Restricted Data Files Available at the Data Centers Researchers and users with approved research projects can access restricted data files that have not been publicly released for reasons of confidentiality at the AHRQ Data Center in Rockville, Maryland. Qualified researchers can also access restricted data files through the U.S. Census Research Data Center (RDC) network (http://www.census.gov/ces/dataproducts/index.html -- Scroll down the page and click on the Agency for Health Care Research and Quality (AHRQ) link.) For information on the RDC research proposal process and the data sets available, read AHRQ-Census Bureau agreement on access to restricted MEPS data.

The Medical Expenditure Panel Survey (MEPS) Household Component (HC) collects data from a sample of families and individuals in selected communities across the United States, drawn from a nationally representative subsample of households that participated in the prior year's National Health Interview Survey (conducted by the National Center for Health Statistics). During the household interviews, MEPS collects detailed information for each person in the household on the following: demographic characteristics, health conditions, health status, use of medical services, charges and source of payments, access to care, satisfaction with care, health insurance coverage, income, and employment. The panel design of the survey, which features several rounds of interviewing, makes it possible to determine how changes in respondents' health status, income, employment, eligibility for public and private insurance coverage, use of services, and payment for care are related. Public Use Files for Household data are available on the MEPS website.

This dataset tracks the updates made on the dataset "Medical Expenditure Panel Survey (MEPS) Restricted Data Files" as a repository for previous versions of the data and metadata.

The Medical Expenditure Panel Survey, which began in 1996, is a set of large-scale surveys of families and individuals, their medical providers (doctors, hospitals, pharmacies, etc.), and employers across the United States. MEPS collects data on the specific health services that Americans use, how frequently they use them, the cost of these services, and how they are paid for, as well as data on the cost, scope, and breadth of health insurance held by and available to U.S. workers.The files in this deposit were downloaded from the AHRQ website by Julia Dennett, Yale University, and Toby Chaiken, J-PAL North America, and archived by Travis Donahoe, Harvard University. Additional information edited by Michael Darisse and Lars Vilhuber, Cornell University and American Economic Association.

The Medical Expenditure Panel Survey Insurance Component (MEPS-IC) is an annual survey of private employers and State and local governments. The MEPS-IC produces national and State level estimates of employer-sponsored insurance, including offered plans, costs, employee eligibility, and number of enrollees. PDF files are available for complete sets of table series on employer-based health insurance at the national, state, and metropolitan area levels. The MEPS-IC is sponsored by the Agency for Healthcare Research and Quality and is fielded by the U.S. Census Bureau.

The Medical Expenditure Panel Survey (MEPS) Household Component collects data on all members of sample households from selected communities across the United States. The MEPS-HC Variable Explorer Tool provides a quick and easy way to search across MEPS Public Use Files for variables and files needed for users' research projects.

This data repository houses SAS and STATA files that correspond with studies evaluating Middle Eastern and North African (MENA) health using linked 2000-2017 NHIS and 2001-2018 MEPS data.

DESCRIPTION: The data files contain analysis and forecast data from the forecasting model MEPS. MEPS is an ensemble forecasting system with a total of 15 members over a three-hour period. This means that MEPS produces 15 different forecasts for each forecast occasion where the different members have equal initial states based on boundary values from selected ensemble perturbation from the European Weather Centre model. By using the information in all ensemble members, different probabilities can be calculated, e.g. how uncertain the forecast is. If you are only interested in a deterministic forecast, MEPS member 0 is used. Each model member runs every 3 hours with forecasts 66 hours ahead of time for the member. All mid-March data is generated in grib2 format, where only a limited number of parameters are retrieved from the members for each time they are generated. For member 0 and 1 all parameters are retrieved in grib1 format for every six hours, 00, 06, 12 and 18 UTC.

The forecast is made over an area covering Scandinavia, Finland, Denmark, the Baltics, the North Sea and parts of north-eastern Europe. The area is divided into grid squares with 2.5km resolution and 65 vertical levels. There are a large number of parameters that describe temperature, precipitation, wind, humidity, cloudiness, etc.

Use: At SMHI, data from MEPS is used as a basis for weather forecasts and as input to other models in areas such as hydrology and oceanography.

