Context

A major shortcoming of medical practice is the lack of an objective measure of conscious level. Impairment of consciousness is common, e.g. following brain injury and seizures, which can also interfere with sensory processing and volitional responses. This is also an important pitfall in neurophysiological methods that infer awareness via command following, e.g. using functional magnetic resonance imaging or electroencephalography (EEG).

Transcranial electrical stimulation (TES) can be employed to non-invasively stimulate the brain, bypassing sensory inputs, and has already showed promising results in providing reliable indicators of brain state. However, current non-invasive solutions have been limited to transcranial magnetic stimulation, which is not easily translatable to clinical settings.

In this neurotechnology research study, we demonstrated the feasibility of a framework using Deep Learning (DL) algorithms to classify EEG brain responses evoked by a defined multi-dimensional pattern of TES. We found that delivering transcranial direct current stimulation (tDCS) to posterior cortical areas targeting the angular gyrus elicited an exceptionally reliable brain response. For this particular paradigm, our best Convolutional Neural Network models reached a 92% classification f1-score on Holdout data from participants never seen during training, significantly surpassing an estimated human-level performance at 60-70% accuracy.

Content

This dataset contains the raw EEG files (in MFF format) used in our study (see Section 2.5), with the corresponding GitHub repository containing all code & support data. The complementary Kaggle dataset with preprocessed EEG files (CSV format) can be found here.

Each root folder corresponds to a different approach we followed: 1. Timeseries: filtered, curated, and epoched EEG time series; resampled to 250Hz. Shape per training example => (250 time samples, 188 good channels) 2. Features: set of 37 statistical measures that describe the EEG time series data. Shape per training example => (37 features, 188 good channels) 3. Concatenated Features: an equivalent version to [2] but with features stacked horizontally. Shape per training example => (1 row, 6956 columns)

Methodology

We enrolled 11 healthy resting awake participants (4 female; ages 20-37, average 25.0±4.6 years old) to conduct 13 separate experimental sessions (subjects P000 and P001 participated twice, after results from first sessions were found invalid). Participants were instructed to sit awake with eyes open, and blinded to conditions applied. Following an initial rest period of 120 seconds (including 60 seconds with eyes closed), up to 58 blocks of TES were performed, with a total time of up to ~60 minutes per session as tolerated per the participant. EEG was continuously recorded at 1000Hz, and later resampled to 250Hz in our MNE preprocessing pipeline.

For equipment we used a newly-acquired GTEN 200 neuromodulation system (Electrical Geodesics, Inc.) that allows simultaneously delivering TES and recording high-density EEG through the same 256-electrodes cap. We delivered tDCS and tACS stimulation across 2 cortical regions: bilateral posterior (targeting angular gyrus) and bilateral frontal (middle frontal gyrus).

Acknowledgements

Thank you to all my participants for playing a crucial part in this study, enabling the creation of two public datasets to be used freely by the DL-EEG research community.

Special thanks for their expert guidance during this research to:
Dr. Ines Ribeiro Violante
Dr. Gregory Scott

Questions?

Open a Kaggle Discussion or contact me via LinkedIn.

Thanks,
Alexis Pomares

Lawrence (Lonny) R. Ness Dissertation Statistical Analysis Master

In order to encourage academic and related research on gender equality education and improve the academic standards of the above-mentioned topics, the Ministry of Education has formulated the "Key Points for the Ministry of Education to Award Master's and Doctoral Thesis and Journal Papers on Gender Equality Education" for awards.

Availability of data, code, and plot creation for various figures throughout my PhD thesis. Rough organisation currently. Pertains to Figures 5.4, 5.8, 6.11, 6.18, 7.3, 7.12, and Table 6.1.

