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Replication files for "The role of actors' issue and sector specialization for policy integration in the parliamentary arena: An analysis of Swiss biodiversity policy using text as data"
<p>The ZIP file contains all data and code to replicate the analyses reported in the following paper.</p> <p>Reber, U., Ingold, K., & Fischer, M. (2023). The role of actors' issue and sector specialization for policy integration in the parliamentary arena: An analysis of Swiss biodiversity policy using text as data. <em>Policy Sciences</em>. <a href="https://doi.org/10.1007/s11077-022-09490-2">https://doi.org/10.1007/s11077-022-09490-2</a></p> <p>If you use any of the material included in this repository, please refer to the paper.</p>
DARIAH Campus Geographical Text Analysis Tutorial - Resources
<p>These datasets are part of the Geographical Text Analysis tutorial hosted on DARIAH-Campus, and produced as part of the GeoHumanities Working Group activities. </p>
PHYLIP format text files for BioGeoBEARS analysis. MSc thesis, University of Edinburgh.
<p>PHYLIP format text files for BioGeoBEARS analysis for each dataset. MSc thesis, University of Edinburgh.</p>
A Comparative Analysis of Temporal Long Text Similarity: Application to Financial Documents
<p>Textual Similarity on MD&A disclosure</p>
text-fig. 52. wo selected reduced consensus trees from the analysis of the complete data set. a, after deletion of 'Chilantaisaurus' maortuensis. B, after deletion of Proceratosaurus and Xuanhanosaurus. in The interrelationships and evolution of basal theropod dinosaurs
text-fig. 52. wo selected reduced consensus trees from the analysis of the complete data set. a, after deletion of 'Chilantaisaurus' maortuensis. B, after deletion of Proceratosaurus and Xuanhanosaurus.
text-fig. 53. Strict consensus tree resulting from the analysis of the pruned data matrix with 51 taxa. Numbers at the nodes indicate bootstrap support values in branches that have more than 50 per cent support. The consensus tree is based on 5544 trees of 652 steps (CI 0-42, RI 0-748, RCI 0-314). in The interrelationships and evolution of basal theropod dinosaurs
text-fig. 53. Strict consensus tree resulting from the analysis of the pruned data matrix with 51 taxa. Numbers at the nodes indicate bootstrap support values in branches that have more than 50 per cent support. The consensus tree is based on 5544 trees of 652 steps (CI 0-42, RI 0-748, RCI 0-314).
BHAAV (भाव) - A Text Corpus for Emotion Analysis from Hindi Stories
<p>The first and largest Hindi text corpus, named BHAAV (भाव), which means emotions in Hindi, for analyzing emotions that a writer expresses through his characters in a story, as perceived by a narrator/reader. The corpus consists of 20,304 sentences collected from 230 different short stories spanning across 18 genres such as प्रेरणादायक (Inspirational) and रहस्यमयी (Mystery). Each sentence has been annotated into one of the five emotion categories anger, joy, suspense, sad, and neutral) by three native Hindi speakers with at least ten years of formal education in Hindi.</p>
MHG4SNA: Middle high german texts annotated for social network analysis
<p><strong>Description</strong></p> <p>This corpus contains multiple middle high german texts with annotations for social network analysis. It contains annotations of: named entities and entity mentions (including partial coreference resolution), direct speech, narrator's comments. See below for further description.</p> <p>The annotated texts are part of my dissertation on social network analysis of arthurian romances. The research was developed in the context of the DH center <a href="https://www.creta.uni-stuttgart.de/">CRETA</a> at the University of Stuttgart.</p> <p> </p> <p><strong>Texts</strong></p> <ul> <li>Wolfram von Eschenbach: 'Parzival', in: Wolfram von Eschenbach: Werke, ed. by Karl Lachmann, 5th edition, Berlin 1891, pp. 11–388.</li> <li>Hartmann von Aue: 'Erec', ed. by Albert Leitzmann continued by Ludwig Wolff, 7th edition by Kurt Gärtner, Tübingen 2006 (Altdeutsche Textbibliothek 39).