FORMAT: The data is in GRIB format.

The Particle Environment Monitor (PEM) level 2 Medium-Energy Particle Spectrometer (MEPS) daily product contains the electron and proton high-resolution spectral data converted to number intensity units from the MEPS sensors mounted on both the zenith and nadir UARS booms. PEM was flown on the UARS spacecraft to measure the type, amount, energy, and distribution of charged particles injected into the Earth's thermosphere, mesosphere, and stratosphere. The PEM MEPS data covers roughly the energy range from 1 eV - 5 eV to 32 keV, where the lower energy cutoff is determined by internal instrument protection potentials. There are five analyzers mounted in different directions on the zenith boom, each of which contains an electron and ion sensor. These analyzers are mounted at -23.7 deg, +6.3 deg, +21.3 deg, +36.3 deg, and +66.3 degrees with respect to the spacecraft -z axis and along the spacecraft +y axis. There are three analyzers mounted in different directions on the nadir boom, each of which contain only an electron sensor. These analyzers are mounted at -158.7 deg, +156.3 deg, and +126.3 deg, with respect to the spacecraft -z axis and along the spacecraft +y axis. All MEPS analyzers accumulate a spectrum in 2.046 sec. There is one data file per day for the PEM MEPS product, and the temporal coverage is from Oct. 1, 1991 to Aug. 23, 2005. Spatial coverage for the MEPS product ranges between -57 and +57 degrees latitude. The MEPS data files are written in network binary format. For more information please review the PEM MEPS data format guide.

The STATA do files for creating recodes and analyzing the data files will be provided in this folder for each paper once submitted for publication. Revised do files will be uploaded after the peer review process is complete if there are any changes to the files. The data for this study are publicly available on the Medical Expenditure Panel Survey website (https://meps.ahrq.gov/mepsweb/). SummarySignificant racial and ethnic disparities in cardiometabolic diseases, such as diabetes, underline entrenched health inequalities in the United States. Non-pregnant, non-Hispanic black women of reproductive age (18-45 years) are more likely to have diagnosed and undiagnosed diabetes, which increases their risk of maternal morbidity and mortality during the perinatal period. Adherence to disease management and monitoring during the preconception period is crucial, especially among non-Hispanic black women who are disproportionately impacted by maternal morbidity and mortality. Studies have shown positive patient experiences are associated with adherence to recommended medication and treatment, preventive care use, and self-rated health outcomes. There is a dearth of studies, however, examining the effects of patient experiences (and racial differences in patient experiences) on chronic disease management outcomes specifically among non-pregnant, reproductive-age women with diabetes. An understanding of these associations have important implications for maternal morbidity and mortality. The goal of this study is to use the Medical Expenditure Panel Survey datasets (2012-2017 longitudinal files) and robust statistical modeling techniques to investigate racial differences in patient experience among non-pregnant, reproductive-age women with diabetes and its relation to ratings of health care received, diabetes care self-efficacy, and diabetes care monitoring. This study provides important information for researchers, clinicians, and policy-makers. The research addresses the Maternal and Child Health Bureau (MCHB) Strategic Research Issue II: MCH services and systems of care efforts to eliminate health disparities and barriers to health care access for MCH populations. This study informs the development of equitable clinical patient-centered practices that promote optimal disease management among diverse women and reduce racial and ethnic disparities in maternal health outcomes. It also strengthens and expands MCH Services Block Grant National Performance and Population Priority Domain I: “Well-Woman Visits and Preconception/Interconception Health”. This study is expected to help determine whether positive patient experiences can improve women’s confidence in their abilities to manage their diabetes, and increase their likelihood of receiving recommended diabetes care during the preconception or interconception period. By elucidating the mechanisms by which promoting patient-centered diabetes care interventions during the preconception/interconception period might improve disease management, our study can inform practices and policies that contribute to the attainment of following Healthy People 2020 Maternal, Infant, and Child Health (MIC) Objectives: Increase the proportion of women delivering a live birth who received preconception care services and practiced key recommended preconception health behaviors (MICH-16); Reduce the rate of maternal illness and complications due to pregnancy (MICH-6); and Reduce the rate of maternal mortality (MICH-5). Furthermore, our study findings are expected identify the patient experiences that have the greatest impact on diabetes management outcomes, and can lead to policy changes for provider reimbursements for demonstrating quality patient-provider interactions. By providing insights into ways health care professionals can better communicate with this at-risk population, our study is relevant to the attainment of Healthy People 2020 Health Communication and Health Information Technology (HC/HIT) Objectives: Increase the proportion of persons who report that their health care providers have satisfactory communication skills (HC/HIT-2) and Increase the proportion of persons who report that their health care providers always involved them in decisions about their health care as much as they wanted (HC/HIT-3).

