 2 and DE staff were instructed to get all anomalies cleared by SIG fieldwork. The program was extensively tested prior to DE. Ten computer staff were employed in each Field Office and as all had worked on Wave 2 training was not undertaken.

Editing

Editing Instructions were compiled (Annex G) and sent to Supervisors. For Wave 3 Supervisors were asked to take more time to edit every questionnaire returned by their interviewers. The FBTSA examined the work twelve of the twenty-two Supervisors. All Supervisors made occasional errors with the Control Form so a further 100% check of Control Forms and Module 1 was undertaken by the FBTSA and SIG members.

Response rate

The panel survey has enjoyed high response rates throughout the three years of data collection with the wave 3 response rates being slightly higher than those achieved at wave 2. At wave 3, 1650 households in the FBiH and 1300 households in the RS were issued for interview. Since there may be new households created from split-off movers it is possible for the number of households to increase during fieldwork. A similar number of new households were formed in each entity; 62 in the FBiH and 63 in the RS. This means that 3073 households were identified during fieldwork. Of these, 3003 were eligible for interview, 70 households having either moved out of BiH, institutionalised or deceased (34 in the RS and 36 in the FBiH).

Interviews were achieved in 96% of eligible households, an extremely high response rate by international standards for a survey of this type.

In total, 8712 individuals (including children) were enumerated within the sample households (4796 in the FBiH and 3916 in the RS). Within in the 3003 eligible households, 7781 individuals aged 15 or over were eligible for interview with 7346 (94.4%) being successfully interviewed. Within cooperating households (where there was at least one interview) the interview rate was higher (98.8%).

A very important measure in longitudinal surveys is the annual individual re-interview rate. This is because a high attrition rate, where large numbers of respondents drop out of the survey over time, can call into question the quality of the data collected. In BiH the individual re-interview rates have been high for the survey. The individual re-interview rate is the proportion of people who gave an interview at time t-1 who also give an interview at t. Of those who gave a full interview at wave 2, 6653 also gave a full interview at wave 3. This represents a re-interview rate of 97.9% - which is extremely high by international standards. When we look at those respondents who have been interviewed at all three years of the survey there are 6409 cases which are available for longitudinal analysis, 2881 in the RS and 3528 in the FBiH. This represents 82.8% of the responding wave 1 sample, a

In the present paper, we present operational definitions of the physical quantities distance, mass and time. Attempts to formulate a well-based operational definition of physical entities were first proposed in the end of the 19-th century by Hermann von Helmholtz and Otto Hölder. The problem consists in finding how to associate a number (the value of the entity) with a non-numerical entity (the entity itself). The paper presents a modern reading of the above mentioned authors. Paraphrasing Feynman in his classic work on path integrals, “there is a pleasure in recognizing old things from a new point of view”.

Acoustic Doppler current profiler (ADCP) discharge measurement data were collected and analyzed for use in developing an operational uncertainty analysis tool known as QUant (Moore and others, 2016). These ADCP measurements were originally collected in the United States, Canada, and New Zealand as a part of research conducted to validate ADCP discharge measurements made with Teledyne RD Instruments RiverRay and SonTek M9 ADCPs (Boldt and Oberg, 2015). The data were chosen in order to represent a variety of geographic and streamflow conditions, such as mean depth and mean velocity. Due to current limitations in the QUant software, only measurements collected using Teledyne RD Instruments Rio Grande and StreamPro ADCPs were used. All measurements were collected and processed with WinRiver II (Teledyne RD Instruments, 2016). An appropriate method for estimation of flow near the water surface and the streambed was obtained by means of the extrap software (Mueller, 2013). The extrapolation method and parameters obtained with extrap were entered into WinRiver II and reprocessed before use in QUant. Due to the complexity of an ADCP data file and the various algorithms applied to compute the streamflow from ADCP data, these data are most useful in their original raw data format which can be opened and processed in either WinRiver II, which is available without cost at: http://www.teledynemarine.com/rdi/support#. Each measurement consists of: (1) .mmt file; an xml configuration file used by WinRiver II for instrument setup, specific measurement data entry, and filenames of the raw transect data files (.pd0). (2) .pd0 files; the raw binary data collected by WinRiver II. The format for these files is defined in Teledyne RD Instruments (2016). (3) .txt files; raw ASCII data from external sensors such as GPS receivers. These data are not used in WinRiver II nor for the present analyses. (4) *_extrap.txt file; a file that summarizes the method and parameters selected for estimation of near-surface and near-bed discharges. (5) WinRiver.pdf files; a file that provides a summary of the discharge measurement in pdf format. References Boldt, J. A., and Oberg, K. A., 2016, Validation of streamflow measurements made with M9 and RiverRay Acoustic Doppler current profilers: Journal of Hydraulic Engineering, v. 142, no. 2. [Also available at https://doi.org/10.1061/(asce)hy.1943-7900.0001087.] Moore, S.A., Jamieson, E. C., Rainville, F., Rennie, C.D.,and& Mueller, D.S., 2017, Monte Carlo approach for uncertainty analysis of Acoustic Doppler current profiler discharge measurement by moving boat: Journal of Hydraulic Engineering: v. 143 no. 3. [Also available at https://doi.org/10.1061/(asce)hy.1943-7900.0001249.] Mueller, D.S., 2013, extrap: Software to assist the selection of extrapolation methods for moving-boat ADCP streamflow measurements: Computers & Geosciences, v. 54, p. 211–218. [Also available at https://doi.org/10.1016/j.cageo.2013.02.001.] Teledyne RD Instruments, Inc., 2016, WinRiver II Software User’s Guide, P/N 957-6231-00, San Diego, CA, 310 p.

