Statistics relating to the publication, date, title, language, faculty and links of all PhD theses published by Technische Hogeschool Delft and Delft University of Technology during the period 1905 to 2020.

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.

Data supporting the Master thesis "Monitoring von Open Data Praktiken - Herausforderungen beim Auffinden von Datenpublikationen am Beispiel der Publikationen von Forschenden der TU Dresden" (Monitoring open data practices - challenges in finding data publications using the example of publications by researchers at TU Dresden) - Katharina Zinke, Institut für Bibliotheks- und Informationswissenschaften, Humboldt-Universität Berlin, 2023

This ZIP-File contains the data the thesis is based on, interim exports of the results and the R script with all pre-processing, data merging and analyses carried out. The documentation of the additional, explorative analysis is also available. The actual PDFs and text files of the scientific papers used are not included as they are published open access.

The folder structure is shown below with the file names and a brief description of the contents of each file. For details concerning the analyses approach, please refer to the master's thesis (publication following soon).

## Data sources

Folder 01_SourceData/

- PLOS-Dataset_v2_Mar23.csv (PLOS-OSI dataset)

- ScopusSearch_ExportResults.csv (export of Scopus search results from Scopus)

- ScopusSearch_ExportResults.ris (export of Scopus search results from Scopus)

- Zotero_Export_ScopusSearch.csv (export of the file names and DOIs of the Scopus search results from Zotero)

## Automatic classification

Folder 02_AutomaticClassification/

- (NOT INCLUDED) PDFs folder (Folder for PDFs of all publications identified by the Scopus search, named AuthorLastName_Year_PublicationTitle_Title)

- (NOT INCLUDED) PDFs_to_text folder (Folder for all texts extracted from the PDFs by ODDPub, named AuthorLastName_Year_PublicationTitle_Title)

- PLOS_ScopusSearch_matched.csv (merge of the Scopus search results with the PLOS_OSI dataset for the files contained in both)

- oddpub_results_wDOIs.csv (results file of the ODDPub classification)

- PLOS_ODDPub.csv (merge of the results file of the ODDPub classification with the PLOS-OSI dataset for the publications contained in both)

## Manual coding

Folder 03_ManualCheck/

- CodeSheet_ManualCheck.txt (Code sheet with descriptions of the variables for manual coding)

- ManualCheck_2023-06-08.csv (Manual coding results file)

- PLOS_ODDPub_Manual.csv (Merge of the results file of the ODDPub and PLOS-OSI classification with the results file of the manual coding)

## Explorative analysis for the discoverability of open data

Folder04_FurtherAnalyses

Proof_of_of_Concept_Open_Data_Monitoring.pdf (Description of the explorative analysis of the discoverability of open data publications using the example of a researcher) - in German

## R-Script

Analyses_MA_OpenDataMonitoring.R (R-Script for preparing, merging and analyzing the data and for performing the ODDPub algorithm)

This research aims to develop a principle-based framework for audit analytics implementation, which addresses the challenges of AA implementation and acknowledges its socio-technical complexities and interdependencies among challenges. This research relies on mixed methods to capture the phenomena from the research’s participants through various approaches, i.e., MICMAC-ISM, case study, and interview with practitioners, with literature exploration as the starting point. The raw data collected consists of multimedia data (audio and video recordings of interviews and focused group discussion), which is then transformed into a text file (transcript), complemented with a softcopy of the documents from the case study object.

The published data in this dataset, consists of the summarized or analyzed data, as the raw data (including transcript) is not allowed to be published according to the decision by the Human Research Ethics Committee pertinent to this research (Approval #1979, 14 February 2022). This dataset's published data are text files representing the summarized/analyzed raw data as an online appendices to the thesis.

Researchers connected to the University of Groningen published the most dissertations in 2020. With 591 publications, they published slightly more than the runner-up, University of Amsterdam with 541 dissertations.

To identify relevant actors for the governance of co-produced forest nature's contributions to people (NCP) the researchers conducted a social-network analysis based on 39 semi-structured interviews with foresters and conservation managers. These interviews were conducted across three case study sites in Germany: Schorfheide-Chorin in the Northeast, Hainich-Dün in the Centre, and Schwäbische Alb in the Southwest. All three case study sites belong to the large-scale and long-term research platform Biodiversity Exploratories. The researchers employed a predefined coding set to analyse the interviews and grasp the relationships between different actors based on the anthropogenic capitals they used to co-produce forest nature's contributions to people (NCP). To secure the interviewees anonymity this coding cannot be published. Therefore, this data set is limited to this coding set.