The resource consists of two datasets related to Members of the 8th European Parliament (MEPs). The first one is a dataset of 2,535 roll-call votes of MEPs until 2016-03-01. The second one is a dataset of 26,133 retweets between MEPs in the period between 2014-10-01 and 2016-03-01. The data can be used to examine the patterns of covoting and retweeting of MEPs and analyze the extent to which they are similar.

The resource is presented and used in the paper:

Darko Cherepnalkoski, Andreas Karpf, Igor Mozetič, Miha Grčar "Cohesion and coalition formation in the European Parliament: Roll-call votes and Twitter activities". PLoS ONE 11(11): e0166586, 2016. http://dx.doi.org/10.1371/journal.pone.0166586

The dataset contains 5 files, of which 3 contain metadata and 2 data.

The metadata comprises information about the Members of 8th European Parliament (MEPs) until 2016-03-01, about roll-call votes (RCV) and possible actions during a RCV. The first data file contains a matrix with the votes of all MEPs during all RCVs while the second contains the retweets between the MEPs.

Data files required to run the analysis of EMG (MEPs) and EEG (TEPs) recordings in response to TMS over the motor cortex:

Darko Cherepnalkoski, Andreas Karpf, Igor Mozetič, Miha Grčar "Cohesion and coalition formation in the European Parliament: Roll-call votes and Twitter activities". PLoS ONE 11(11): e0166586, 2016. http://dx.doi.org/10.1371/journal.pone.0166586 The dataset contains 5 files, of which 3 contain metadata and 2 data. The metadata comprises information about the Members of 8th European Parliament (MEPs) until 2016-03-01, about roll-call votes (RCV) and possible actions during a RCV. The first data file contains a matrix with the votes of all MEPs during all RCVs while the second contains the retweets between the MEPs.

Description. This is the data used in the experiment of the following conference paper:

N. Arınık, R. Figueiredo, and V. Labatut, “Signed Graph Analysis for the Interpretation of Voting Behavior,” in International Conference on Knowledge Technologies and Data-driven Business - International Workshop on Social Network Analysis and Digital Humanities, Graz, AT, 2017, vol. 2025. ⟨hal-01583133⟩

Source code. The code source is accessible on GitHub: https://github.com/CompNet/NetVotes

Citation. If you use the data or source code, please cite the above paper.

@InProceedings{Arinik2017, author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent}, title = {Signed Graph Analysis for the Interpretation of Voting Behavior}, booktitle = {International Conference on Knowledge Technologies and Data-driven Business - International Workshop on Social Network Analysis and Digital Humanities}, year = {2017}, volume = {2025}, series = {CEUR Workshop Proceedings}, address = {Graz, AT}, url = {http://ceur-ws.org/Vol-2025/paper_rssna_1.pdf},}

Details.

