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Open Call for Data Analysts! - Please enter your name, email address, and first ideas as well as experience (could also be added later) until April 21st. We will contact you for the next steps.
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NameEmail-AddressIdeas for analysesYour experience with the suggested analyses/methods
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Helena Hartmann
helena.m.hartmann@gmail.com
We could do association analyses using the R package associatoR and word clouds
no experience yet but did a theoretical course on thematic analysis and happy to learn! I am experienced in R and can make nice plots 😁
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Vaitsa Giannouligiannouliv@hotmail.com
I have no prior experience, but it seems that if you have access to Atlas.ti, maybe qualitative analyses could also be performed using it.
No experience at all (but if you show me what is requested with detailed guidelines for R, I'll try to assist)!
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Christopher J. Graham
christopherjgraham93@gmail.com
None additionalNot with suggested analyses but have applied thematic analysis
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David Grüning
david.gruening@psychologie.uni-heidelberg.de
As far as I'm concerned, topic modeling is suited for extracting overarching topics, while text network analysis is more interesting to identify specific co-occurences of single relevant words/concepts. These two different applications might help us generating different perspectives on how to analyze the corpus.

So as a rather broad idea to combine both approaches' strengths: We could first identify overarching topics depending on the time phase (i.e., across publication years); for example, showing that from 2015 onwards the overarching topic X became substantially more relevant. This we could do via topic modeling. Afterwards within each of these identified overarching topics from topic modeling, we could use text network analysis to understand the interconnections of specific subtopics within each of these identified overarching topics. Lastly, we could then even analyze how interconnections between these subtopics change over time (i.e., while an overarching topic might be present in the last 20 years, it might have had changing qualities over these times, represented as subtopics being differently interconnected across these years).

This would allow for a multilevel perspective on OS topical time shifts (super excited to do this)!
Have done different projects with text network analysis (& some experience with topic modeling, mostly from more quantiative cluster analysis approaches).
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Steven Verheyenverheyen@essb.eur.nl
Multidimensional scaling and/or additive clustering on similarity between sources (e.g., based on shared keywords) to uncover latent dimensions or clusters
I am proficient in mds, e.g. using smacof in R
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Bradley Baker
bradley.baker@temple.edu
None yet, although I'll give it some thought!
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Mathy Adrienamathy@uliege.besemantic graph ? As a linguist I know some tools but I have a lot of to learn
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Heather Rollinshedwyg@pm.me
I live more on the statistical side, and I've got skills in R and SPSS (and SAS, but I don't have access to it right now, and it's costly!). I've done some reading in textual analysis, and I'm always up for learning new methods.
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Elizabeth Perkovich
elizabethsperkovich@gmail.com
No prior experience, just looking to learn (and open to learning fast in order to help!)
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Dustin Haradendharaden@gmail.com
No previous text analyses, but have always wanted to learn. I am very comfortable with R and would love to gain knowledge in this area and support the project
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Shanu Shukla
shanu.shukla11@gmail.com
regression and correlation analysis, Geospatial analysis, temporal analysis
No prior experience in linguistic analysis but work on quantitative data analysis on SPSS, qualitative analysis on nvivo and open to acquire new skill for it.
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Amanda Woodwardwoodw284@umn.eduNo prior experience, but very interested in learning. I've got decent R skills
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Cassie Lemlemcassa@msu.edu
Topic modeling using Latent Dirichlet Allocation, word embeddings
No prior experience, but I'm experienced in Python and have some idea of general NLP workflows and common Python libraries for NLP. I'm eager to learn and really interested in contributing to this!
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Julie Kirwanj.a.kirwan@rug.nlPrinciple component analysis
I'm experienced in Matlab and R, with some Python skills. Experience in growth curve analysis and regression. No experience with PCA or suggested analyses but curious to try it out.
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Franziska McManusz.mcmanus@ed.ac.uk
Experience analysing and visualising data in R. Have read up on qualitative methods (e.g. thematic analysis) and would like to learn more!
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Mary DePascale
mary.depascale@bc.edu; mary.m.depascale@gmail.com
No prior experience, but some experience with systematic review and thematic analysis of qualitative data, experience with R
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Antony Casula
antonycasula@gmail.com
1 year experience in R and Matlab, 4 years in Jamovi for research activity. Main experience in quantitative analysis. Familiarity with General (and Generalized) Linear (and mixed) Models.
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Belay Moges
belaynathanem@gmail.com
Conduct content validation, CFA, and EFA Statistical Analysis to validate instruments either developed or adapted to measure psychological constructs. Participated in the London, Ottawa and Madrid Replication studies organized by I4R, and experience in handling the required procedures to be held in the entire instrument validation, contextualization, adaptation, and generation to develop measures
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David Moreau
d.moreau@auckland.ac.nz
Experience with a number of methods/analyses - R/Python
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Pablo Ezequiel Flores Kanter
ezequielfk@gmail.comExperience with R
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