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    • Added Tools; Recreate MethodsList authored by Gallenkamp, Fabian's avatar Gallenkamp, Fabian
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    SoftwareToolsList.asciidoc
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    ......@@ -8,12 +8,14 @@
    == user-consented tracking
    _Collection of sensor data on (mobile) devices in accordance with data protection laws._
    link:Tool_AWARE[AWARE] (link:http://www.awareframework.com/[website] link:https://github.com/denzilferreira/aware-client[repository-android] link:https://github.com/tetujin/aware-client-ios[repository-iOS] link:https://github.com/tetujin/aware-client-osx[repository-OSX] link:https://github.com/denzilferreira/aware-server[repository-server] ):: < Apache-2.0 | framework | Java >
    link:Tool_AWARE[AWARE] (link:http://www.awareframework.com/[website] link:https://github.com/denzilferreira/aware-client[repository-android] link:https://github.com/tetujin/aware-client-ios[repository-iOS] link:https://github.com/tetujin/aware-client-osx[repository-OSX] link:https://github.com/denzilferreira/aware-server[repository-server] ):: 'AWARE is an Android framework dedicated to instrument, infer, log and share mobile context information, for application developers, researchers and smartphone users. AWARE captures hardware-, software-, and human-based data. The data is then analyzed using AWARE plugins. They transform data into information you can understand.' link:http://www.awareframework.com/what-is-aware/[Source, visited: 27.02.2019] < Apache-2.0 | framework | Java >
    link:Tool_MEILI[MEILI] (link:https://github.com/Badger-MEILI[repository-group] ):: < GPL-3.0 | framework | Java >
    link:Tool_MEILI[MEILI] (link:http://adrianprelipcean.github.io/[website-dev] link:https://github.com/Badger-MEILI[repository-group] ):: < GPL-3.0 | framework | Java >
    link:Tool_PassiveDataKit[Passive Data Kit] (link:https://passivedatakit.org/[website] link:https://github.com/audaciouscode/PassiveDataKit-Django[repository-djangoserver] link:https://github.com/audaciouscode/PassiveDataKit-Android[repository-android] link:https://github.com/audaciouscode/PassiveDataKit-iOS[repository-iOS] ):: < Apache-2.0 | framework | Python Java >
    link:Tool_WebHistorian-CommunityEdition[Web Historian - Community Edition] (link:https://doi.org/10.5281/zenodo.1322782[website-doi] link:http://www.webhistorian.org[website] link:https://github.com/WebHistorian/community[repository] ):: Chrome browser extension designed to integrate web browsing history data collection into research projects collecting other types of data from participants (e.g. surveys, in-depth interviews, experiments). It uses client-side D3 visualizations to inform participants about the data being collected during the informed consent process. It allows participants to delete specific browsing data or opt-out of browsing data collection. It directs participants to an online survey once they have reviewed their data and made a choice of whether to participate. It has been used with Qualtrics surveys, but any survey that accepts data from a URL will work. It works with the open source Passive Data Kit (PDK) as the backend for data collection. To successfully upload, you need to fill in the address of your PDK server in the js/app/config.js file. < GPL-3.0 | plugin | Javascript >
    == scraping
    _Tools in the area of web-scraping_
    ......@@ -28,11 +30,11 @@ link:Tool_RSelenium[RSelenium] (link:https://github.com/ropensci/RSelenium[repos
    == tools for corpus linguistics
    _Integrated platforms for corpus analysis and processing._
    link:Tool_AmCAT[AmCAT] (link:http://vanatteveldt.com/amcat/[website-entwickler] link:https://github.com/amcat/amcat[repository] link:http://wiki.amcat.nl/3.4:AmCAT_Navigator_3[wiki] ):: The Amsterdam Content Analysis Toolkit (AmCAT) is an open source infrastructure that makes it easy to do large-scale automatic and manual content analysis (text analysis) for the social sciences and humanities. < AGPL-3.0 | SaaS | Python >