RAW INPUT FILESThe 'itsyourparliament' folder contains all raw input files for further data processing (such as network extraction).The folder structure is as follows:* itsyourparliament/** domains: There are 28 domain files. Each file corresponds to a domain (such as Agriculture, Economy, etc.) and contains corresponding vote identifiers and their "itsyourparliament.eu" links.** meps: There are 870 Member of Parliament (MEP) files. Each file contains the MEP information (such as name, country, address, etc.)** votes: There are 7513 vote files. Each file contains the votes expressed by MEPs# NETWORKS AND CORRESPONDING PARTITIONSThis work studies the voting behavior of French and Italian MEPs on "Agriculture and Rural Development" (AGRI) and "Economic and Monetary Affairs" (ECON) for each separate year of the 7th EP term (2009-10, 2010-11, 2011-12, 2012-13, 2013-14). Note that the interpretation part (section 4) of the published paper is limited to only a few of these instances (2009-10 in ECON and 2012-13 in AGRI).The extracted networks are located in the "networks" folder and the corresponding partitions are in the "partitions" folder. Both folders have the same structure, which is as follows:COUNTRY-NAME|_DOMAIN-NAME|_2009-10|_2010-11|_2011-12|_2012-13|_2013-14## NETWORKSThe networks in this folder are used in the article. All those networks are the ones obtained after the filtering step (as explained in the article). The networks are in 'Graphml' format. These networks are enriched with some MEPs' properties (such as name, political party, etc.) associated with each node.## ALL NETWORKSFor those who are interested in other countries or domains, we make available all possible networks that we can extract from raw data with vs. without filtering step.COUNTRY-NAME|_m3|_negtr=NA_postr=NA: This folder contains all filtered networks. Note that the filtering step is explained in Section 2.1.2 of the article.|_bygroup|_bycountry|_negtr=0_postr=0: This folder contains all original networks (i.e. no filtering step).|_bygroup|_bycountry## PARTITIONSThe partitions are obtained in this way: First, the Ex-CC (exact) method is run and we denote 'k' for the the number of detected cluster in output. This 'k' value is the reference point in order to run the ILS-RCC (heuristic) method by specifying the number of desired cluster in output. Then, ILS-RCC is run with various values ('k', 'k+1', 'k+2'). All those results are integrated into the initial network graphml files and then converted into gephi format so that this will help dive in the results in interactive way.Note that we need to handle the absent MEPs in clustering results. Because, those MEPs correspond to isolated nodes in networks. Each isolated node is considered a single cluster node in Ex-CC results. We simply omit those nodes in order to find the 'k' (number of detected cluster) value before running ILS-RCC. Not also that ILS-RCC does not process isolated nodes such that an isolated node can be part of a cluster.

----------------------# COMPARISON RESULTSThe 'material-stats' folder contains all the comparison results obtained for Ex-CC and ILS-CC. The csv files associated with plots are also provided.The folder structure is as follows:* material-stats/** execTimePerf: The plot shows the execution time of Ex-CC and ILS-CC based on randomly generated complete networks of different size.** graphStructureAnalysis: The plots show the weights and links statistics for all instances.** ILS-CC-vs-Ex-CC: The folder contains 4 different comparisons between Ex-CC and ILS-CC: Imbalance difference, number of detected clusters, difference of the number of detected clusters, NMI (Normalized Mutual Information)

----------------------Funding: Agorantic FR 3621, FMJH Program Gaspard Monge in optimization and operation research (Project 2015-2842H)

Presentation. For more than a decade, graphs have been used to model the voting behavior taking place in parliaments. However, the methods described in the literature suffer from several limitations. The two main ones are that 1) they rely on some temporal integration of the raw data, which causes some information loss; and/or 2) they identify groups of antagonistic voters, but not the context associated with their occurrence. In this article, we propose a novel method taking advantage of multiplex signed graphs to solve both these issues. It consists in first partitioning separately each layer, before grouping these partitions by similarity. We show the interest of our approach by applying it to a European Parliament dataset. Particularly, we study the voting behavior of French and Italian MEPs on "Agriculture and Rural Development" (AGRI) during the 2012-13 legislative year.

These are the data used in the following paper:

N. Arınık, R. Figueiredo, and V. Labatut, “Multiple partitioning of multiplex signed networks: Application to European Parliament votes,” Social Networks, vol. 60, pp. 83–102, 2020. DOI: 10.1016/j.socnet.2019.02.001 ⟨hal-02082574⟩

Source code. The code source is accessible on GitHub: https://github.com/CompNet/MultiNetVotes

Citation. If you use these data our this source code, please cite the above paper.