Abstract: This paper presents some results of a study that valued manipulative conditions in the process of knowledge construction by handling a sixteenth-century mathematical instrument. The study was based on a problem-situation elaborated by epistemological and mathematical questions, which emerged from an interface built between the history of mathematics and teaching. The handling of this instrument triggered a series of actions that led teachers to reflect and discuss the very notion of magnitude, number and measurement. The results of the study suggest an epistemological gap between the observer who measures, the instrument that mediates the measuring, and the measured object. This gap compromises the proper understanding of measuring and the relationship between number and magnitude in measurement process.

Context

List of images with the preparation of pipes for welding

Content

Problem definition: measuring the distance between the pipes on the photo. The measurement must be carried out along the entire length of the pipes being welded.

This study evaluates the reliability of osteometric data commonly used in forensic case analyses, with specific reference to the measurements in Data Collection Procedures 2.0 (DCP 2.0). Four observers took a set of 99 measurements four times on a sample of 50 skeletons (each measurement was taken 200 times by each observer). Two-way mixed ANOVAs and repeated measures ANOVAs with pairwise comparisons were used to examine interobserver (between-subjects) and intraobserver (within-subjects) variability. Relative technical error of measurement (TEM) was calculated for measurements with significant ANOVA results to examine the error among a single observer repeating a measurement multiple times (e.g. repeatability or intraobserver error), as well as the variability between multiple observers (interobserver error). Two general trends emerged from these analyses: (1) maximum lengths and breadths have the lowest error across the board (TEM < 0.5), and (2) maximum and minimum diameters at midshaft are more reliable than their positionally-dependent counterparts (i.e. sagittal, vertical, transverse, dorso-volar). Therefore, maxima and minima are specified for all midshaft measurements in DCP 2.0. Twenty-two measurements were flagged for excessive variability (either interobserver, intraobserver, or both); 15 of these measurements were part of the standard set of measurements in Data Collection Procedures for Forensic Skeletal Material, 3rd edition. Each measurement was examined carefully to determine the likely source of the error (e.g. data input, instrumentation, observer’s method, or measurement definition). For several measurements (e.g. anterior sacral breadth, distal epiphyseal breadth of the tibia) only one observer differed significantly from the remaining observers, indicating a likely problem with the measurement definition as interpreted by that observer; these definitions were clarified in DCP 2.0 to eliminate this confusion. Other measurements were taken from landmarks that are difficult to locate consistently (e.g. pubis length, ischium length); these measurements were omitted from DCP 2.0. This manual is available for free download online (https://fac.utk.edu/wp-content/uploads/2016/03/DCP20_webversion.pdf), along with an accompanying instructional video (https://www.youtube.com/watch?v=BtkLFl3vim4). Observer experience also played a role in the ability to consistently reproduce measurements. Average intraobserver relative TEM values of the measurements in Table 3 from lowest to highest were 2.31 (Observer 2), 3.25 (Observer 1), 3.36 (Observer 3), and 3.41 (Observer 4). Observer 2 had the lowest TEM for most measurements, and Observer 4 had the highest TEM most frequently. While Observer 1 had the most experience in number of years (27 years), Observer 2 had more technical training than any other observers. Observer 2 had 14 years of experience, but had measured approximately 900 skeletons (more than any other observer) during this time.