    link:Tool_AmCAT[AmCAT] (link:http://vanatteveldt.com/amcat/[website-entwickler] link:https://github.com/amcat/amcat[repository] link:http://wiki.amcat.nl/3.4:AmCAT_Navigator_3[wiki] ):: 'The Amsterdam Content Analysis Toolkit (AmCAT) is an open source infrastructure that makes it easy to do large-scale automatic and manual content analysis (text analysis) for the social sciences and humanities.' < AGPL-3.0 | SaaS | Python >
    link:Tool_COSMOS[COSMOS] (link:http://socialdatalab.net/COSMOS[website] ):: COSMOS Open Data Analytics software < Proprietary | standalone | >
    link:Tool_CWB[CWB] (link:http://cwb.sourceforge.net/index.php[website] link:http://svn.code.sf.net/p/cwb/code/cwb/trunk[repository-cwb] link:http://svn.code.sf.net/p/cwb/code/gui/cqpweb/trunk[repository-cqpweb] ):: a fast, powerful and extremely flexible corpus querying system < GPL-3.0 | framework | C >
    link:Tool_CWB[CWB] (link:http://cwb.sourceforge.net/index.php[website] link:http://svn.code.sf.net/p/cwb/code/cwb/trunk[repository-cwb] link:http://svn.code.sf.net/p/cwb/code/gui/cqpweb/trunk[repository-cqpweb] ):: CWB, the IMS[Institut für Maschinelle Sprachverarbeitung Stuttgart] Open Corpus Workbench is 'a fast, powerful and extremely flexible corpus querying system.' < GPL-3.0 | framework | C >
    link:Tool_LCM[LCM] (link:http://lcm.informatik.uni-leipzig.de/generic.html[website] ):: Leipzig Corpus Miner a decentralized SaaS application for the analysis of very large amounts of news texts < LGPL | framework | Java R >
    ......@@ -44,9 +46,9 @@ _assist with qualitative research such as transcription analysis, coding and tex
    link:Tool_ATLAS.ti[ATLAS.ti] (link:https://atlasti.com/de/produkt/what-is-atlas-ti/[website] ):: < Proprietary | standalone | >
    link:Tool_Leximancer[Leximancer] (link:https://info.leximancer.com/[website] ):: Leximancer automatically analyses your text documents to identify the high level concepts in your text documents, delivering the key ideas and actionable insights you need with powerful interactive visualisations and data exports. < Proprietary | standalone | >
    link:Tool_Leximancer[Leximancer] (link:https://info.leximancer.com/[website] ):: 'Leximancer automatically analyses your text documents to identify the high level concepts in your text documents, delivering the key ideas and actionable insights you need with powerful interactive visualisations and data exports.' < Proprietary | standalone | >
    link:Tool_MAXQDA[MAXQDA] (link:https://www.rrz.uni-hamburg.de/services/software/alphabetisch/maxqda.html[website-uhh] ):: < Proprietary | standalone | >
    link:Tool_MAXQDA[MAXQDA] (link:https://www.rrz.uni-hamburg.de/services/software/alphabetisch/maxqda.html[website-uhh] ):: 'MAXQDA gehört zu den weltweit führenden und umfangreichsten QDA-Software-Programmen im Bereich der Qualitativen und Mixed-Methods-Forschung. Die Software hilft Ihnen beim Erfassen, Organisieren, Analysieren, Visualisieren und Veröffentlichen Ihrer Daten. Ob Grounded Theory, Literaturreview, explorative Marktforschung, Interviews, Webseitenanalyse oder Surveys: Analysieren Sie was Sie wollen, wie Sie wollen.MAXQDA Analytics Pro ist die erweiterte Version von MAXQDA und enthält neben allen Funktionen für die Qualitative & Mixed Methods-Forschung auch ein Modul für die quantitative Textanalyse (MAXDictio) und ein Modul für die statistische Datenanalyse (MAXQDA Stats)'link:https://www.rrz.uni-hamburg.de/services/software/alphabetisch/maxqda.html[Source, visited: 27.02.2019] < Proprietary | standalone | >
    link:Tool_NVivo[NVivo] (link:https://www.qsrinternational.com/nvivo/who-uses-nvivo/academics[website] ):: < Proprietary | standalone | >
    ......@@ -56,37 +58,37 @@ link:Tool_ORAPro[ORA Pro] (link:http://netanomics.com/[website] ):: < Proprieta
    link:Tool_Quirkos[Quirkos] (link:https://www.quirkos.com/[website] link:[repository] ):: < Proprietary | standalone | >
    link:Tool_RQDA[RQDA] (link:http://rqda.r-forge.r-project.org/[website] link:https://github.com/Ronggui/RQDA[repository] ):: It includes a number of standard Computer-Aided Qualitative Data Analysis features. In addition it seamlessly integrates with R, which means that a) statistical analysis on the coding is possible, and b) functions for data manipulation and analysis can be easily extended by writing R functions. To some extent, RQDA and R make an integrated platform for both quantitative and qualitative data analysis. < BSD | package | R >
