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.

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

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.

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

The KCI Linkage Information Service provides linkage information between major academic databases, including KCI, WOS, SCOPUS, and NDSL, for domestic academic papers. This allows users to view a variety of information, including citation status for each paper, related literature, and linkage dates. It also includes detailed information, such as citation relationships between papers, citation counts by database, and original text URLs, making it highly useful for research performance analysis, citation statistics, academic information linkage, and research trend monitoring. It can also be useful for researchers, institutions, and evaluation agencies in analyzing paper impact and managing research performance.

Tabular data for lynx captures used in analyses related to Derek Arnold's Ph.D. dissertation, 'Movement ecology, survival, and territorial dynamics in Canada lynx (Lynx canadensis) over a cyclic population decline'. These data are a subset of the capture data for the 'Movement Patterns, Dispersal Behavior, and Survival of Lynx in Relation to Snowshoe Hare Abundance in the Boreal Forest' project sponsored by the U.S. Fish and Wildlife Service.

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.

Mathematical reasoning, a fundamental aspect of human cognition, poses significant challenges for artificial intelligence (AI) systems. Despite recent advancements in natural language processing (NLP) and large language models (LLMs), AI's ability to replicate human-like reasoning, generalization, and efficiency remains an ongoing research challenge. In this dissertation, we address key limitations in MWP solving, focusing on the accuracy, generalization ability and efficiency of AI-based mathematical reasoners by applying human-like reasoning methods and principles.

This dissertation introduces several innovative approaches in mathematical reasoning. First, a numeracy-driven framework is proposed to enhance math word problem (MWP) solvers by integrating numerical reasoning into model training, surpassing human-level performance on benchmark datasets. Second, a novel multi-solution framework captures the diversity of valid solutions to math problems, improving the generalization capabilities of AI models. Third, a customized knowledge distillation technique, termed Customized Exercise for Math Learning (CEMAL), is developed to create tailored exercises for smaller models, significantly improving their efficiency and accuracy in solving MWPs. Additionally, a multi-view fine-tuning paradigm (MinT) is introduced to enable smaller models to handle diverse annotation styles from different datasets, improving their adaptability and generalization. To further advance mathematical reasoning, a benchmark, MathChat, is introduced to evaluate large language models (LLMs) in multi-turn reasoning and instruction-following tasks, demonstrating significant performance improvements. Finally, new inference-time verifiers, Math-Rev and Code-Rev, are developed to enhance reasoning verification, combining language-based and code-based solutions for improved accuracy in both math and code reasoning tasks.

In summary, this dissertation provides a comprehensive exploration of these challenges and contributes novel solutions that push the boundaries of AI-driven mathematical reasoning. Potential future research directions are also discussed to further extend the impact of this dissertation.

Mosquito-transmitted diseases such as malaria and dengue are major public health burdens. Interventions that target mosquito vectors are promising for preventing contact between humans and mosquitoes. One such intervention class that is currently being tested in clinical trials is spatial repellents (SRs), which are products that may lower human-mosquito contact by driving mosquitoes away from human-inhabited spaces and/or interfere with the mosquito host-seeking and biting processes. Properly quantifying the mechanism of action of SRs is crucial to projecting the effectiveness of SRs when deployed on a large scale as the effectiveness of SRs is likely to vary geographically due to differences in transmission intensity and climate, among other factors. Three analyses were conducted to quantify SR mechanisms of action and cost-effectiveness.

Firstly, a Bayesian model was developed to attribute changes in malaria incidence, malaria prevalence, and human biting rates in a clinical trial in Indonesia to behavioral effects in the \textit{Anopheles} mosquito population induced by SRs. This analysis showed that SRs lowered the mosquito biting rate in treated homes by 25.5% (95% credible interval [CrI]: 4.62-39.7%), reduced mosquito mortality by 7.10% (95% CrI: 16.3% decrease-7.02% increase), and increased the amount of time mosquitoes spent inside human-inhabited spaces by 7.56% (95% CrI: 10.3% decrease-26.0% increase).

Secondly, a similar model was developed for a trial of SRs for prevention of arboviruses such as dengue in Peru. This analysis showed that SRs lowered the mosquito biting rate in treated homes by 9.85% (95% CrI: 4.72-15.7%), increased mosquito mortality by 32.6% (95% CrI: 11.7-60.5%), decreased the probability of mosquitoes entering treated homes by 33.4% (95% CrI: 15.1-52.6%) and decreased the exiting rate of mosquitoes from treated homes by 7.81% (95% CrI: 34.0% decrease - 17.4% increase).

Thirdly, optimal control theory along with estimates of SR effects from chapter two, a climate-driven model of mosquito abunadance, and an SEI-SEIS model were used to project the cost-effectiveness of SRs when deployed throughout Kenya. We found that cost-effectiveness of SRs depended critically on estimates of SR impact on mosquito mortality, and that SRs are likely to be cost-effective .

In conclusion, this dissertation proposes three studies to aid the decision of approval of SRs for use against malaria and dengue and provides a framework for understanding and elaborating on clinical trials of mosquito-borne diseases.

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.

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.

TechnicalRemarks: Data of study I and study III of the dissertation "The Gamification of Crowdsourcing Systems: Empirical Investigations and Design" by Benedikt Morschheuser 2017: DOI: 10.5445/IR/1000074115

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).

Uganda Road Fund Allocation Formula application 2014 and 2015

The dataset associated with the PhD Thesis '', by Ellen L White. This dataset does not include any raw passive acoustic data, please contact me if you are interested in access to any of this data. The data repository contains the training and testing data used within the thesis to develop a CNN for muli-sound source detection, including all required scripts to replicate this work.

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.

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.