Three streamflow measurements are used to demonstrate the use of equations developed in Mueller (in review). All three measurements are from various locations on the Mississippi River. These data were not collected for the purpose of this paper but provide practical examples of the effect of heading errors. The use of data from the Mississippi River allows the collection of 500 or more ensembles in each transect which reduce the overall effect of random errors that could complicate the identification of effects due to heading errors. In addition, by using wide cross sections, the effect of GPS errors due to vegetation near the boundaries of the river are minimized. All measurements were collected with WinRiver II (Teledyne RD Instruments, 2016) and processed with QRev (Mueller, 2016). These three data sets represent three different situations: 1) availability of heading data from a GPS compass (Mississippi River near Hickman, KY), 2) transects intentionally collected at different speeds (Mississippi River near Vicksburg, MS), and 3) GPS data collected where there is minimal influence from a moving bed (Mississippi River near Clinton, IA). All data were collected using Teledyne RD Instruments Rio Grande ADCPs. All data were collected with Teledyne RD Instrument WinRiver II (Teledyne RD Instruments, 2016) and processed with QRev version 3.43 (Mueller, 2016). Due to the complexity of an ADCP data file and the various algorithms applied to compute the streamflow from ADCP data, these data are most useful in either 1) their original raw data format which can be opened and processed in either WinRiver II or QRev or 2) their processed format that can be opened and processed by QRev or opened by Matlab or any software that can read Matlab formatted files. Both WinRiver II and QRev are distributed free. WinRiver II can be obtained from: http://www.teledynemarine.com/rdi/support# QRev can be obtained from: https://hydroacoustics.usgs.gov/movingboat/QRev.shtml Each measurement consists of: 1) .mmt file is an xml configuration file used by WinRiver II for setup, specific measurement data entry, and filenames of the raw transect data files (pd0) 2) .pd0 files are the raw binary data collected by WinRiver II. The format for these files is defined in Teledyne RD Instruments (2016). 3) .txt files contain raw ASCII data from external sensors such as GPS receivers. These data are not used by WinRiver II or QRev but provide the raw external data strings sent by the GPS receiver. 4) .mat files are the saved data processed by QRev. These files can be opened and processed by QRev or loaded into Matlab or software that can read Matlab formatted files. The variable definitions are documented in Mueller (2016). 5) *.xml are summaries of the data processed by QRev. The variable definitions are documented in Mueller (2016).

Data accompanying the article Exploring Definitions of Quality and Diversity in Sonic Measurement Spaces The Innovation Engine algorithm is used to evolve sounds, where Quality Diversity search is guided by Behaviour Definitions by unsupervised models and full-reference and no-reference quality evaluation approaches. Sonic discoveries have shaped and transformed creative processes in sound art and music production. Compositions prompted by new timbres influence and improve our lives. Modern technology offers a vast space of sonic possibilities to explore. Background and expertise influence the explorers ability to navigate that space of possibilities. Efforts have been made to develop automated systems that can systematically generate and explore these sonic possibilities. One route of such efforts has involved the search for diversity and quality with evolutionary algorithms, automating the evaluation of those metrics with supervised models. We continue on that path of investigation by further exploring possible definitions of quality and diversity in sonic measurement spaces by applying and dynamically redefining unsupervised models to autonomously illuminate sonic search spaces. In particular we investigate the applicability of unsupervised dimensionality reduction models for defining dynamically expanding, structured containers for a quality diversity search algorithm to operate within. Furthermore we evaluate different approaches for defining sonic characteristics with different feature extraction approaches. Results demonstrate considerable ability in autonomously discovering a diversity of sounds, as well as limitations of simulating evolution within the confines of a single, structured, albeit dynamically redefined, search landscape. Sound objects discovered in traversals through such autonomously illuminated sonic spaces can serve as resources in shaping our lives and steering them through diverse creative paths, along which stepping stones towards interesting innovations can be collected and used as input to human culture.

Abstract This article presents the characterization of the mathematical and pedagogical knowledge regarding the measure estimation concept possessed by the Chilean primary school teachers based on the way they propose to utilize activities aimed to work on this concept. In the analysis, we use a definition of the measurement estimation concept constructed from the previous work of different authors. The methodology is of a descriptive and interpretative nature. The results shows weaknesses in the teachers knowledge about the measurement estimate and its use in the classroom, noting that some teachers confuse the measurement estimation activities indistinctly with the measurement activities or interpreting that they are those activities in which a random response can be given without justification, evidencing the need to include measurement estimation in teacher training.

Data description

This dataset is dedicated to benchmarking Machine Learning solutions to the problem of estimation of the components of the state vector in nonlinear dynamical systems.

The dataset is built using two dynamical systems, namely:

• The Electronic Throttle Controlled (ETC) system representing a technological device that controls the air flow rate in automotive motors. This is a three-states system in which only the first state and the control input are measured while the other two states are to be estimated using the previous available measurements. The system is controlled via an input signal (which is also measured) representing the electric current that acts on an electric torque generation sub-system. This torque changes acts on the angle of a device hence changing the flow-rate entering the combustion chamber.

• The Lorentz attractor representing a famous nonlinear chaotic system with no inputs (autonomous system). Here again, this is a three-states system in which only the first state is measured while the two remaining states are to be estimated using the available measurements over a past window.

Some definitions and notation to understand the context

The state vector and the control input (if any) are denoted by x and u respectively. Both systems are defined up to the knowledge of an associated vector of parameters p involved in the model's definition.