This data set contains the research data for the master's thesis: Integrating Explainability into Federated Learning: A Non-functional Requirement Perspective. The master's thesis was written by Nicolas Sebastian Schuler at the Computer Science Department at Karlsruhe Institute for Technology (KIT) in Germany. The data set contains: - Associate Jupyter notebooks for reproducing the figures in the master's thesis. - Generated experiment data by the federated learning simulations. - Results of the user survey conducted for the master's thesis. - Used Python Libraries. It also includes the submitted final thesis. Notice: The research data is split into multiple chunks and can be combined via the following command after downloading: $ cat thesis-results-part-* > thesis-results.tar.zst and extracted via: $ tar --zstd -xvf thesis-results.tar.zst

This simple dataset contains publication statistics of Swedish PhD and Licentiate thesis in Software Engineering from 1999 to 2018. The contents of this dataset were discussed in a blog post on https://grischaliebel.de.

The data is offered in two formats, xlsx and csv, but with the same content. Names and affiliation are anonymised in the data set to prevent identification of subjects. In the following, we describe the content of the different columns in the table.

Level: 'lic' for Licentiate theses or 'phd' for PhD theses

Year: The year of publication of the thesis

Included: The total number of papers included in the compilation-style thesis.

Listed: Number of papers listed in addition to the included papers (basically "I have also published these, but they are not relevant to the thesis). Note that we cannot distinguish between cases, where no papers are listed because none are published, or because the author decided not to list them.

IncludedPublished: The amount of included papers that are published or accepted for publication.

IncludedSubmitted: The amount of included papers that in submission/under review.

IncludedPublishedISI: The amount of included, published papers that are in ISI-ranked journals.

IncludedPublishedNonISIJ: The amount of included, published papers that are in non ISI-ranked journals.

IncludedPublishedConf: The amount of included, published papers that are in CORE-ranked conferences (any grade).

IncludedPublishedWS: The amount of included, published papers that are in workshops. Non CORE-ranked conferences are counted as workshops as well.

IncludedPublishedOther: The amount of included, published papers that do not fit in any other category (e.g., book chapters, technical reports).

IncludedSubmitted*: Amount of included, submitted papers broken down by category (Journal, conference, workshop, and other).

ListedPublished*: Amount of listed, published papers broken down by category (ISI/Non-ISI Journal, conference, workshop, and other).

ListedSubmitted*: Amount of listed, submitted papers broken down by category (Journal, conference, workshop, and other).

In 2020, a total of 4,966 dissertations were produced in the Netherlands. In that year, 1,813 dissertations were published in the fields of nature and technology at Dutch universities.

This dataset has been used to explore and analyse the article publications made by PhD students at Stockholm University between 2013–2016. The publications have been measured by their Altmetric scores and by their citation rates in Scopus and Web of Science. The data about which articles to include in the study was retrieved from the Stockholm University DiVA database in October 2016. The Altmetrics data was retrieved on October 26, 2017. The citation data was retrieved on February 2, 2017.This information was analysed in a master's thesis from the Department of Education at Stockholm University, authored by Sofie Wennström in 2017 (the reference to the published version of the thesis will be added when it is made available.) The master's thesis was published in the Stockholm University DiVA repository in October 2018: http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-159244.

This is the Repository of all the research data for PhD Thesis of the doctoral candidate Nan BAI from the Faculty Architecture and Built Environment at Delft University of Technology, with the title of '*Sensing the Cultural Significance with AI for Social Inclusion: A Computational Spatiotemporal Network-based Framework of Heritage Knowledge Documentation using User-Generated*', to be defended on October 5th, 2023.

Social Inclusion has been growing as a goal in heritage management. Whereas the 2011 UNESCO Recommendation on the Historic Urban Landscape (HUL) called for tools of knowledge documentation, social media already functions as a platform for online communities to actively involve themselves in heritage-related discussions. Such discussions happen both in “baseline scenarios” when people calmly share their experiences about the cities they live in or travel to, and in “activated scenarios” when radical events trigger their emotions. To organize, process, and analyse the massive unstructured multi-modal (mainly images and texts) user-generated data from social media efficiently and systematically, Artificial Intelligence (AI) is shown to be indispensable. This thesis explores the use of AI in a methodological framework to include the contribution of a larger and more diverse group of participants with user-generated data. It is an interdisciplinary study integrating methods and knowledge from heritage studies, computer science, social sciences, network science, and spatial analysis. AI models were applied, nurtured, and tested, helping to analyse the massive information content to derive the knowledge of cultural significance perceived by online communities. The framework was tested in case study cities including Venice, Paris, Suzhou, Amsterdam, and Rome for the baseline and/or activated scenarios. The AI-based methodological framework proposed in this thesis is shown to be able to collect information in cities and map the knowledge of the communities about cultural significance, fulfilling the expectation and requirement of HUL, useful and informative for future socially inclusive heritage management processes.