</li> <li>Hartmann von Aue: 'Iwein', ed. by G. F. Benecke and K. Lachmann, revised by Ludwig Wolff, 7th edition, part 1: Text, Berlin 1968.</li> <li>Wolfram von Eschenbach: 'Willehalm', in: Wolfram von Eschenbach: Werke, ed. by Karl Lachmann, 5th edition, Berlin 1891, pp. 421–640.</li> <li>'Das Rolandslied des Pfaffen Konrad', ed. by Carl Wesle, 3rd edition by Peter Wapnewski, Tübingen 1985 (Altdeutsche Textbibliothek 69).</li> </ul> <p>All texts are part of the MHDBDB (<a href="http://mhdbdb.sbg.ac.at/">Mittelhochdeutsche Begriffsdatenbank</a>).</p> <p> </p> <p><strong>Annotations</strong></p> <p>The texts contain annotations of different categories, as described in the following sections.</p> <p> 1.<em> Named Entities and Entity Mentions</em></p> <p>I annotated all namend entities and entity mentions that belong to the categories PER and LOC. PER stands for 'person' and refers to real persons as well as fictional characters. LOC stands for 'location' and includes real and fictional places.</p> <p>I annotated named entities (e.g. 'Parzival' as PER or 'Nantes' as LOC) as well as entity mentions referring to an instance of PER or LOC (e.g. 'the knight' for Parzival, or 'the city' for Nantes). I did not annotate pronouns. Entity references can contain multiple words, e.g. 'the lovely queen Ginover', and they can be nested, e.g. '[the son of [the king Gahmuret]]'.</p> <p>The annotations follow the guidelines created for multiple categories and disciplines in the context of CRETA. They are published <a href="https://www.creta.uni-stuttgart.de/cute/datenmaterial/annotationsrichtlinien-1-1/index.html">here</a>. </p> <p> 2.<em> Entity Grounding</em></p> <p>All annotated entity references are mapped to the entity instance that they refer to. E.g. the refences 'Parzival', 'Herzeloyde's son', 'the young man', 'the red knight' etc. all refer to the character instance 'Parzival'. The entity grounding takes into consideration the context of the entity mentions since one and the same expression can refer to different instances (in one context 'the king' refers to Arthur, in another context to Gahmuret)</p> <p> 3.<em> Direct Speech (DS)</em></p> <p>Passages of direct speech have been annotated by detecting quotation marks. They are tagged as 'DS'. There are a few cases of embedded direct speech (passages of direct speech containing another passage of direct speech); these cases are annotated as well.</p> <p> 4. <em>Narrator's comments (EK)</em></p> <p>As additional category I annotated passages that contain statements of the narrator, narrator's comments, extensive descriptions or digressions (e.g. an excursus to a specific topic). These passages are not part of the fictional world or lead to a pause in the timeline of events. The are annotated as 'EK' ('EK': passages that aren't part of the diegesis, 'EK2': passages that lead to a pause, e.g. comments or descriptions).</p> <p> 5. <em>Segmentation</em></p> <p>The texts are subdivided in passages of 30 verses. Since some text's editions ('Parzival', 'Willehalm') contain a formal segmentation in passages of 30 verses each, the same kind of segmentation has been transfered to the other texts. This means 'segment 1' contains the first 30 verses, 'segment 2' contains verses 31-60 and so on.</p> <p>According to the editions by Lachmann, 'Parzival' and 'Willehalm' are also subdivided in chapter-like books (Parzival: book 1 to 16, Willehalm: book 1 to 9). The other texts are similarly subdivided in chapter-like sections following common content-based divisions.</p> <p> </p> <p><strong>Social Network Analysis</strong></p> <p>The data can be used to explore and analyse the social network of the texts. SNA can be performed via gephi [4] using the gefx files.</p> <p>The social network is based on co-occurrences using a) the annotated and grounded entities, and b) the text segmentation in segments of 30 verses each. A relation between two or more entities is extracted whenever they co-occur in a segment.