    link:Tool_RQDA[RQDA] (link:http://rqda.r-forge.r-project.org/[website] link:https://github.com/Ronggui/RQDA[repository] ):: 'It includes a number of standard Computer-Aided Qualitative Data Analysis features. In addition it seamlessly integrates with R, which means that a) statistical analysis on the coding is possible, and b) functions for data manipulation and analysis can be easily extended by writing R functions. To some extent, RQDA and R make an integrated platform for both quantitative and qualitative data analysis.' < BSD | package | R >
    link:Tool_TAMS[TAMS] (link:https://sourceforge.net/projects/tamsys[website] ):: Text Analysis Markup System (TAMS) is both a system of marking documents for qualitative analysis and a series of tools for mining information based on that syntax. < GPL-2.0 | standalone | >
    link:Tool_TAMS[TAMS] (link:https://sourceforge.net/projects/tamsys[website] ):: 'Text Analysis Markup System (TAMS) is both a system of marking documents for qualitative analysis and a series of tools for mining information based on that syntax.' < GPL-2.0 | standalone | >
    == text mining/natuaral language processing(NLP)
    __
    link:Tool_ApacheOpenNLP[Apache OpenNLP] (link:https://opennlp.apache.org/[website] link:[repository] ):: OpenNLP supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, language detection and coreference resolution. < Apache-2.0 | package | Java >
    link:Tool_ApacheOpenNLP[Apache OpenNLP] (link:https://opennlp.apache.org/[website] link:[repository] ):: 'OpenNLP supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, language detection and coreference resolution.' < Apache-2.0 | package | Java >
    link:Tool_GATE[GATE] (link:https://gate.ac.uk/overview.html[website] link:https://github.com/GateNLP/gate-core[repository] ):: GATE - General Architecture for Text Engineering < LGPL | package | Java >
    link:Tool_Gensim[Gensim] (link:https://pypi.org/project/gensim/[website] link:[repository] ):: Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community. < LGPL | package | Python >
    link:Tool_Gensim[Gensim] (link:https://pypi.org/project/gensim/[website] link:[repository] ):: 'Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.' < LGPL | package | Python >
    link:Tool_NLTK[NLTK] (link:http://www.nltk.org/index.html[website] link:https://github.com/nltk/nltk[repository] ):: NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum. < Apache-2.0 | package | Python >
    link:Tool_NLTK[NLTK] (link:http://www.nltk.org/index.html[website] link:https://github.com/nltk/nltk[repository] ):: 'NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum.' < Apache-2.0 | package | Python >
    link:Tool_Pandas[Pandas] (link:http://pandas.pydata.org/[website] link:https://github.com/pandas-dev/pandas[repository] ):: < BSD | package | Python >
    link:Tool_polmineR[polmineR] (link:https://cran.r-project.org/package=polmineR[website-cran] link:https://github.com/PolMine/polmineR[repository] ):: < GPL-3.0 | package | R >
    link:Tool_quanteda[quanteda] (link:https://quanteda.io/[website] link:https://github.com/quanteda/quanteda[repository] ):: The package is designed for R users needing to apply natural language processing to texts, from documents to final analysis. Its capabilities match or exceed those provided in many end-user software applications, many of which are expensive and not open source. The package is therefore of great benefit to researchers, students, and other analysts with fewer financial resources. While using quanteda requires R programming knowledge, its API is designed to enable powerful, efficient analysis with a minimum of steps. By emphasizing consistent design, furthermore, quanteda lowers the barriers to learning and using NLP and quantitative text analysis even for proficient R programmers. < GPL-3.0 | package | R >
