Journals & Ranking

An academic journal or research journal is a periodical publication in which research articles relating to a particular academic discipline is published, according to Wikipedia. Currently, there are more than 25,000 peer-reviewed journals that are indexed in citation index databases such as Scopus and Web of Science. These indexes are ranked on the basis of various metrics such as CiteScore, H-index, etc. The metrics are calculated from yearly citation data of the journal. A lot of efforts are given to make a metric that reflects the journal's quality.

Journal Ranking Dataset

This is a comprehensive dataset on the academic journals coving their metadata information as well as citation, metrics, and ranking information. Detailed data on their subject area is also given in this dataset. The dataset is collected from the following indexing databases: - Scimago Journal Ranking - Scopus - Web of Science Master Journal List

The data is collected by scraping and then it was cleaned, details of which can be found in HERE.

Key Features

• Rank: Overall rank of journal (derived from sorted SJR index).
• Title: Name or title of journal.
• OA: Open Access or not.
• Country: Country of origin.
• SJR-index: A citation index calculated by Scimago.
• CiteScore: A citation index calculated by Scopus.
• H-index: Hirsh index, the largest number h such that at least h articles in that journal were cited at least h times each.
• Best Quartile: Top Q-index or quartile a journal has in any subject area.
• Best Categories: Subject areas with top quartile.
• Best Subject Area: Highest ranking subject area.
• Best Subject Rank: Rank of the highest ranking subject area.
• Total Docs.: Total number of documents of the journal.
• Total Docs. 3y: Total number of documents in the past 3 years.
• Total Refs.: Total number of references of the journal.
• Total Cites 3y: Total number of citations in the past 3 years.
• Citable Docs. 3y: Total number of citable documents in the past 3 years.
• Cites/Doc. 2y: Total number of citations divided by the total number of documents in the past 2 years.
• Refs./Doc.: Total number of references divided by the total number of documents.
• Publisher: Name of the publisher company of the journal.
• Core Collection: Web of Science core collection name.
• Coverage: Starting year of coverage.
• Active: Active or inactive.
• In-Press: Articles in press or not.
• ISO Language Code: Three-letter ISO 639 code for language.
• ASJC Codes: All Science Journal Classification codes for the journal.

Rest of the features provide further details on the journal's subject area or category: - Life Sciences: Top level subject area. - Social Sciences: Top level subject area. - Physical Sciences: Top level subject area. - Health Sciences: Top level subject area. - 1000 General: ASJC main category. - 1100 Agricultural and Biological Sciences: ASJC main category. - 1200 Arts and Humanities: ASJC main category. - 1300 Biochemistry, Genetics and Molecular Biology: ASJC main category. - 1400 Business, Management and Accounting: ASJC main category. - 1500 Chemical Engineering: ASJC main category. - 1600 Chemistry: ASJC main category. - 1700 Computer Science: ASJC main category. - 1800 Decision Sciences: ASJC main category. - 1900 Earth and Planetary Sciences: ASJC main category. - 2000 Economics, Econometrics and Finance: ASJC main category. - 2100 Energy: ASJC main category. - 2200 Engineering: ASJC main category. - 2300 Environmental Science: ASJC main category. - 2400 Immunology and Microbiology: ASJC main category. - 2500 Materials Science: ASJC main category. - 2600 Mathematics: ASJC main category. - 2700 Medicine: ASJC main category. - 2800 Neuroscience: ASJC main category. - 2900 Nursing: ASJC main category. - 3000 Pharmacology, Toxicology and Pharmaceutics: ASJC main category. - 3100 Physics and Astronomy: ASJC main category. - 3200 Psychology: ASJC main category. - 3300 Social Sciences: ASJC main category. - 3400 Veterinary: ASJC main category. - 3500 Dentistry: ASJC main category. - 3600 Health Professions: ASJC main category.