The very possibility of estimating the non measured components xi of the states, such as x2 and x3 in the data set of both systems relies on the existence of an associated maps of the form:

xi(k) = Fi(y_past(k), p)

where y_past encompasses the measurement acquired on some past moving window spanning the past time interval defined by:

(k-window, ..., k-1, k).

More precisely, the vector of features (used in the X features matrix) is built from the values of the measurements over the previously defined time interval with some under-sampling consisting in taking one value over nJump values. Namely when nJump=1 all the measurements are used while when nJump=5 only the fifth of the instantes are considered.

Based on the precious definitions, the features vector and the label to be identified are schematically shown in the figure below.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F9193311%2F595a8b464752a4e0eb31d431580b489b%2FCapture%20decran%202024-11-26%20a%2013.07.52.png?generation=1732623079125879&alt=media" alt="Description">

This is the main file containing the dictionary of the dataset the can be used as a benchmark for nonlinear state estimators design via Machine Learning.

The file contains a dictionary that can be acceded by using the pickle.load command:

import pickle 
data = pickle.load(open('data.pkl', 'rb')

The list of keys of the data dictionary is the following:

[('etc', 0.0, 'x2'),
 ('etc', 0.0, 'x3'),
 ('etc', 0.05, 'x2'),
 ('etc', 0.05, 'x3'),
 ('etc', 0.1, 'x2'),
 ('etc', 0.1, 'x3'),
 ('lorentz', 0.0, 'x2'),
 ('lorentz', 0.0, 'x3'),
 ('lorentz', 0.05, 'x2'),
 ('lorentz', 0.05, 'x3'),
 ('lorentz', 0.1, 'x2'),
 ('lorentz', 0.1, 'x3')]

Where each key is a triplet of values representing

• The system being considered: Possible values art etc or lorentz
• The relative standard deviation of the system's parameters: Possible values are 0, 0.05 or 0.1
• The state component to be estimated: Possible values are x2 or x3.

Notice that the noise level can be chosen and the corresponding noise added to the features matrices.

Once a key k is chosen among the above mentioned list, the corresponding value data[k] is again a dictionary enabling to access the (X,y) paires for training and test, namely:

data[k].Xtrain, data[k].Xtest, data[k].ytrain, data[k].ytest

Finally, in order to grasp an idea regarding the size of the datasets, the following script is used:

print(data[('etc', 0.0, 'x2')]['Xtrain'].shape)
print(data[('etc', 0.0, 'x2')]['Xtest'].shape)
print(data[('lorentz', 0.0, 'x2')]['Xtrain'].shape)
print(data[('lorentz', 0.0, 'x2')]['Xtest'].shape)

which produces the following results:

(136000, 30) (136000, 30) (44000, 5) (44000, 5)

CERN-LHC. Observables sensitive to the anomalous production of events containing hadronic jets and missing momentum in the plane transverse to the proton beams at the Large Hadron Collider are presented. The observables are defined as a ratio of cross sections, for events containing jets and large missing transverse momentum to events containing jets and a pair of charged leptons from the decay of a $Z/\gamma^\ast$ boson. This definition minimises experimental and theoretical systematic uncertainties in the measurements. This ratio is measured differentially with respect to a number of kinematic properties of the hadronic system in two phase-space regions; one inclusive single-jet region and one region sensitive to vector-boson-fusion topologies. The data are found to be in agreement with the Standard Model predictions and used to constrain a variety of theoretical models for dark-matter production, including simplified models, effective field theory models, and invisible decays of the Higgs boson. The measurements use 3.2 fb${}^{-1}$ of proton-proton collision data recorded by the ATLAS experiment at a centre-of-mass energy of 13 TeV and are fully corrected for detector effects, meaning that the data can be used to constrain new-physics models beyond those shown in this paper.

Numerator and denominator ($\geq 1$ jet): - $p_\text{T}^\text{miss} > 200$ GeV - no additional electron or muon with $p_\text{T}$(lepton)>7 GeV and |$\eta$(lepton)|<2.5 - |$y$(jet)|<4.5 and $p_\text{T}$(jet)>25 GeV - $\Delta\phi$(jet,$p_\text{T}^\text{miss}$) > 0.4 for the four leading jets with $p_\text{T}$(jet)>30 GeV - leading $p_\text{T}$(jet)>120 GeV and |$\eta$(jet)|<2.4

Numerator and denominator (VBF): - $p_\text{T}^\text{miss} > 200$ GeV - no additional electron or muon with $p_\text{T}$(lepton)>7 GeV and |$\eta$(lepton)|<2.5 - |$y$(jet)|<4.5 and $p_\text{T}$(jet)>25 GeV - $\Delta\phi$(jet,$p_\text{T}^\text{miss}$) > 0.4 for the four leading jets with $p_\text{T}$(jet)>30 GeV - leading pT(jet)>80 GeV and subleading pT(jet)>50 GeV - $m_\text{jj}$>200 GeV - no additional jets with $p_\text{T}$(jet)>25 GeV inside rapidity interval