    link:Tool_quanteda[quanteda] (link:https://quanteda.io/[website] link:https://github.com/quanteda/quanteda[repository] ):: 'The package is designed for R users needing to apply natural language processing to texts, from documents to final analysis. Its capabilities match or exceed those provided in many end-user software applications, many of which are expensive and not open source. The package is therefore of great benefit to researchers, students, and other analysts with fewer financial resources. While using quanteda requires R programming knowledge, its API is designed to enable powerful, efficient analysis with a minimum of steps. By emphasizing consistent design, furthermore, quanteda lowers the barriers to learning and using NLP and quantitative text analysis even for proficient R programmers.' < GPL-3.0 | package | R >
    link:Tool_RapidMiner[RapidMiner] (link:https://rapidminer.com/[website] link:https://github.com/rapidminer/rapidminer-studio[repository] ):: < AGPL-3.0 | framework | Java >
    link:Tool_spaCy[spaCy] (link:https://spacy.io/[website] link:https://github.com/explosion/spaCy[repository] ):: spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. Independent research has confirmed that spaCy is the fastest in the world. If your application needs to process entire web dumps, spaCy is the library you want to be using. < MIT | package | Cython >
    link:Tool_spaCy[spaCy] (link:https://spacy.io/[website] link:https://github.com/explosion/spaCy[repository] ):: spaCy 'excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. Independent research has confirmed that spaCy is the fastest in the world. If your application needs to process entire web dumps, spaCy is the library you want to be using.' < MIT | package | Cython >
    link:Tool_StanfordCoreNLP[Stanford CoreNLP] (link:https://stanfordnlp.github.io/CoreNLP/[website] link:https://github.com/stanfordnlp/CoreNLP[repository] ):: < GPL-3.0 | framework | Java >
    link:Tool_tm[tm] (link:http://tm.r-forge.r-project.org/[website] link:https://cran.r-project.org/package=tm[website-cran] link:[repository] ):: < GPL-3.0 | package | R >
    link:Tool_xtas[xtas] (link:http://nlesc.github.io/xtas/[website] link:https://github.com/NLeSC/xtas[repository] ):: the eXtensible Text Analysis Suite(xtas) is a collection of natural language processing and text mining tools, brought together in a single software package with built-in distributed computing and support for the Elasticsearch document store. < Apache-2.0 | framework | Python >
    link:Tool_xtas[xtas] (link:http://nlesc.github.io/xtas/[website] link:https://github.com/NLeSC/xtas[repository] ):: the eXtensible Text Analysis Suite(xtas) 'is a collection of natural language processing and text mining tools, brought together in a single software package with built-in distributed computing and support for the Elasticsearch document store.' < Apache-2.0 | framework | Python >
    == topic-models
    ......@@ -94,33 +96,35 @@ __
    link:Tool_MALLET[MALLET] (link:http://mallet.cs.umass.edu/[website] link:https://github.com/mimno/Mallet[repository] ):: < Apache-2.0 | package | Java >
    link:Tool_TOME[TOME] (link:https://dhlab.lmc.gatech.edu/tome/[website] link:https://github.com/GeorgiaTechDHLab/TOME/[repository] ):: TOME is a tool to support the interactive exploration and visualization of text-based archives, supported by a Digital Humanities Startup Grant from the National Endowment for the Humanities (Lauren Klein and Jacob Eisenstein, co-PIs). Drawing upon the technique of topic modeling—a machine learning method for identifying the set of topics, or themes, in a document set—our tool allows humanities scholars to trace the evolution and circulation of these themes across networks and over time. < Unknown | package | Python Jupyter Notebook >
    link:Tool_TOME[TOME] (link:https://dhlab.lmc.gatech.edu/tome/[website] link:https://github.com/GeorgiaTechDHLab/TOME/[repository] ):: 'TOME is a tool to support the interactive exploration and visualization of text-based archives, supported by a Digital Humanities Startup Grant from the National Endowment for the Humanities (Lauren Klein and Jacob Eisenstein, co-PIs). Drawing upon the technique of topic modeling—a machine learning method for identifying the set of topics, or themes, in a document set—our tool allows humanities scholars to trace the evolution and circulation of these themes across networks and over time.' < Unknown | package | Python Jupyter Notebook >