This API is designed to find the rankings by any geography ID within the nation with a specific census metric (population or household) and ranking metric (any of the metrics from provider, demographic, technology or speed). The results are the top ten and bottom ten rankings within the nation for the particular geography type and my area rankings include +/- 5 rankings from the my area rank.

Dataset containing university rankings. See source: https://www.timeshighereducation.com/world-university-rankings

Originally used in:

Anahideh, Hadis, and Nasrin Mohabbati-Kalejahi. "Local explanations of global rankings: insights for competitive rankings." IEEE Access 10 (2022): 30676-30693. https://ieeexplore.ieee.org/abstract/document/9733934

AS Rank is CAIDA's ranking of Autonomous Systems (AS) (which approximately map to Internet Service Providers) and organizations (Orgs) (which are a collection of one or more ASes). This ranking is derived from topological data collected by CAIDA's Archipelago Measurement Infrastructure and Border Gateway Protocol (BGP) routing data collected by the Route Views Project and RIPE NCC.
ASes and Orgs are ranked by their customer cone size, which is the number of their direct and indirect customers.
Note: We do not have data to rank ASes (ISPs) by traffic, revenue, users, or any other non-topological metric..

Explore the World Bank Population dataset to access rankings and insights on global population statistics. Click here for extensive data on various countries.

Rankings

Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Monaco, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, San Marino, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovenia, Solomon Islands, Somalia, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkmenistan, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, ZimbabweFollow data.kapsarc.org for timely data to advance energy economics research..

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The QS Rankings, renowned for its esteemed university evaluations, annually releases the QS World University Rankings. The 2024 edition comprises a dataset encompassing the top 100 universities globally, with each entry defined by 12 features.

The 'rank' feature denotes the university's position in the QS rankings, offering a quantitative representation of its standing. The 'university' column identifies the institution by name. The 'overall score' is a floating-point value derived from various contributing factors, reflecting the comprehensive evaluation undertaken by QS.

Academic reputation, an integral aspect, is quantified in the 'academic reputation' feature, while 'employer reputation' gauges the institution's standing in the professional realm. The 'faculty student ratio' is calculated by dividing the faculty count by the number of students, a metric often indicative of the learning environment's quality.

'Citations per faculty' delves into the scholarly impact, measuring the total citations received by an institution's papers over five years, normalized by faculty size. The 'international faculty ratio' and 'international students ratio' shed light on the global diversity of the academic community, capturing the proportion of foreign faculty and students.

The 'international research network' employs a formula to quantify the institution's global partnerships and collaborations. 'Employment outcomes' are assessed through a formula involving alumni impact and graduate employment indices, providing insights into the professional success of graduates.

Finally, the 'sustainability' feature evaluates an institution's commitment to environmental sciences, considering alumni outcomes and academic reputation within the field. It also examines the inclusion of climate science and sustainability in the curriculum, reflecting the growing emphasis on environmental consciousness in higher education.

In essence, this dataset encapsulates a multifaceted evaluation of universities worldwide, encompassing academic, professional, and sustainability dimensions, making it a valuable resource for individuals and institutions navigating the dynamic landscape of global higher education. VALUE FOUNDS IS HIPOTICALY data 2021

Top 1000 scholars world-wide according to their publications in journals related to business and management.

Ranked users Kaggle data

Data about Kaggle ranked users

Context

This data is available online here. I image it was obtained by a crawler since it is displayed on the Kaggle leader board. I took the data and standardize the country names and add a continent label to each user, but I did not use the city name. To preserve anonymity I removed the columns UserName and DisplayName from the original dataset.

Content

Each row represent a ranked user. The columns are: register date, current points, current ranking, highest ranking, country and continent.

In Kaggle, points and ranking change over time. So, all the positions represented here correspond only to a specific point in time (around August 2018).

Acknowledgements

I want to thank the team from Norconsult responsible to make this data public.

This API is designed to find the rankings by any geography type within the state with a specific census metric (population or household) and ranking metric (any of the metrics from provider, demographic, technology or speed). Only the top ten and bottom ten rankings would be returned through the API if the result set is greater than 500; otherwise full ranking list be returned.