</p> <p> </p> <p><strong>Data downloads</strong></p> <p>The annotated texts can be downloaded in multiple formats: conll, csv, and gexf.</p> <p> 1. <em>Conll </em></p> <p>The files contain seven columns:</p> <ul> <li>(1) token,</li> <li>(2) POS-tag, tagged using a <a href="https://www.ims.uni-stuttgart.de/forschung/ressourcen/werkzeuge/pos-tag-mhg/">middle high german pos tagger</a>,</li> <li>(3) number of segment,</li> <li>(4) Entity reference annotation indicating the intance that the entity reference refers to. '-' if there is no entity reference,</li> <li>(5) EK: '1' in case there is an annotation of 'EK', '0' if not,</li> <li>(6) EK2: '1' in case there is an annotation of 'EK2', '0' if not,</li> <li>(7) DS: '1' if the token is tagged as direct speech, '0' if not.</li> </ul> <p> 2. <em>Csv</em></p> <p>The csv files contain all annotations of the category PER including entity grounding.</p> <p>The files contain the following columns:</p> <ul> <li>begin and end (start and end of the entity reference expression, character offset),</li> <li>doc_id (document id),</li> <li>buch (book number),</li> <li>quote (entity reference expression),</li> <li>coref (the entity instance that the expression refers to),</li> <li>overlap (indicates if there is an overlap, relevant for embedded entities),</li> <li>ek and ek2 (narrator's comment),</li> <li>ds (direct speech),</li> <li>space (annotations of the space where the story takes action, can be ignored here),</li> <li>segnr (number of segment),</li> <li>em (embedded),</li> <li>klasse (entity class),</li> <li>xrange (technical, relevant for annotation view).</li> </ul> <p> 3. <em>Gexf</em></p> <p>These files can be used to import the data to gephi. It is based on the annotation and grounding of entities (categorie PER). A relation between entities is based on co-occurrence (whenever two or more entities co-occur in a segment, they have a relation; with more relations, the intensitiy of their relation grows). The text segmentation is described above.</p> <p>Embedded entities are excluded. Entities mentioned in direct speech (DS) or in comments (EK) can optionally be selected or deselected. These optional filters are indicated in the name of the files.</p> <p>To visualize the graph dynamically, one can use the text segmentation as timeline.</p> <p> </p> <p>release v1.0.0: data publication in the context of my dissertation. </p>
Big Data and Text-mining Technologies Applied for Breast Cancer Medical Data Analysis
ClinicalTrials.gov study NCT02810093. IPD Sharing: NO. Countries: 1. Publications: 1.
Frame Analysis of Chinese COVID-19 Press Conference Texts during 2020-2023: A LDA Topic Modelling Approach
Open the record for dataset details and reuse information.
Text-fig. 2. "Site screen" scheme of complete results of the IPR-vegetation analysis derived from the database. in The Integrated Plant Record Vegetation Analysis: Internet Platform And Online Application
Text-fig. 2. "Site screen" scheme of complete results of the IPR-vegetation analysis derived from the database.
Text-fig. 1. Taphonomic and pathological phenomena of cave bear bones from Late Pleistocene deposits from Vařekova chodba in Za Hájovnou Cave (Moravia, the Czech Republic); a – fragment of vertebra with bite marks; b – fragment of right humerus, proximal part with bite marks; c – thoracic vertebra with pathological rib facet; d–e – fused left astragalus with left calcaneus (d: medial view, e: dorsal view). in Basic Population And Taphonomic Analysis Of Bear Assemblages From Za Hájovnou Cave (Moravia, The Czech Republic): A Fossil Record From 1987-2007
Text-fig. 1. Taphonomic and pathological phenomena of cave bear bones from Late Pleistocene deposits from Vařekova chodba in Za Hájovnou Cave (Moravia, the Czech Republic); a – fragment of vertebra with bite marks; b – fragment of right humerus, proximal part with bite marks; c – thoracic vertebra with pathological rib facet; d–e – fused left astragalus with left calcaneus (d: medial view, e: dorsal view).