    link:Tool_Stm[Stm] (link:http://structuraltopicmodel.com[website] link:https://github.com/bstewart/stm[repository] ):: The Structural Topic Model (STM) allows researchers to estimate topic models with document-level covariates. The package also includes tools for model selection, visualization, and estimation of topic-covariate regressions. Methods developed in Roberts et al (2014) <doi:10.1111/ajps.12103> and Roberts et al (2016) <doi:10.1080/01621459.2016.1141684>. < MIT | package | R >
    link:Tool_Stm[Stm] (link:http://structuraltopicmodel.com[website] link:https://github.com/bstewart/stm[repository] ):: 'The Structural Topic Model (STM) allows researchers to estimate topic models with document-level covariates. The package also includes tools for model selection, visualization, and estimation of topic-covariate regressions. Methods developed in Roberts et al (2014) <doi:10.1111/ajps.12103> and Roberts et al (2016) <doi:10.1080/01621459.2016.1141684>.' < MIT | package | R >
    == sentiment analysis
    __
    link:Tool_lexicoder[lexicoder] (link:http://www.lexicoder.com/index.html[website] ):: Lexicoder performs simple deductive content analyses of any kind of text, in almost any language. All that is required is the text itself, and a dictionary. Our own work initially focused on the analysis of newspaper stories during election campaigns, and both television and newspaper stories about public policy issues. The software can deal with almost any text, however, and lots of it. Our own databases typically include up to 100,000 news stories. Lexicoder processes these data, even with a relatively complicated coding dictionary, in about fifteen minutes. The software has, we hope, a wide range of applications in the social sciences. It is not the only software that conducts content analysis, of course - there are many packages out there, some of which are much more sophisticated than this one. The advantage to Lexicoder, however, is that it can run on any computer with a recent version of Java (PC or Mac), it is very simple to use, it can deal with huge bodies of data, it can be called from R as well as from the Command Line, and its free. < Proprietary | package | Java >
    link:Tool_lexicoder[lexicoder] (link:http://www.lexicoder.com/index.html[website] ):: 'Lexicoder performs simple deductive content analyses of any kind of text, in almost any language. All that is required is the text itself, and a dictionary. Our own work initially focused on the analysis of newspaper stories during election campaigns, and both television and newspaper stories about public policy issues. The software can deal with almost any text, however, and lots of it. Our own databases typically include up to 100,000 news stories. Lexicoder processes these data, even with a relatively complicated coding dictionary, in about fifteen minutes. The software has, we hope, a wide range of applications in the social sciences. It is not the only software that conducts content analysis, of course - there are many packages out there, some of which are much more sophisticated than this one. The advantage to Lexicoder, however, is that it can run on any computer with a recent version of Java (PC or Mac), it is very simple to use, it can deal with huge bodies of data, it can be called from R as well as from the Command Line, and its free.' < Proprietary | package | Java >
    link:Tool_OpinionFinder[OpinionFinder] (link:https://mpqa.cs.pitt.edu/opinionfinder/[website] link:[repository] ):: OpinionFinder is a system that processes documents and automatically identifies subjective sentences as well as various aspects of subjectivity within sentences, including agents who are sources of opinion, direct subjective expressions and speech events, and sentiment expressions. OpinionFinder was developed by researchers at the University of Pittsburgh, Cornell University, and the University of Utah. In addition to OpinionFinder, we are also releasing the automatic annotations produced by running OpinionFinder on a subset of the Penn Treebank. < Unknown | package | Java >