Denominator only ($\geq 1$ jet and VBF): - leading $p_\text{T}$(lepton)>80 GeV and |$\eta$(lepton)|<2.5 - subleading $p_\text{T}$(lepton)>7 GeV and |$\eta$(lepton)|<2.5 - 66 GeV $< m_{\ell\ell}<$ 116 GeV - $\Delta R$(jet,lepton)>0.5, otherwise jet is removed

Abstract

In 1992, Bosnia-Herzegovina, one of the six republics in former Yugoslavia, became an independent nation. A civil war started soon thereafter, lasting until 1995 and causing widespread destruction and losses of lives. Following the Dayton accord, BosniaHerzegovina (BiH) emerged as an independent state comprised of two entities, namely, the Federation of Bosnia-Herzegovina (FBiH) and the Republika Srpska (RS), and the district of Brcko. In addition to the destruction caused to the physical infrastructure, there was considerable social disruption and decline in living standards for a large section of the population. Along side these events, a period of economic transition to a market economy was occurring. The distributive impacts of this transition, both positive and negative, are unknown. In short, while it is clear that welfare levels have changed, there is very little information on poverty and social indicators on which to base policies and programs.

In the post-war process of rebuilding the economic and social base of the country, the government has faced the problems created by having little relevant data at the household level. The three statistical organizations in the country (State Agency for Statistics for BiH –BHAS, the RS Institute of Statistics-RSIS, and the FBiH Institute of Statistics-FIS) have been active in working to improve the data available to policy makers: both at the macro and the household level. One facet of their activities is to design and implement a series of household series. The first of these surveys is the Living Standards Measurement Study survey (LSMS). Later surveys will include the Household Budget Survey (an Income and Expenditure Survey) and a Labor Force Survey. A subset of the LSMS households will be re-interviewed in the two years following the LSMS to create a panel data set.

The three statistical organizations began work on the design of the Living Standards Measurement Study Survey (LSMS) in 1999. The purpose of the survey was to collect data needed for assessing the living standards of the population and for providing the key indicators needed for social and economic policy formulation. The survey was to provide data at the country and the entity level and to allow valid comparisons between entities to be made.

The LSMS survey was carried out in the Fall of 2001 by the three statistical organizations with financial and technical support from the Department for International Development of the British Government (DfID), United Nations Development Program (UNDP), the Japanese Government, and the World Bank (WB). The creation of a Master Sample for the survey was supported by the Swedish Government through SIDA, the European Commission, the Department for International Development of the British Government and the World Bank.

The overall management of the project was carried out by the Steering Board, comprised of the Directors of the RS and FBiH Statistical Institutes, the Management Board of the State Agency for Statistics and representatives from DfID, UNDP and the WB. The day-to-day project activities were carried out by the Survey Mangement Team, made up of two professionals from each of the three statistical organizations.

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.

Geographic coverage

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

Kind of data

Sample survey data [ssd]

Sampling procedure

A total sample of 5,400 households was determined to be adequate for the needs of the survey: with 2,400 in the Republika Srpska and 3,000 in the Federation of BiH. The difficulty was in selecting a probability sample that would be representative of the country's population. The sample design for any survey depends upon the availability of information on the universe of households and individuals in the country. Usually this comes from a census or administrative records. In the case of BiH the most recent census was done in 1991. The data from this census were rendered obsolete due to both the simple passage of time but, more importantly, due to the massive population displacements that occurred during the war.

At the initial stages of this project it was decided that a master sample should be constructed. Experts from Statistics Sweden developed the plan for the master sample and provided the procedures for its construction. From this master sample, the households for the LSMS were selected.

Master Sample [This section is based on Peter Lynn's note "LSMS Sample Design and Weighting - Summary". April, 2002. Essex University, commissioned by DfID.]

The master sample is based on a selection of municipalities and a full enumeration of the selected municipalities. Optimally, one would prefer smaller units (geographic or administrative) than municipalities. However, while it was considered that the population estimates of municipalities were reasonably accurate, this was not the case for smaller geographic or administrative areas. To avoid the error involved in sampling smaller areas with very uncertain population estimates, municipalities were used as the base unit for the master sample.

The Statistics Sweden team proposed two options based on this same method, with the only difference being in the number of municipalities included and enumerated. For reasons of funding, the smaller option proposed by the team was used, or Option B.

Stratification of Municipalities

The first step in creating the Master Sample was to group the 146 municipalities in the country into three strata- Urban, Rural and Mixed - within each of the two entities. Urban municipalities are those where 65 percent or more of the households are considered to be urban, and rural municipalities are those where the proportion of urban households is below 35 percent. The remaining municipalities were classified as Mixed (Urban and Rural) Municipalities. Brcko was excluded from the sampling frame.