Universities and higher education institutions form an integral part of the national infrastructure and prestige. As academic research benefits increasingly from international exchange and cooperation, many universities have increased investment in improving and enabling their global connectivity. Yet, the relationship of university performance and its global physical connectedness has not been explored in detail. We conduct the first large-scale data-driven analysis into whether there is a correlation between university relative ranking performance and its global connectivity via the air transport network. The results show that local access to global hubs (as measured by air transport network betweenness) strongly and positively correlates with the ranking growth (statistical significance in different models ranges between 5% and 1% level). We also showed that the local airport's aggregate flight paths (degree) and capacity (weighted degree) has no effect on university ranking, further showing global connectivity distance is more important that the capacity of flight connections. We also examined the effect of local city economic development as a confounding variable and no effect was observed suggesting that access to global transportation hubs outweighs economic performance as a determinant of university ranking. The impact of this research is that we have determined the importance of the centrality of global connectivity and, hence, established initial evidence for further exploring potential connections between university ranking and regional investment policies on improving global connectivity.

The SCImago Institutions Rankings (SIR) is a classification of academic and research-related institutions ranked by a composite indicator that combines three different sets of indicators based on research performance, innovation outputs and societal impact measured by their web visibility. It provides a friendly interface that allows the visualization of any customized ranking from the combination of these three sets of indicators. Additionally, it is possible to compare the trends for individual indicators of up to six institutions. For each large sector it is also possible to obtain distribution charts of the different indicators. For comparative purposes, the value of the composite indicator has been set on a scale of 0 to 100. However the line graphs and bar graphs always represent ranks (lower is better, so the highest values are the worst).

The County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. This feature layer contains 2022 County Health Rankings data for nation, state, and county levels. The Rankings are compiled using county-level measures from a variety of national and state data sources. Some example measures are:adult smokingphysical inactivityflu vaccinationschild povertydriving alone to workTo see a full list of variables, as well as their definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details. These measures are standardized and combined using scientifically-informed weights."By ranking the health of nearly every county in the nation, County Health Rankings & Roadmaps (CHR&R) illustrates how where we live affects how well and how long we live. CHR&R also shows what each of us can do to create healthier places to live, learn, work, and play – for everyone."Counties are ranked within their state on both health outcomes and health factors. Counties with a lower (better) health outcomes ranking than health factors ranking may see the health of their county decline in the future, as factors today can result in outcomes later. Conversely, counties with a lower (better) factors ranking than outcomes ranking may see the health of their county improve in the future.Some new variables in the 2022 Rankings data compared to previous versions:COVID-19 age-adjusted mortalitySchool segregationSchool funding adequacyGender pay gapChildcare cost burdenChildcare centersLiving wage (while the Living wage measure was introduced to the CHRR dataset in 2022 from the Living Wage Calculator, it is not available in the Living Atlas dataset and user’s interested in the most up to date living wage data can look that up on the Living Wage Calculator website).Data Processing Notes:Data downloaded April 2022Slight modifications made to the source data are as follows:The string " raw value" was removed from field labels/aliases so that auto-generated legends and pop-ups would only have the measure's name, not "(measure's name) raw value" and strings such as "(%)", "rate", or "per 100,000" were added depending on the type of measure.Percentage and Prevalence fields were multiplied by 100 to make them easier to work with in the map.Ratios were set to null if negative to make them easier to work with in the map.For demographic variables, the word "numerator" was removed and the word "population" was added where appropriate.Fields dropped from analytic data file: yearall fields ending in "_cihigh" and "_cilow"and any variables that are not listed in the sources and years documentation.Analytic data file was then merged with state-specific ranking files so that all county rankings and subrankings are included in this layer.2010 US boundaries were used as the data contain 2010 US census geographies, for a total of 3,142 counties.