@Article{Arinik2020, author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent}, title = {Multiple Partitioning of Multiplex Signed Networks: Application to {E}uropean {P}arliament Votes}, journal = {Social Networks}, year = {2020}, volume = {60}, pages = {83-102}, doi = {10.1016/j.socnet.2019.02.001},}----------------------------------------------Details.# RAW INPUT FILESThe 'itsyourparliament' folder contains all raw input files for further data processing. This is the same raw data that can be found in our previous Figshare repository: https://doi.org/10.6084/m9.figshare.5785833The folder structure is as follows:* itsyourparliament/** domains: There are 28 domain files. Each file corresponds to a domain (such as Agriculture, Economy, etc.) and contains corresponding vote identifiers and their "itsyourparliament.eu" links.** meps: There are 870 Members of Parliament (MEP) files. Each file contains the MEP information (such as name, country, address, etc.)** votes: There are 7513 vote files. Each file contains the votes expressed by MEPs# ROLLCALL NETWORKSThis folder contains two separate zip files regarding rollcall networks:- rollcall-networks: This folder contains only the rollcall networks that are used in the article.- all-rollcall-networks: For those who are interested in other countries or domains, we make available all rollcall networks that we can extract from raw data.Note that these rollcall networks constitute the layers of the input signed multplex network, as illustrated in Figure 1 of the article. Note also that we consider three vote types in our network extraction process: FOR, AGAINST and ABSTAIN.# ROLLCALL PARTITIONSNote that MEPs who voted similarly are connected together by positive links, and are connected by negative links to MEPs that voted differently from them. MEPs who did not vote at all (ABSENT) are isolates (nodes without anyneighbor). We identify the factions of similarly voting MEPs in the graph by solving the Correlation Clustering problem (CC).The rollcall partitions correspond to voting patterns, as illustrated in Figure 1 of the article.# ROLLCALL CLUSTERINGThis folder contains the results of Steps 3 and 4 of our workflow (see Figure 1 in the article). The structure of this folder is as follows:|_ votetypes=FAA/: 'FAA' means we consider three vote types in our analysis: FOR, AGAINST and ABSTAIN.|_ F.purity-k=2-sil=SILHOUETTE_SCORE|_ clu=CLUSTER_NO/|_ network: It corresponds to the network created through the similarity network-based approach, as explained in Section 4.4 of the article.|_ partition: It corresponds to the characteristic voting pattern, as explained in Section 4.4 of the article.----------------------------------------------

Funding: this research benefited from the support of the Agorantic FR 3621, as well as the FMJH Program PGMO and from the support to this program from EDF-THALES-ORANGE-CRITEO.