Urban, Rural and Mixed Municipalities: It is worth noting that the urban-rural definitions used in BiH are unusual with such large administrative units as municipalities classified as if they were completely homogeneous. Their classification into urban, rural, mixed comes from the 1991 Census which used the predominant type of income of households in the municipality to define the municipality. This definition is imperfect in two ways. First, the distribution of income sources may have changed dramatically from the pre-war times: populations have shifted, large industries have closed and much agricultural land remains unusable due to the presence of land mines. Second, the definition is not comparable to other countries' where villages, towns and cities are classified by population size into rural or urban or by types of services and infrastructure available. Clearly, the types of communities within a municipality vary substantially in terms of both population and infrastructure.

However, these imperfections are not detrimental to the sample design (the urban/rural definition may not be very useful for analysis purposes, but that is a separate issue). [Note: It may be noted that the percent of LSMS households in each stratum reporting using agricultural land or having livestock is highest in the "rural" municipalities and lowest in the "urban" municipalities. However, the concentration of agricultural households is higher in RS, so the municipality types are not comparable across entities. The percent reporting no land or livestock in RS was 74.7% in "urban" municipalities, 43.4% in "mixed" municipalities and 31.2% in "rural" municipalities. Respective figures for FbiH were 88.7%, 60.4% and 40.0%.]

The classification is used simply for stratification. The stratification is likely to have some small impact on the variance of survey estimates, but it does not introduce any bias.

Selection of Municipalities

Option B of the Master Sample involved sampling municipalities independently from each of the six strata described in the previous section. Municipalities were selected with probability proportional to estimated population size (PPES) within each stratum, so as to select approximately 50% of the mostly urban municipalities, 20% of the mixed and 10% of the mostly rural ones. Overall, 25 municipalities were selected (out of 146) with 14 in the FbiH and 11 in the RS. The distribution of selected municipalities over the sampling strata is shown below.

Stratum / Total municipalities Mi / Sampled municipalities mi 1. Federation, mostly urban / 10 / 5 2. Federation, mostly mixed / 26 / 4 3. Federation, mostly rural / 48 / 5 4. RS, mostly urban /4 / 2 5. RS, mostly mixed /29 / 5 6. RS, mostly rural / 29 / 4

Note: Mi is the total number of municipalities in stratum i (i=1, … , 6); mi is the number of municipalities selected from stratum

The dissertation consists of three chapters relating to the measurement of immigration policies, which developed out of my work as an initial co-author of the International Migration Policy and Law Analysis (IMPALA) Database Project. The first chapter entitled, “Brain Gain? Measuring skill bias in U.S. migrant admissions policy,” develops a conceptual and operational definition of skill bias. I apply the measure to new data revealing the level of skill bias in U.S. migrant admissions policy between 1965 and 2008. Skill bias in U.S. migrant admissions policy is both a critical determinant of the skill composition of the migrant population and a response to economic and public demand for highly skilled migrants. However, despite its central role, this is the first direct, comprehensive, annual measure of skill bias in U.S. migrant admissions policy. The second chapter entitled, “Stalled in the Senate: Explaining change in US migrant admissions policy since 1965,” presents new data characterizing change in U.S. migrant admissions policy as both expansive and infrequent over recent decades. I present a new theory of policy change in U.S. migrant admissions policy that incorporates the role of supermajoritarian decision making procedures and organized anti-immigration groups to better account for both the expansive nature and t he infrequency of policy change. The theory highlights the importance of a coalition of immigrant advocacy groups, employers and unions in achieving policy change and identifies the conditions under which this coalition is most likely to form and least likely to be blocked by an anti-immigration group opposition. The third chapter entitled, “Post-coding aggregation: A methodological principle for independent data collection,” presents a new technique developed to enable independent collection of flexible, high quality data: post-coding aggregation. Post-coding aggregation is a methodological principle that minimizes data loss, increases transparency, and grants data analysts the ability to decide how best to aggregate information to produce measures. I demonstrate how it increases the fl exibility of data use by expanding the utility of data collections for a wider range of research objectives and improves the reliability and the content validity of measures in data analysis.

ACF Children Bureau resource Metadata-only record linking to the original dataset. Open original dataset below.