An understanding of the similar and divergent metrics and methodologies underlying open government data benchmarks can reduce the risks of the potential misinterpretation and misuse of benchmarking outcomes by policymakers, politicians, and researchers. Hence, this study aims to compare the metrics and methodologies used to measure, benchmark, and rank governments' progress in open government data initiatives. Using a critical meta-analysis approach, we compare nine benchmarks with reference to meta-data, meta-methods, and meta-theories. This study finds that both existing open government data benchmarks and academic open data progress models use a great variety of metrics and methodologies, although open data impact is not usually measured. While several benchmarks’ methods have changed over time, and variables measured have been adjusted, we did not identify a similar pattern for academic open data progress models. This study contributes to open data research in three ways: 1) it reveals the strengths and weaknesses of existing open government data benchmarks and academic open data progress models; 2) it reveals that the selected open data benchmarks employ relatively similar measures as the theoretical open data progress models; and 3) it provides an updated overview of the different approaches used to measure open government data initiatives’ progress. Finally, this study offers two practical contributions: 1) it provides the basis for combining the strengths of benchmarks to create more comprehensive approaches for measuring governments’ progress in open data initiatives; and 2) it explains why particular countries are ranked in a certain way. This information is essential for governments and researchers to identify and propose effective measures to improve their open data initiatives.

This data set contains the data used to create the Open Edition of the CWTS Leiden Ranking 2023. The data set includes (1) data about the universities included in the Leiden Ranking Open Edition 2023 and the links between these universities and their affiliated organizations, (2) data about the publications included in the Leiden Ranking Open Edition 2023 and the links between these publications and universities and main fields, (3) indicators at the level of publications, and (4) indicators at the level of universities and main fields.

The Leiden Ranking Open Edition 2023 is based on the OpenAlex snapshot released on November 21, 2023. The snapshot data is not included in this data set.

The source code for creating this data set is available in this GitHub repository.

See this blog post for more information about the Leiden Ranking Open Edition 2023.

This data set consists of the following tab-delimited files.

university

• university_id
• university
• university_full_name
• ror_id
• ror_name
• country_code
• latitude
• longitude
• is_mtor_university

affiliated_organization

• ror_id
• ror_name

university_affiliated_organization

• university_ror_id
• relation_type
• affiliated_organization_ror_id
• affiliated_organization_weight

main_field

• main_field_id
• main_field

pub

• work_id
• doi
• pub_year
• micro_cluster_id

pub_university

• work_id
• university_id
• weight

pub_main_field

• work_id
• main_field_id
• weight

period

• period_begin_year
• period_end_year
• period

pub_period_impact_indicators

• work_id
• period_begin_year
• cs
• ncs
• p_top_1
• p_top_5
• p_top_10
• p_top_50

pub_collab_indicators

• work_id
• p_collab
• p_int_collab
• p_industry
• p_short_dist_collab
• p_long_dist_collab

pub_oa_indicators

• work_id
• p_oa_unknown
• p_oa
• p_gold_oa
• p_hybrid_oa
• p_bonze_oa
• p_green_oa

university_main_field_period_impact_indicators

• university_id
• main_field_id
• period_begin_year
• fractional_counting
• p
• tcs
• tncs
• p_top_1
• p_top_5
• p_top_10
• p_top_50
• mcs
• mcs_lb
• mcs_ub
• mncs
• mncs_lb
• mncs_ub
• pp_top_1
• pp_top_1_lb
• pp_top_1_ub
• pp_top_5
• pp_top_5_lb
• pp_top_5_ub
• pp_top_10
• pp_top_10_lb
• pp_top_10_ub
• pp_top_50
• pp_top_50_lb
• pp_top_50_ub