    link:Tool_OpinionFinder[OpinionFinder] (link:https://mpqa.cs.pitt.edu/opinionfinder/[website] link:[repository] ):: 'OpinionFinder is a system that processes documents and automatically identifies subjective sentences as well as various aspects of subjectivity within sentences, including agents who are sources of opinion, direct subjective expressions and speech events, and sentiment expressions. OpinionFinder was developed by researchers at the University of Pittsburgh, Cornell University, and the University of Utah. In addition to OpinionFinder, we are also releasing the automatic annotations produced by running OpinionFinder on a subset of the Penn Treebank.' < Unknown | package | Java >
    link:Tool_Readme[Readme] (link:https://gking.harvard.edu/readme[website] ):: The ReadMe software package for R takes as input a set of text documents (such as speeches, blog posts, newspaper articles, judicial opinions, movie reviews, etc.), a categorization scheme chosen by the user (e.g., ordered positive to negative sentiment ratings, unordered policy topics, or any other mutually exclusive and exhaustive set of categories), and a small subset of text documents hand classified into the given categories. < CC BY-NC-ND-3.0 | package | R >
    link:Tool_Readme[Readme] (link:https://gking.harvard.edu/readme[website] ):: 'The ReadMe software package for R takes as input a set of text documents (such as speeches, blog posts, newspaper articles, judicial opinions, movie reviews, etc.), a categorization scheme chosen by the user (e.g., ordered positive to negative sentiment ratings, unordered policy topics, or any other mutually exclusive and exhaustive set of categories), and a small subset of text documents hand classified into the given categories.' < CC BY-NC-ND-3.0 | package | R >
    == visualization
    __
    link:Tool_Gephi[Gephi] (link:https://gephi.org/[website] link:https://github.com/gephi/gephi/[repository] ):: Gephi is an award-winning open-source platform for visualizing and manipulating large graphs. < GPL-3.0 | package | Java >
    link:Tool_Gephi[Gephi] (link:https://gephi.org/[website] link:https://github.com/gephi/gephi/[repository] ):: 'Gephi is an award-winning open-source platform for visualizing and manipulating large graphs.' < GPL-3.0 | package | Java >
    link:Tool_sigma.js[sigma.js] (link:http://sigmajs.org/[website] link:https://github.com/jacomyal/sigma.js[repository] ):: 'Sigma is a JavaScript library dedicated to graph drawing. It makes easy to publish networks on Web pages, and allows developers to integrate network exploration in rich Web applications.' < MIT | package | Javascript >
    link:Tool_scikit-image[scikit-image] (link:https://scikit-image.org/[website] link:https://github.com/scikit-image/scikit-image[repository] ):: scikit-image is a collection of algorithms for image processing. It is available free of charge and free of restriction. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers. < BSD | package | Python >
    link:Tool_scikit-image[scikit-image] (link:https://scikit-image.org/[website] link:https://github.com/scikit-image/scikit-image[repository] ):: 'scikit-image is a collection of algorithms for image processing. It is available free of charge and free of restriction. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers.' < BSD | package | Python >
    == collaborative annotation
    __
    link:Tool_CATMA[CATMA] (link:http://catma.de/[website] link:https://www.slm.uni-hamburg.de/germanistik/forschung/forschungsprojekte/catma.html[website-uhh] link:https://github.com/mpetris/catma[repository] ):: CATMA (Computer Assisted Text Markup and Analysis) is a practical and intuitive tool for text researchers. In CATMA users can combine the hermeneutic, ‘undogmatic’ and the digital, taxonomy based approach to text and corpora—as a single researcher, or in real-time collaboration with other team members. < Apache-2.0 | package | Python >
    link:Tool_CATMA[CATMA] (link:http://catma.de/[website] link:https://www.slm.uni-hamburg.de/germanistik/forschung/forschungsprojekte/catma.html[website-uhh] link:https://github.com/mpetris/catma[repository] ):: 'CATMA (Computer Assisted Text Markup and Analysis) is a practical and intuitive tool for text researchers. In CATMA users can combine the hermeneutic, ‘undogmatic’ and the digital, taxonomy based approach to text and corpora—as a single researcher, or in real-time collaboration with other team members.' < Apache-2.0 | package | Python >