This data package was created 2025-01-14 15:12:40 by NPSTORET and includes selected project, location, and result data. Data were collected in advance of a Ph.D. proposal by Lisa Chang. This work ultimately led to Chang's University of Virginia dissertation entitled 'Carbon and nitrogen effects on nitrification in Shaver Hollow Watershed, Shenandoah National Park'. Data contained in Shenandoah National Park - University of Virginia NPSTORET back-end file (NPS_UVA_NPSTORET_BE_20250108.ACCDB) were filtered to include: Project: - SHEN_UVA_CHANG_1994: Lisa Chang’s Pre-Ph.D. Dissertation Research Data from Shaver Hollow Station: - Include Trip QC And All Station Visit Results Park/Unit Code: - SHEN Value Status: - Accepted or Certified (exported as Final) or Final The data package is organized into five data tables: - Projects.csv - describes the purpose and background of the monitoring efforts - Locations.csv - documents the attributes of the monitoring locations/stations - Results.csv - contains the field measurements, observations, and/or lab analyses for each sample/event/data grouping - HUC.csv - enumerates the domain of allowed values for 8-digit and 12-digit hydrologic unit codes utilized by the Locations datatable - Characteristics.csv - enumerates the domain of characteristics available in NPSTORET to identify what was sampled, measured or observed in Results Period of record for filtered data is 1994-06-15 to 1994-12-03. This data package is a snapshot in time of one National Park Service project. The most current data for this project, which may be more or less extensive than that in this data package, can be found on the Water Quality Portal at: https://www.waterqualitydata.us/data/Result/search?project=SHEN_UVA_CHANG_1994

Text files:

readme_general.txt contains a brief description of files included.

readme_modeldata.txt contains a metadata description of model_data.csv.

readme_languageslandNorthAmerica.txt contains a metadata description of Languages_land_NorthAmerica.csv.

readme_languagerevitalizationdatabase.txt contains a metadata description of Language_revitalization_database.csv.

CSV files:

Languages_land_NorthAmerica.csv is a version of the Languages of Government-Recognized Native Land Areas in the Continental United States database. It includes data from the US Census 2017 TIGER/Line AIANNH shapefile with one row per Native land area and additional columns for associated information that was coded and calculated for this dissertation as discussed in Section 3.3.1.

Language_revitalization_database.csv is the Language Revitalization Database. It contains the master language list used for this dissertation and columns created while coding data for the language revitalization variable, as discussed in Section 3.3.2.

model_data.csv contains data for all variables used in the analysis and is the .csv file needed to run LanguageVitalityModels.R.

R scripts:

LanguageVitalityModels.R is the R script for the main part of the dissertation analysis.

Uganda Road Fund Allocation Formula application 2014 and 2015

This dataset contains in high resolution all graphical visualizations of data analysis provided in my doctoral dissertation. The graphs are organized according to chapters and subchapters and titeled respectively. Additionally, this dataset provides all dataframes (German, English, and Armenian) in XLSX format of the manual semantic annotation based on which the graphs are generated. Among presented graphical visualizations are (Multiple) Correspondence Analysis (MCA vs. CA), Mosaic-Plots, Conditional Infererence Trees (CIT), and Context-Conditional Correlations Graphs (CCCG).

Thesis Data Sets

## Overview

Thesis Data Sets is a dataset for object detection tasks - it contains Fruits annotations for 2,805 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for psychometrics and statistics equiv to us phd in statistics in the U.S.

Collection of source code implementing methods and for reproducing experiments included in each chapter of the Ph.D. dissertation "Safer Causal Inference: Theory & Algorithms for Falsification, Trial Augmentation and Policy Evaluation". The source code also includes methods for generating simulated datasets used in the evaluation of the methods. The goal of the of the research was to develop methods to improve treatment effect estimation, this includes: methods to detect unmeasured confounding from observational data, methods to integrate historical data into randomized experiments to improve data efficiency, methods to evaluate treatment policies under treatment interference.

This data is supplementary to the paper "AckSent: Human Annotated Dataset of Support and Sentiments in Dissertation Acknowledgments" .