AbstractObjectives: To assess feasibility, acceptability, and clinical sensibility of a novel survey, the Advance Care Planning (ACP) Engagement Survey in various health care settings. Setting: A target sample of 50 patients from each of primary care, hospital, cancer care, and dialysis care settings. Participants: A convenience sample of patients without cognitive impairment who could speak and read English was recruited. Patients 50 years and older were eligible in primary care; patients 80 and older or 55 years and older with clinical markers of advanced chronic disease were recruited in hospital; patients aged 19 and older were recruited in cancer and renal dialysis centres. Outcomes: We assessed feasibility, acceptability and clinical sensibility of the ACP Engagement Survey using a 6-point scale. The ACP Engagement Survey measures ACP processes (knowledge, contemplation, self-efficacy, readiness) on 5-point Likert scales and actions (yes/no). Results: 196 patients (38 to 96 years old, 50.5% women) participated. Mean (±standard deviation) time to administer was 48.8 ±19.6 minutes. Mean acceptability scores ranged from 3.2±1.3 in hospital to 4.7±0.9 in primary care and mean relevance ranged from 3.5±1.0 in hospital to 4.9±0.9 in dialysis centres (p values <0.001 for both). The mean process score was 3.1±0.6 and the mean action score was 11.2±5.6 (of a possible 25). Conclusions: The ACP Engagement Survey demonstrated feasibility and acceptability in out-patient settings, but was less feasible and acceptable among hospitalized patients due to length. A shorter version may improve feasibility. Engagement in ACP was low to moderate. Usage notesREADMEThe Readme file includes a list of files in this data package, and a description of the variables that were removed from the dataset to protect participant identity. Please see the "Data dictionary" for a description of the variables that were included in the dataset, and the "Summary table of indirect identifier data" for a summary of values reported at removed variables.Data Dictionary - Canadian ACP engagement sample BMJ OpenThis file describes the variables that were included in the dataset, and their allowable values.Canadian ACP engagement sample BMJ Open_data dictionary.xlsxCanadian ACP engagement survey pilotThis file contains the responses of 196 patients in acute care, primary care, cancer care and renal care to a 108-item ACP engagement survey. Process Measures (knowledge, contemplation, self-efficacy, and readiness, 5-point Likert scales) and Action Measures (yes/no whether an ACP behavior was completed) are included.Canadian ACP engagement sample_BMJ Open_indirect identifiers removed.xlsxSummary table of indirect identifier data - Canadian ACP engagement_BMJ OpenThis file contains descriptive analysis summary tables of indirect identifiers that were removed from the dataset.Canadian ACP engagement_BMJ Open_summary table of indirect identifier data.docx

This data package contains mean values for dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) for water samples taken from the East River Watershed in Colorado. The East River is part of the Watershed Function Scientific Focus Area (WFSFA) located in the Upper Colorado River Basin, United States. DOC and DIC concentrations in water samples were determined using a TOC-VCPH analyzer (Shimadzu Corporation, Japan). DOC was analyzed as non-purgeable organic carbon (NPOC) by purging HCl acidified samples with carbon-free air to remove DIC prior to measurement. After the acidified sample has been sparged, it is injected into a combustion tube filled with oxidation catalyst heated to 680 degrees C. The DOC in samples is combusted to CO2 and measured by a non-dispersive infrared (NDIR) detector. The peak area of the analog signal produced by the NDIR detector is proportional to the DOC concentration of the sample. DIC was determined by acidifying the samples with HCl first, and then purge with carbon-free air to release CO2 for analysis by NDIR detector. All files are labeled by location and variable, and data reported are the mean values upon minimum three replicate measurements with a relative standard deviation < 3%. All samples were analyzed under a rigorous quality assurance and quality control (QA/QC) process as detailed in the methods. This data package contains (1) a zip file (dic_npoc_data_2014-2023.zip) containing a total of 319 files: 318 data files of DIC and NPOC data from across the Lawrence Berkeley National Laboratory (LBNL) Watershed Function Scientific Focus Area (SFA) which is reported in .csv files per location and a locations.csv (1 file) with latitude and longitude for each location; (2) a file-level metadata (v3_20230808_flmd.csv) file that lists each file contained in the dataset with associated metadata; (3) a data dictionary (v3_20230808_dd.csv) file that contains terms/column_headers used throughout the files along with a definition, units, and data type; and (4) PDF and docx files for the determination of Method Detection Limits (MDLs) for DIC and NPOC data. There are a total of 106 locations containing DIC/NPOC data. Update on 2020-10-07: Updated the data files to remove times from the timestamps, so that only dates remain. The data values have not changed. Update on 2021-04-11: Added Determination of Method Detection Limits (MDLs) for DIC, NPOC and TDN Analyses document, which can be accessed as a PDF or with Microsoft Word. Update on 2022-06-10: versioned updates to this dataset was made along with these changes: (1) updated dissolved inorganic carbon and dissolved organic carbon data for all locations up to 2021-12-31, (2) removal of units from column headers in datafiles, (3) added row underneath headers to contain units of variables, (4) restructure of units to comply with CSV reporting format requirements, (5) added -9999 for empty numerical cells, and (6) the addition of the file-level metadata (flmd.csv) and data dictionary (dd.csv) were added to comply with the File-Level Metadata Reporting Format. Update on 2022-09-09: Updates were made to reporting format specific files (file-level metadata and data dictionary) to correct swapped file names, add additional details on metadata descriptions on both files, add a header_row column to enable parsing, and add version number and date to file names (v2_20220909_flmd.csv and v2_20220909_dd.csv). Update on 2023-08-08: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-01-05. The file level metadata and data dictionary files were updated to reflect the additional data added.