    link:Tool_WebAnno[WebAnno] (link:https://webanno.github.io/webanno/[website] link:https://github.com/webanno/webanno[repository] ):: WebAnno is a multi-user tool supporting different roles such as annotator, curator, and project manager. The progress and quality of annotation projects can be monitored and measuered in terms of inter-annotator agreement. Multiple annotation projects can be conducted in parallel. < Apache-2.0 | package | Python >
    ......@@ -158,7 +162,7 @@ link:Tool_Nowcasting[Nowcasting] (link:https://cran.r-project.org/package=nowcas
    == network analysis
    _social network analysis_
    link:Tool_AutoMap[AutoMap] (link:http://www.casos.cs.cmu.edu/projects/automap/software.php[website] ):: AutoMap enables the extraction of information from texts using Network Text Analysis methods. AutoMap supports the extraction of several types of data from unstructured documents. The type of information that can be extracted includes: content analytic data (words and frequencies), semantic network data (the network of concepts), meta-network data (the cross classification of concepts into their ontological category such as people, places and things and the connections among these classified concepts), and sentiment data (attitudes, beliefs). Extraction of each type of data assumes the previously listed type of data has been extracted. < Proprietary | package | Java >
    link:Tool_AutoMap[AutoMap] (link:http://www.casos.cs.cmu.edu/projects/automap/software.php[website] ):: 'AutoMap enables the extraction of information from texts using Network Text Analysis methods. AutoMap supports the extraction of several types of data from unstructured documents. The type of information that can be extracted includes: content analytic data (words and frequencies), semantic network data (the network of concepts), meta-network data (the cross classification of concepts into their ontological category such as people, places and things and the connections among these classified concepts), and sentiment data (attitudes, beliefs). Extraction of each type of data assumes the previously listed type of data has been extracted.' < Proprietary | package | Java >
    link:Tool_NodeXL[NodeXL] (link:https://www.smrfoundation.org/nodexl/[website] ):: < Proprietary | package | >
    ......@@ -166,9 +170,9 @@ link:Tool_ORAPro[ORA Pro] (link:[website] link:[repository] ):: < Proprietary |
    link:Tool_Pajek[Pajek] (link:http://mrvar.fdv.uni-lj.si/pajek/[website] ):: < Proprietary | package | >
    link:Tool_NetworkX[NetworkX] (link:[website] link:[repository] ):: Data structures for graphs, digraphs, and multigraphs Many standard graph algorithms Network structure and analysis measures Generators for classic graphs, random graphs, and synthetic networks Nodes can be 'anything' (e.g., text, images, XML records) Edges can hold arbitrary data (e.g., weights, time-series) Open source 3-clause BSD license Well tested with over 90% code coverage Additional benefits from Python include fast prototyping, easy to teach, and multi-platform < BSD | package | Python >
    link:Tool_NetworkX[NetworkX] (link:[website] link:[repository] ):: 'Data structures for graphs, digraphs, and multigraphs Many standard graph algorithms Network structure and analysis measures Generators for classic graphs, random graphs, and synthetic networks Nodes can be 'anything' (e.g., text, images, XML records) Edges can hold arbitrary data (e.g., weights, time-series) Open source 3-clause BSD license Well tested with over 90% code coverage Additional benefits from Python include fast prototyping, easy to teach, and multi-platform.' < BSD | package | Python >
    link:Tool_UCINET[UCINET] (link:https://sites.google.com/site/ucinetsoftware/home[website] ):: UCINET 6 for Windows is a software package for the analysis of social network data. It was developed by Lin Freeman, Martin Everett and Steve Borgatti. It comes with the NetDraw network visualization tool. < Proprietary | package | >