The data underlying two chapters of the dissertation are included in this dataset.

The questionnaire with residents were conducted in Chapter 2 of this dissertation to assess the traditional courtyard housing conditions, the residents needs and interests concerning the reconstruction following the Syrian war in the Old City of Aleppo. This chapter has been published as a peer-reviewed journal article:

https://www.mdpi.com/2071-1050/13/21/12213

Chapter 5 of this dissertation includes a review and analysis of co-creation projects focused on residential areas to get insight into participatory methods and to understand how to engage residents actively in the intervention process. The databases searched were Community Research and Development Information Service (CORDIS) and the Trans-Atlantic Platform for Social Sciences and Humanities (T-AP). This chapter has been submitted to a peer-reviewed journal "Heritage".

This folder contains data underlying the PhD dissertation of Ashraf Shaharudin titled, “Decoding Open Data Intermediation Business Models: More than Just a Bridge”. It consists of:

1. Chapter 2 - README
2. Chapter 2 - Stage-0 Search results
3. Chapter 2 - Stage-1 Remove redundant
4. Chapter 2 - Stage-2 Remove irrelevant
5. Chapter 2 - Stage-3 Final filtering
6. Chapter 3 - README
7. Chapter 3 - Tentative interview questions
8. Chapter 3 - Interview transcripts - All
9. Chapter 3 - Coding results
10. Chapter 4 - Dataset of desk survey
11. Chapter 5 - Interview transcripts - All
12. Chapter 6 - Interview transcripts - All

Note about the de-identified interview transcripts:

The de-identified interview transcripts should be read in the context of the research on open data ecosystem and open data intermediaries. The interviews were conducted between April 2023 and October 2024 based on the semi-structured approach. Tentative interview questions were shared with the interviewees in advance (for the majority, at least three working days prior). Personally identifiable information is redacted from the transcripts. With verbal communication, some sentences may be less incomprehensible in writing. Thus, minimal edits were done when transcribing to improve the comprehensibility where necessary, but the main objective was to keep the transcripts as close to verbatim as possible.

Acknowledgement:

This research is part of the 'Towards a Sustainable Open Data ECOsystem' (ODECO) project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569. The opinions expressed in this document reflect only the author’s view and in no way reflect the European Commission’s opinions. The European Commission is not responsible for any use that may be made of the information it contains.

This dataset is the "English Master's and Doctoral Dissertations" dataset, covering 760000 English degree theses and their structured analysis results. Among them, there are 517000 doctoral dissertations and 243000 master's dissertations. The paper is presented in Markdown/LaTex format and covers 21 disciplines including physics, chemistry, biology, medicine, and economics. Currently, the data is undergoing structured parsing work, aiming to achieve accurate parsing and effective processing of text, formulas, tables, and images, providing strong support for related research.

Data content

760000 English Dissertation Text Parsing And Processing Data

Data volume

517000 doctoral dissertations, 243000 master's dissertations

Format

PDF/Markdown/LaTex

Disciplines

21 classifications including physics, chemistry, biology, medicine, economics, etc

Language

English

Fields

title, author, publish_time, university, degree, classification

Data Processing

currently performing structured analysis to extract and process text, formulas, tables, and images.

This dataset comprises five sets of data collected throughout the PhD Thesis project of Pelin Esnaf-Uslu.

Esnaf-Uslu, P. (2024). Design for Interpersonal Mood Regulation: Introducing a Framework and Three Tools to Support Mood-Sensitive Service Encounters. (Doctoral dissertation in review). Delft University of Technology, Delft, the Netherlands.

The research in this thesis is based on the premise that service providers can enhance their effectiveness in client interactions by acquiring a detailed understanding of IMR strategies and effectively applying this knowledge. To achieve this overall aim, the current research aimed to explore (1) the current role of mood in service encounters, (2) the IMR strategies used by service providers during service encounters in response to client’s moods, (3) how IMR strategies can be facilitated by means of tools for service providers and the (4) strengths and limitations of the developed materials.