Text-fig. 8. Sex estimation of Moča skull (Komárno district, southern Slovakia), linear discriminant analysis using Henke's Late Upper Palaeolithic and Mesolithic database (n = 129, f = 46, m = 83) with variables M1 (GOL) and M45 (ZYB). As the closest to the Moča skull, the following fossil samples from Mesolithic could be identified: Vlasac, Arruda 2, Moita 16, Moita 17, Téviec 2, Téviec 3. in A Late Upper Palaeolithic Skull From Moča (The Slovak Republic) In The Context Of Central Europe
Text-fig. 8. Sex estimation of Moča skull (Komárno district, southern Slovakia), linear discriminant analysis using Henke's Late Upper Palaeolithic and Mesolithic database (n = 129, f = 46, m = 83) with variables M1 (GOL) and M45 (ZYB). As the closest to the Moča skull, the following fossil samples from Mesolithic could be identified: Vlasac, Arruda 2, Moita 16, Moita 17, Téviec 2, Téviec 3.
References for articles included in text analysis
<p>The raw data for the literature review of Construction 4.0 technologies in the context of construction projects.</p>
Natural Language Processing (NLP) Analysis of Free Text Notes to Investigate Coronavirus (COVID-19)
ClinicalTrials.gov study NCT04432961. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Methodology Flowchart for Thematic Trends Analysis Using Text Mining
<p><strong>Figure: Methodology Flowchart for Thematic Trends Analysis Using Text Mining</strong><br>This flowchart outlines the methodology used to analyze thematic trends in academic research related to trade exhibitions through text mining techniques. The process begins with <strong>Data Collection</strong>, where academic articles are sourced from leading journals. <strong>Data Preprocessing</strong> involves cleaning and preparing the text data for further analysis. The methodology then branches into <strong>Keyword Extraction</strong> and <strong>LDA Topic Modeling</strong>. Keyword extraction identifies key themes and terms within the corpus (Manning, Raghavan, & Schütze, 2008), while LDA (Latent Dirichlet Allocation) is employed to discover latent thematic structures (Blei, Ng, & Jordan, 2003). These steps lead to <strong>Cluster Analysis</strong> to group similar articles based on the extracted keywords (Everitt et al., 2011) and <strong>Sentiment Analysis</strong> to determine the emotional tone associated with the identified themes (Pang & Lee, 2008). The final step is <strong>Data Visualization & Interpretation</strong>, where the results are visualized and insights are derived.</p>
Computerized automated text and voice analysis - a single case study of seven chronically schizophrenia patients in art therapy (livopict)
<p>This is an explorative monocentric art therapy study in the form of a quantitative single-case study (n-of-one-trial without baseline) in seven patients with chronic schizophrenia. The interventions consist of therapist-guided interviews with patients about their images, which are digitally recorded and analyzed using both text and voice analysis software.</p> <p>This dual study approach of digitized interviews allows correlations between the two portions of the recorded voice samples and forms the internal control in the study approach.</p> <p>The sample of seven patients with chronic schizophrenia (n=7), defined under inclusion and exclusion criteria, engaged in pictorial activity as part of their weekly group art therapy (non- invasive portion of the studies intervention: image creation, IC). In a one-on-one interview after three or four days, study participants were invited to talk with the author about the paintings they had created. An interview developed especially for the study served as a guide and, above all, as a standardized stimulus for the patients' statements (therapist-guided picture discussion, TGPD).</p> <p> </p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.