This is the data used in the experiment of the paper submited to the following conference:N. Arinik, R. Figueiredo, V. Labatut, Signed graph analysis for the interpretation of voting behavior, in: International Conference on Knowledge Technologies and Data-driven Business - International Workshop on Social Network Analysis and Digital Humanities, Graz, AT, 2017.URL http://ceur-ws.org/Vol-2025/paper_rssna_1.pdfThe code source is accessible here: https://github.com/CompNet/NetVotes# RAW INPUT FILESThe 'itsyourparliament' folder contains all raw input files for further data processing (such as network extraction).The folder structure is as follows:* itsyourparliament/** domains: There are 28 domain files. Each file corresponds to a domain (such as Agriculture, Economy, etc.) and contains corresponding vote identifiers and their "itsyourparliament.eu" links.** meps: There 870 Member of Parliament (MEP) files. Each file contains the MEP information (such as name, country, address, etc.)** votes: There are 7513 vote files. Each file contains the votes expressed by MEPs# NETWORKS AND CORRESPONDING PARTITIONSThis work studies the voting behavior of French and Italian MEPs on "Agriculture and Rural Development" (AGRI) and "Economic and Monetary Affairs" (ECON) for each separate year of the 7th EP term (2009-10, 2010-11, 2011-12, 2012-13, 2013-14). Note that the interpretation part (section 4) of the published paper are limited to only a few instances of them (2009-10 in ECON and 2012-13 in AGRI).The extracted networks are located in the "networks" folder and the corresponding partitions are in the "partitions" folder. Both folders has the same folder structure and it is as follows:COUNTRY-NAME|_DOMAIN-NAME|_2009-10|_2010-11|_2011-12|_2012-13|_2013-14## NETWORKSThe networks in this folder are used in the article. All those networks are the ones obtained after the filtering step (as explained in the article). The networks are in 'Graphml' format. These networks are enriched with some MEPs' properties (such as name, political party, etc.) associated with each node.## ALL NETWORKSFor those who are interested in other countries or domains, we make available all possible networks that we can extract from raw data with vs. without filtering step. COUNTRY-NAME |_m3 |_negtr=NA_postr=NA: This folder contains all filtered networks. Note that the filtering step is explained in Section 2.1.2 of the article. |_bygroup |_bycountry |_negtr=0_postr=0: This folder contains all original networks (i.e. no filtering step). |_bygroup |_bycountry## PARTITIONSThe partitions are obtained in this way: First, the Ex-CC (exact) method is run and we denote 'k' for the the number of detected cluster in output. This 'k' value is the reference point in order to run the ILS-RCC (heuristic) method by specifying the number of desired cluster in output. Then, ILS-RCC is run with various values ('k', 'k+1', 'k+2'). All those results are integrated into the initial network graphml files and then converted into gephi format so that this will help dive in the results in interactive way.Note that we need to handle the absent MEPs in clustering results. Because, those MEPs correspond to isolated nodes in networks. Each isolated node is considered a single cluster node in Ex-CC results. We simply omit those nodes in order to find the 'k' (number of detected cluster) value before running ILS-RCC. Not also that ILS-RCC does not process isolated nodes such that an isolated node can be part of a cluster.# COMPARISON RESULTSThe 'material-stats' folder contains all the comparison results obtained for Ex-CC and ILS-CC. The csv files associated with plots are also provided.The folder structure is as follows:* material-stats/** execTimePerf: The plot shows the execution time of Ex-CC and ILS-CC based on randomly generated complete networks of different size.** graphStructureAnalysis: The plots show the weights and links statistics for all instances.** ILS-CC-vs-Ex-CC: The folder contains 4 different comparisons between Ex-CC and ILS-CC: Imbalance difference, number of detected clusters, difference of the number of detected clusters, NMI (Normalized Mutual Information)

This dataset contains corticospinal excitability measures (MEPs, rMT, background EMG) investigated via single-pulse transcranial magnetic stimulation (TMS) of 166 anonymized adult participants (141 neurotypical, 25 with a clinical autism spectrum diagnosis) included in the PhD project of the dataset author. TMS-induced MEPs were collected during rest and during different action observation settings (see ReadMe files per cohort for experimental details and stimuli descriptions). The dataset is structured per experimental cohort (1 to 6) and composed of 1 CSV file per participant, reporting raw MEP waveform characteristics and background EMG-related parameters elicited under different experimental conditions. Participants' demographic characteristics, individual TMS-related parameters (rMT, hotspot coordinates) as well as self-reported raw questionnaire scores are reported in the tabular data overview. Questionnaires include the Social Responsiveness Scale (SRS), Social Phobia Inventory (SPIN) and State Adult Attachment Scale (SAAM).

Stimulating the nervous system and measuring muscle response offers a unique opportunity to interrogate motor system function. Often, this is performed by stimulating motor cortex and recording muscle activity with electromyography; the evoked response is called the motor evoked potential (MEP). To understand system dynamics, MEPs are typically recorded through a range of motor cortex stimulation intensities. The MEPs increase with increasing stimulation intensities, and these typically produce a sigmoidal response curve. Analysis of MEPs is often complex and analysis of response curves is time-consuming. We created an MEP analysis software, called Motometrics, to facilitate analysis of MEPs and response curves. The goal is to combine robust signal processing algorithms with a simple user interface. Motometrics first enables the user to annotate data files acquired from the recording system so that the responses can be extracted and labeled with the correct subject and experimental condition. The software enables quick visual representations of entire datasets, to ensure uniform quality of the signal. It then enables the user to choose a variety of response curve analyses and to perform near real time quantification of the MEPs for quick feedback during experimental procedures. This is a modular open source tool that is compatible with several popular electrophysiological systems. Initial use indicates that Motometrics enables rapid, robust, and intuitive analysis of MEP response curves by neuroscientists without programming or signal processing expertise.