university_main_field_period_collab_indicators

• university_id
• main_field_id
• period_begin_year
• p
• p_collab
• p_int_collab
• p_industry_collab
• p_short_dist_collab
• p_long_dist_collab
• pp_collab
• pp_collab_lb
• pp_collab_ub
• pp_int_collab
• pp_int_collab_lb
• pp_int_collab_ub
• pp_industry_collab
• pp_industry_collab_lb
• pp_industry_collab_ub
• pp_short_dist_collab
• pp_short_dist_collab_lb
• pp_short_dist_collab_ub
• pp_long_dist_collab
• pp_long_dist_collab_lb
• pp_long_dist_collab_ub

university_main_field_period_oa_indicators

• university_id
• main_field_id
• period_begin_year
• p
• p_oa_unknown
• p_oa
• p_gold_oa
• p_hybrid_oa
• p_bronze_oa
• p_green_oa
• pp_oa_unknown
• pp_oa_unknown_lb
• pp_oa_unknown_ub
• pp_oa
• pp_oa_lb
• pp_oa_ub
• pp_gold_oa
• pp_gold_oa_lb
• pp_gold_oa_ub
• pp_hybrid_oa
• pp_hybrid_oa_lb
• pp_hybrid_oa_ub
• pp_bronze_oa
• pp_bronze_oa_lb
• pp_bronze_oa_ub
• pp_green_oa
• pp_green_oa_lb
• pp_green_oa_ub

Data on the top universities for Computer Science in 2025.

A comprehensive dataset containing crowdsourced rankings of nearly all ski resorts worldwide. The dataset includes detailed information on each resort, such as location, snowfall, number of lifts and slopes, total slope length, and vertical drop. The dataset is updated regularly as more votes are collected.

A ranking of Office of Hearings Operations (OHO) hearing offices by the average number of hearings dispositions per administrative law judge (ALJ) per day. The average shown will be a combined average for all ALJs working in that hearing office.

Participants' rankings of four alternatives during a citizens' summit. The participants ranked the alternatives twice (ranking1 and ranking 2). In each file the rankings of a group of participants is collected, with a maximum of 10 participants per group. In total we collected the rankings of 61 groups.

Electoral rules can affect who wins, who loses, and how voters feel about the electoral process. Most cities select office holders through plurality rule, but an alternative, ranked choice voting (RCV), has become increasingly popular. RCV requires voters to rank candidates, instead of simply selecting their most preferred candidate. Observers debate whether RCV will cure a variety of electoral ills or undermine representation. We test the effect of RCV on voter’s choices and perceptions of representation using survey experiments with large, representative samples of respondents. We find that candidates of color are significantly penalized in both plurality and RCV elections, with no significant difference between the rule types. However, providing respondents with candidates’ partisan affiliation significantly increases support for candidates of color.