Some parts of this data are published as GitHub repositories:

WHOSe Heritage

The data of Chapter_3_Lexicon is published as https://github.com/zzbn12345/WHOSe_Heritage, which is also the Code for the Paper WHOSe Heritage: Classification of UNESCO World Heritage Statements of “Outstanding Universal Value” Documents with Soft Labels published in Findings of EMNLP 2021 (https://aclanthology.org/2021.findings-emnlp.34/).

Heri Graphs

The data of Chapter_4_Datasets is published as https://github.com/zzbn12345/Heri_Graphs, which is also the Code and Dataset for the Paper Heri-Graphs: A Dataset Creation Framework for Multi-modal Machine Learning on Graphs of Heritage Values and Attributes with Social Media published in ISPRS International Journal of Geo-Information showing the collection, preprocessing, and rearrangement of data related to Heritage values and attributes in three cities that have canal-related UNESCO World Heritage properties: Venice, Suzhou, and Amsterdam.

Stones Venice

The data of Chapter_5_Mapping is published as https://github.com/zzbn12345/Stones_Venice, which is also the Code and Dataset for the Paper Screening the stones of Venice: Mapping social perceptions of cultural significance through graph-based semi-supervised classification published in ISPRS Journal of Photogrammetry and Remote Sensing showing the mapping of cultural significance in the city of Venice.

Research institute's annual paper publication content

This archive contains model forcing and output for the Shyft model, along with scripts of the related data processing. The structure of the archive (folders) is as follows:

1. "lalm" and "elverum: contain the meteorological forcing data for the two catchments Lalm and Elverum, respectively.
2. "model forcing for historical periods": contains the bias corrected climate model data representing the three historical periods.
3. "model simulation output": contains the model output data for the simulations of the three historical periods.
4. "shyft workspace": contains the data processing scripts.

1. This dataset consists of the folders: "senorge", "era5" and "hysn5". The included data variables are: temperature, precipitation, wind speed, relative humidity and radiation. Temperature and precipitation are found in "senorge". Wind speed is found in "era5". Lastly, relative humidity and radiation are found in "hysn5". The dataset is of the netCDF-format. The folders contain data that was downloaded from the sources: SeNorge2018 (The Norwegian Meteorological institute, 2022), ERA5-land (Muñoz, 2019; Muñoz, 2021) and HYSN5 (Haddeland, 2022). The data is described as follows:

temperature:

• Description: daily mean air temperature
• Unit: degrees Celsius
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

precipitation:

• Description: daily mean precipitation
• Unit: mm/day
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

wind speed:

• Description: daily mean wind speed
• Unit: m/s
• Spatial resolution: 0.1x0.1 degree (native resolution of 9 km)
• Grid mapping: EPSG:4326
• Dimension: time, latitude and longitude

relative humidity:

• Description: daily mean near-surface relative humidity
• Unit: %
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

radiation:

• Description: daily mean surface downwelling shortwave radiation
• Unit: W/m²
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

2. This dataset contains climate model data for the three historical periods: Medieval Warm Period (MWP; 1000-1150 AD), Little Ice Age (LIA; 1600-1750 AD) and Industrial Time (IT; 1800-1950 AD). The data covers the two catchments Lalm (L) and Elverum (E) for simulations using both low solar variability (Solar 1; S1) and high solar variability (Solar 2; S2). The data consists of the variables: temperature (temp), precipitation (prec), wind speed (wind), relative humidity (humi) and radiation (radi). The dataset is of the netCDF-format. The related source data is not published here, due to licences. Contact Lu Li at the NORCE research centre regarding data accessibility. The data is described as follows:

temperature:

• Description: daily mean air temperature
• Unit: degrees Celsius
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

precipitation:

• Description: daily mean precipitation
• Unit: mm/hour
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

wind speed:

• Description: daily mean wind speed
• Unit: m/s
• Spatial resolution: 0.1x0.1 degree (native resolution of 9 km)
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

relative humidity:

• Description: daily mean near-surface relative humidity
• Unit: -
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

radiation:

• Description: daily mean surface downwelling shortwave radiation
• Unit: W/m²
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

3. This dataset contains time series data for the three historical periods: Medieval Warm Period (MWP; 1000-1150 AD), Little Ice Age (LIA; 1600-1750 AD) and Industrial Time (IT; 1800-1950 AD), which are output from the Shyft model. The data covers the two catchments Lalm (L) and Elverum (E) for simulations using both low solar variability (Solar 1; S1) and high solar variability (Solar 2; S2). The data consists of the variables: discharge, temperature, precipitation, wind_speed, relative_humidity and radiation, snow water equivalent (SWE) and snow covered area (SCA). The dataset is of the csv-format.