    link:Tool_UCINET[UCINET] (link:https://sites.google.com/site/ucinetsoftware/home[website] ):: 'UCINET 6 for Windows is a software package for the analysis of social network data. It was developed by Lin Freeman, Martin Everett and Steve Borgatti. It comes with the NetDraw network visualization tool.' < Proprietary | package | >
    == search
    ......@@ -188,23 +192,31 @@ _software that converts speech into electronic text document._
    link:Tool_f4analyse[f4analyse] (link:https://www.audiotranskription.de/f4-analyse[website] ):: < Proprietary | standalone | >
    link:Tool_EXMARaLDA[EXMARaLDA] (link:[website] link:https://github.com/EXMARaLDA/exmaralda[repository] ):: EXMARaLDA ist ein System für das computergestützte Arbeiten mit (vor allem) mündlichen Korpora. Es besteht aus einem Transkriptions- und Annotationseditor (Partitur-Editor), einem Tool zum Verwalten von Korpora (Corpus-Manager) und einem Such- und Analysewerkzeug (EXAKT). < Unknown | framework | Java >
    link:Tool_EXMARaLDA[EXMARaLDA] (link:[website] link:https://github.com/EXMARaLDA/exmaralda[repository] ):: 'EXMARaLDA ist ein System für das computergestützte Arbeiten mit (vor allem) mündlichen Korpora. Es besteht aus einem Transkriptions- und Annotationseditor (Partitur-Editor), einem Tool zum Verwalten von Korpora (Corpus-Manager) und einem Such- und Analysewerkzeug (EXAKT).' < Unknown | framework | Java >
    == optical character recognition (OCR)
    _OCR is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text._
    link:Tool_tesseract[tesseract] (link:https://github.com/tesseract-ocr/tesseract[repository] ):: Tesseract is an open source text recognizer (OCR) Engine, available under the Apache 2.0 license. It can be used directly, or (for programmers) using an API to extract printed text from images. It supports a wide variety of languages. < Apache-2.0 | package | Python >
    link:Tool_tesseract[tesseract] (link:https://github.com/tesseract-ocr/tesseract[repository] ):: 'Tesseract is an open source text recognizer (OCR) Engine, available under the Apache 2.0 license. It can be used directly, or (for programmers) using an API to extract printed text from images. It supports a wide variety of languages.' < Apache-2.0 | package | Python >
    == online experiments
    __
    link:Tool_nodeGame[nodeGame] (link:https://nodegame.org/[website] link:https://github.com/nodeGame[repository] ):: NodeGame is a free, open source JavaScript/HTML5 framework for conducting synchronous experiments online and in the lab directly in the browser window. It is specifically designed to support behavioral research along three dimensions: larger group sizes, real-time (but also discrete time) experiments, batches of simultaneous experiments. < MIT | package | Javascript >
    link:Tool_nodeGame[nodeGame] (link:https://nodegame.org/[website] link:https://github.com/nodeGame[repository] ):: 'NodeGame is a free, open source JavaScript/HTML5 framework for conducting synchronous experiments online and in the lab directly in the browser window. It is specifically designed to support behavioral research along three dimensions: larger group sizes, real-time (but also discrete time) experiments, batches of simultaneous experiments.' < MIT | package | Javascript >
    == miscellaneous
    __
    link:Tool_scikit-learn[scikit-learn] (link:https://scikit-learn.org/stable/index.html[website] link:https://github.com/scikit-learn/scikit-learn[repository] ):: Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. < BSD | package | Python >
    link:Tool_scikit-learn[scikit-learn] (link:https://scikit-learn.org/stable/index.html[website] link:https://github.com/scikit-learn/scikit-learn[repository] ):: 'Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.' < BSD | package | Python >
    link:Tool_spades[spades] (link:http://spades.predictiveecology.org/[website] link:[repository] ):: < None | package | R >
    == Agent-based modeling
    __
    link:Tool_NetLogo[NetLogo] (link:http://ccl.northwestern.edu/netlogo/[website] link:https://github.com/NetLogo/NetLogo[repository] ):: 'NetLogo is a multi-agent programmable modeling environment. It is used by many tens of thousands of students, teachers and researchers worldwide. It also powers HubNet participatory simulations.' < Unknown | package | Java Scala >