In this paper, we generalize the notion of measurement error on deterministic sample datasets to accommodate sample data that are random-variable-valued. This leads to the formulation of two distinct kinds of measurement error: intrinsic measurement error, and incidental measurement error. Incidental measurement error will be recognized as the traditional kind that arises from a set of deterministic sample measurements, and upon which the traditional measurement error modelling literature is based, while intrinsic measurement error reflects some subjective quality of either the measurement tool or the measurand itself. We define calibrating conditions that generalize common and classical types of measurement error models to this broader measurement domain, and explain how the notion of generalized Berkson error in particular mathematicizes what it means to be an expert assessor or rater for a measurement process. We then explore how classical point estimation, inference, and likelihood theory can be generalized to accommodate sample data composed of generic random-variable-valued measurements.

This collection contains data obtained with an unmanned aircraft equipped with an analyser measuring carbon dioxide dry air mole fraction, ambient temperature, humidity and pressure. Measurement flights were carried out as part of the ScaleX 2016 campaign in southern Germany (47.833 °N, 11.060 °E, WGS84) in July 2016. Evan Flatt, Richard H. Grant (both at Purdue University, West Lafayette, IN, USA), Martin Kunz and Jost V. Lavric (both at Max Planck Institute for Biogeochemistry, Jena, Germany) have contributed to these measurements. Furthermore, this collection contains output of the STILT (Stochastic Time-Inverted Lagrangian Transport) model, which was run on the basis of meteorological data from the ECMWF IFS (European Centre for Medium-Range Weather Forecasts Integrated Forecast System). Christoph Gerbig (Max Planck Institute for Biogeochemistry, Jena, Germany) and Frank-Thomas Koch (Deutscher Wetterdienst, Meteorologisches Observatorium Hohenpeissenberg, Germany) contributed this data. Finally, this collection also contains all necessary scripts to obtain estimates of surface flux from the aforementioned data by means of a nocturnal boundary layer budget approach. Author information is included in the script files. Instructions on how to run these scripts are give in the file "readme.txt".

An unambiguous algorithm, added to the study of the applicability domain and appropriate measures of the goodness of fit and robustness, represent the key characteristics that should be ideally fulfilled for a QSAR model to be considered for regulatory purposes. In this paper, we propose a new algorithm (RINH) based on the rivality index for the construction of QSAR classification models. This index is capable of predicting the activity of the data set molecules by means of a measurement of the rivality between their nearest neighbors belonging to different classes, contributing with a robust measurement of the reliability of the predictions. In order to demonstrate the goodness of the proposed algorithm we have selected four independent and orthogonally different benchmark data sets (balanced/unbalanced and high/low modelable) and we have compared the results with those obtained using 12 different machine learning algorithms. These results have been validated using 20 data sets of different balancing and sizes, corroborating that the proposed algorithm is able to generate highly accurate classification models and contribute with valuable measurements of the reliability of the predictions and the applicability domain of the built models.

Level: Beginner Recommended Use: Regression Models Domain: Environment

Abalone Data Set

Find out the age of Abalone from physical measurements

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This beginner level data set has 4177 rows and 9 columns and physical measurements of abalones and the number of rings (representing age). The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task. Other measurements, which are easier to obtain, are used to predict the age.

This data set is recommended for learning and practicing your skills in exploratory data analysis, data visualization, and classification modelling techniques. Feel free to explore the data set with multiple supervised and unsupervised learning techniques. The Following data dictionary gives more details on this data set:

|Column Position|Atribute Name|Definition|Data Type|Example| | --- | --- | |1 |gender|Gender (M: Male, F: Female, I: Infant) |Quantitative |"M", "F", "I" | |2 |Length|Longest Shell measurement (millimeters - mm) |Quantitative |0.530, 0.440, 0.425 | |3 |Diameter|Diameter - perpendicular to length (mm) |Quantitative |0.350, 0.380, 0.300 | |4 |Height|Height - with meat in shell (mm) |Quantitative |0.095, 0.150, 0.110 | |5 |Whole weight|Weight of whole abalone (grams) |Quantitative |0.5140, 0.2255, 0.6845 | |6 |Shucked weight|Weight of meat (grams) |Quantitative |0.1940, 0.1675, 0.0975 | |7 |Viscera weight|Gut weight after bleeding (grams) |Quantitative |0.1010, 0.1495, 0.0490 | |8 |Shell weight|Shell weight - after being dried (grams) |Quantitative |0.330, 0.115, 0.245 | |9 |Rings|Rings - value + 1.5 gives age in years (eg. 4 = 5.5 years) |Quantitative |19, 8, 29 |