This research was supported by VICI grant number 453-16-009 from The Netherlands Organization for Scientific Research (NWO), Division for the Social and Behavioral Sciences, awarded to Pieter M. A. Desmet.

The data is organized into folders corresponding to the chapters of the thesis. Each folder contains a README file with specific information about the dataset.

Chapter_2: This study investigates the role of mood in service encounters. Samples are collected from service providers experiences during service encounters and in-depth interviews are conducted. The dataset includes the blank diary and the interview protocol.

Chapter_3: This study investigates the clarity of the images developed representing Interpersonal Mood Regulation (IMR) strategies. The dataset includes anonymized scores from 27 and 29 participants, showing the associations between images representing nine IMR strategies and their corresponding labels and descriptions, along with the free descriptions provided by the participants. Additionally, the dataset contains a screenshot of the workshop material used in the implementation study.

Chapter_4: This study examines the clarity of developed videos depicting IMR strategies. The dataset includes anonymized scores from 32 participants, showing the associations between videos depicting nine IMR strategies and their corresponding labels and descriptions, along with the free descriptions provided by the participants. In addition, the dataset contains the workshop guideline developed for the implementation study.

Chapter_5: This study evaluates the clarity of character animations depicting Interpersonal Mood Regulation (IMR) strategies. The dataset includes anonymized scores from 39 participants, demonstrating the associations between videos illustrating nine IMR strategies and their corresponding labels and descriptions, along with the free descriptions provided by the participants.

Chapter_6: This dataset comprises correspondence analysis files for each material, created for the purpose of comparison.

All the data is anonymized by removing the names of individuals and institutions.

Total Atlas.ti project in support of the dissertation.

This dataset contains data used in the publication "Identifying publications of cumulative dissertation theses by bilingual text similarity. Evaluation of similarity methods on a new short text task". It included bibliographical data for German PhD theses (dissertations) and associated publications for cumulative dissertations. Not included is content from Elsevier's Scopus database used in the study, except item identifiers. Users with access to the data can use these for matching.

File diss_data.csv contains bibliographic data of dissertation theses obtained from German National Library and cleaned and postprocessed The columns are: REQUIZ_NORM_ID: Identifier for the thesis TITLE: Cleaned thesis title HEADING: Descriptor terms (German) AUTO_LANG: Language, either from original record or automatically derived from title

File ground_truth_pub_metadata.csv contains bibliographic data for identified consitutive publications of theses. If columns 2 to 7 are empty, the thesis did not include any publications ("stand-alone" or monograph thesis).

The columns are: REQUIZ_NORM_ID: Identifier for the thesis, for matching with the data in file SCOPUS_ID: Scopus ID for the identified publication AUTORS: Author names of the publication as in the original thesis citation YEAR: Publication year of the publication as in the original thesis citation TITLE: Publication title as in the original thesis citation SOURCETITLE: Source title as in the original thesis citation PAGES: Page information of the publication as in the original thesis citation

Scopus identifiers are published with permission by Elsevier.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Data Fulfillment and Analysis Division of the Chicago Police Department at DFA@ChicagoPolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data are updated daily. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

We present the results of a quantitative assessment of research data produced and submitted with dissertations Special attention is paid to the size of the research data in appendices, to their presentation and link to the text, to their sources and typology, and to their potential for further research. The discussion puts the focus on legal aspects (database protection, intellectual property, privacy, third-party rights) and other barriers to data sharing, reuse and dissemination through open access.Another part adds insight into the potential handling of these data, in the framework of the French and Slovenian dissertation infrastructures. What could be done to valorize these data in a centralized system for electronic theses and dissertations (ETDs)? The topics are formats, metadata (including attribution of unique identifiers), submission/deposit, long-term preservation and dissemination. This part will also draw on experiences from other campuses and make use of results from surveys on data management at the Universities of Berlin and Lille.