This dataset contains the code for Relevance and Redundancy ranking; a an efficient filter-based feature ranking framework for evaluating relevance based on multi-feature interactions and redundancy on mixed datasets.Source code is in .scala and .sbt format, metadata in .xml, all of which can be accessed and edited in standard, openly accessible text edit software. Diagrams are in openly accessible .png format.Supplementary_2.pdf: contains the results of experiments on multiple classifiers, along with parameter settings and a description of how KLD converges to mutual information based on its symmetricity.dataGenerator.zip: Synthetic data generator inspired from NIPS: Workshop on variable and feature selection (2001), http://www.clopinet.com/isabelle/Projects/NIPS2001/rar-mfs-master.zip: Relevance and Redundancy Framework containing overview diagram, example datasets, source code and metadata. Details on installing and running are provided below.Background. Feature ranking is benfiecial to gain knowledge and to identify the relevant features from a high-dimensional dataset. However, in several datasets, few features by themselves might have small correlation with the target classes, but by combining these features with some other features, they can be strongly correlated with the target. This means that multiple features exhibit interactions among themselves. It is necessary to rank the features based on these interactions for better analysis and classifier performance. However, evaluating these interactions on large datasets is computationally challenging. Furthermore, datasets often have features with redundant information. Using such redundant features hinders both efficiency and generalization capability of the classifier. The major challenge is to efficiently rank the features based on relevance and redundancy on mixed datasets. In the related publication, we propose a filter-based framework based on Relevance and Redundancy (RaR), RaR computes a single score that quantifies the feature relevance by considering interactions between features and redundancy. The top ranked features of RaR are characterized by maximum relevance and non-redundancy. The evaluation on synthetic and real world datasets demonstrates that our approach outperforms several state of-the-art feature selection techniques.# Relevance and Redundancy Framework (rar-mfs) rar-mfs is an algorithm for feature selection and can be employed to select features from labelled data sets. The Relevance and Redundancy Framework (RaR), which is the theory behind the implementation, is a novel feature selection algorithm that - works on large data sets (polynomial runtime),- can handle differently typed features (e.g. nominal features and continuous features), and- handles multivariate correlations.## InstallationThe tool is written in scala and uses the weka framework to load and handle data sets. You can either run it independently providing the data as an .arff or .csv file or you can include the algorithm as a (maven / ivy) dependency in your project. As an example data set we use heart-c. ### Project dependencyThe project is published to maven central (link). To depend on the project use:- maven xml de.hpi.kddm rar-mfs_2.11 1.0.2 - sbt: sbt libraryDependencies += "de.hpi.kddm" %% "rar-mfs" % "1.0.2" To run the algorithm usescalaimport de.hpi.kddm.rar._// ...val dataSet = de.hpi.kddm.rar.Runner.loadCSVDataSet(new File("heart-c.csv", isNormalized = false, "")val algorithm = new RaRSearch( HicsContrastPramsFA(numIterations = config.samples, maxRetries = 1, alphaFixed = config.alpha, maxInstances = 1000), RaRParamsFixed(k = 5, numberOfMonteCarlosFixed = 5000, parallelismFactor = 4))algorithm.selectFeatures(dataSet)### Command line tool- EITHER download the prebuild binary which requires only an installation of a recent java version (>= 6) 1. download the prebuild jar from the releases tab (latest) 2. run java -jar rar-mfs-1.0.2.jar--help Using the prebuild jar, here is an example usage: sh rar-mfs > java -jar rar-mfs-1.0.2.jar arff --samples 100 --subsetSize 5 --nonorm heart-c.arff Feature Ranking: 1 - age (12) 2 - sex (8) 3 - cp (11) ...- OR build the repository on your own: 1. make sure sbt is installed 2. clone repository 3. run sbt run Simple example using sbt directly after cloning the repository: sh rar-mfs > sbt "run arff --samples 100 --subsetSize 5 --nonorm heart-c.arff" Feature Ranking: 1 - age (12) 2 - sex (8) 3 - cp (11) ... ### [Optional]To speed up the algorithm, consider using a fast solver such as Gurobi (http://www.gurobi.com/). Install the solver and put the provided gurobi.jar into the java classpath. ## Algorithm### IdeaAbstract overview of the different steps of the proposed feature selection algorithm:https://github.com/tmbo/rar-mfs/blob/master/docu/images/algorithm_overview.png" alt="Algorithm Overview">The Relevance and Redundancy ranking framework (RaR) is a method able to handle large scale data sets and data sets with mixed features. Instead of directly selecting a subset, a feature ranking gives a more detailed overview into the relevance of the features. The method consists of a multistep approach where we 1. repeatedly sample subsets from the whole feature space and examine their relevance and redundancy: exploration of the search space to gather more and more knowledge about the relevance and redundancy of features 2. decude scores for features based on the scores of the subsets 3. create the best possible ranking given the sampled insights.### Parameters| Parameter | Default value | Description || ---------- | ------------- | ------------|| m - contrast iterations | 100 | Number of different slices to evaluate while comparing marginal and conditional probabilities || alpha - subspace slice size | 0.01 | Percentage of all instances to use as part of a slice which is used to compare distributions || n - sampling itertations | 1000 | Number of different subsets to select in the sampling phase|| k - sample set size | 5 | Maximum size of the subsets to be selected in the sampling phase|