Description. The NetVote dataset contains the outputs of the NetVote program when applied to voting data coming from VoteWatch (http://www.votewatch.eu/). These results were used in the following conference papers: I. Mendonça, R. Figueiredo, V. Labatut, and P. Michelon, “Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes From the European Parliament,” in 2nd European Network Intelligence Conference, 2015, pp. 122–129. ⟨hal-01176090⟩ DOI: 10.1109/ENIC.2015.25 I. Mendonça, R. Figueiredo, V. Labatut, and P. Michelon, “Informative Value of Negative Links for Graph Partitioning, with an application to European Parliament Votes,” in 6ème Conférence sur les modèles et lánalyse de réseaux : approches mathématiques et informatiques, 2015, p. 12p. ⟨hal-02055158⟩ Source code. The NetVote source code is available on GitHub: https://github.com/CompNet/NetVotes. Citation. If you use our dataset or tool, please cite article [1] above. @InProceedings{Mendonca2015, author = {Mendonça, Israel and Figueiredo, Rosa and Labatut, Vincent and Michelon, Philippe}, title = {Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes From the {E}uropean {P}arliament}, booktitle = {2\textsuperscript{nd} European Network Intelligence Conference ({ENIC})}, year = {2015}, pages = {122-129}, address = {Karlskrona, SE}, publisher = {IEEE Publishing}, doi = {10.1109/ENIC.2015.25},}------------------------- Details. This archive contains the following folders: votewatch_data: the raw data extracted from the VoteWatch website. VoteWatch Europe European Parliament, Council of the EU.csv: list of the documents voted during the considered term, with some details such as the date and topic. votes_by_document: this folder contains a collection of CSV files, each one describing the outcome of the vote session relatively to one specific document. intermediate_files: this folder contains several CSV files: allvotes.csv: concatenation of all vote outcomes for all documents and all MEPS. Can be considered as a compact representation of the data contained in the folder votes_by_document. loyalty.csv: same thing than allvotes.csv, but for the loyalty (i.e. whether or not the MEP voted like the majority of the MEPs in his political group). MPs.csv: list of the MEPs having voted at least once in the considered term, with their details. policies.csv: list of the topics considered during the term. qtd_docs.csv: list of the topics with the corresponding number of documents. parallel_ils_results: contains the raw results of the ILS tool. This is an external algorithm able to estimate the optimal partition of the network nodes in terms of structural balance. It was applied to all the networks extracted by our scripts (from the VoteWatch data), and the produced files were placed here for postprocessing. Each subfolder corresponds to one of the topic-year pair. output_files: contains the file produced by our scripts. agreement: histograms representing the distributions of agreement and rebellion indices. Each subfolder corresponds to a specific topic. community_algorithms_csv: Performances obtained by the partitioning algorithms (for both community detection and correlation clustering). Each subfolder corresponds to a specific topic. xxxx_cluster_information.csv: table containing several variants of the imbalance measure, for the considered algorithms. community_algorithms_results: Comparison of the partitions detected by the various algorithms considered, and distribution of the cluster/community sizes. Each subfolder corresponds to a specific topic. xxxx_cluster_comparison.csv: table comparing the partitions detected by the community detection algorithms, in terms of Rand index and other measures. xxxx_ils_cluster_comparison.csv: like xxxx_cluster_comparison.csv, except we compare the partition of community detection algorithms with that of the ILS. xxxx_yyyy_distribution.pdf: histogram of the community (or cluster) sizes detected by algorithm yyyy. graphs: the networks extracted from the vote data. Each subfolder corresponds to a specific topic. xxxx_complete_graph.graphml: network at the Graphml format, with all the information: nodes, edges, nodal attributes (including communities), weights, etc. xxxx_edges_Gephi.csv: only the links, with their weights (i.e. vote similarity). xxxx_graph.g: network at the g format (for ILS). xxxx_net_measures.csv: table containing some stats on the network (number of links, etc.). xxxx_nodes_Gephi.csv: list of nodes (i.e. MEPs), with details. plots: synthesis plots from the paper. ------------------------- License. These data are shared under a Creative Commons 0 license. Contact. Vincent Labatut & Rosa Figueiredo