NB: the datetime index of the data suggests that the data covers the period of 1700-1850, however this is only true for IT. This inconsistency is caused by a limitation of datetime64 in pandas, which does not handle dates prior to the year 1678.

The data is described as follows:

discharge:

• Description: daily mean air temperature
• Unit: degrees Celsius
• Dimension: time

temperature:

• Description: daily mean air temperature
• Unit: degrees Celsius
• Dimension: time

precipitation:

• Description: daily mean precipitation
• Unit: mm/hour
• Dimension: time

wind_speed:

• Description: daily mean wind speed
• Unit: m/s
• Dimension: time

relative_humidity:

• Description: daily mean near-surface relative humidity
• Unit: -
• Dimension: time

radiation:

• Description: daily mean surface downwelling shortwave radiation
• Unit: W/m²
• Dimension: time

SWE:

• Description: daily mean snow water equivalent
• Unit: mm
• Dimension: time

SCA:

• Description: daily mean snow covered area (% of total catchment area)
• Unit: -
• Dimension: time

4. The scripts make up the workflow of the thesis. In order to reproduce the results, the first script has to be run firstly, then the second script is applied on the output from the first etc. Keep in mind that manual adjustments inside the scripts are required in order to obtain some of the results. The scripts are described as follows:

1. "Subsetting_data.ipynb": This script subsets forcing data (temperature, precipitation, wind speed, relative humidity and radiation) from the sources (SeNorge2018, ERA5-Land and HySN5) to the catchments of Lalm and Elverum.
2. "convert_netcdf.ipynb": This script converts netCDF-files of temperature, precipitation, wind speed, relative humidity and radiation to fit as model forcing to the Shyft modeling framework. It also creates a cell data file containing information about the catchments (Lalm and Elverum) forest, lake and glacier fraction, which are required in Shyft.
3. "QDM_lalm.ipynb" and "QDM_elverum.ipynb": These scripts perform the bias correction approach, Quantile Delta Mapping (QDM), on the climate model data (temperature, precipitation, wind speed, relative humidity and radiation).
4. "extract_historical_periods.ipynb": This script extracts the three historical periods of 1000-1150 (Medieval Warm Period), 1600-1750 (Little Ice Age) and 1800-1950 (Industrial Time) from the climate model data (temperature, precipitation, wind speed, relative humidity and radiation).
5. "calibration_lalm.ipynb" and "calibration_elverum.ipynb": Scripts that runs the calibration of Lalm and Elverum catchment using the Shyft model, respectively.*
6. "simulation_lalm.ipynb" and "simulation_elverum.ipynb": Scripts that runs the simulation of Lalm and Elverum catchment using the Shyft model, respectively.*
7. "data_analysis.ipynb": This script contains the data analysis performed on the Shyft model simulation output. The analysis includes: calculations of mean monthly values of the climate variables (discharge, temperature, precipitation, snow water equivalent and snow covered area), decadal time series of the climate variables, calculations of mean floods and 100-year floods, flood and extreme precipitation frequency analysis, calculation of season index, estimation of flood generating processes, plotting of flood roses and estimation of Standardised Precipitation Index.

*For the Shyft model configuration, simulation and calibration files (yaml-files) are included in the folder "yaml_lalm" and "yaml_elverum" for the two catchments. These yaml-files are described as follows:

• simulation.yaml: is used for configuration of the model simulation
• calibration.yaml: is used for configuration of the model calibration
• calibrated_model.yaml: contains the calibrated model parameters
• datasets.yaml: contains the paths to the data variables
• interpolation.yaml: contains the interpolation methods and parameters
• region.yaml: contains the modeling domain

References:

Haddeland, I. (2022). HySN2018v2005ERA5 (Version 1) [Data set]. Zenodo. (Accessed on: 19-09-2022). doi: https://doi.org/10.5281/zenodo.5947547.

Muñoz Sabater, J. (2019). ERA5-Land hourly data from 1981 to present [Dataset]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on: 19-09-2022). doi: https://doi.org/10.24381/cds.e2161bac.

Muñoz Sabater, J. (2021).

ProQuest Dissertations and Theses Full text is a comprehensive collection of dissertations and theses, as well as the official digital dissertations archive for the Library of Congress and the database of record for graduate research. PQDT Full Text includes nearly 3 million searchable citations to dissertations and theses available for download in PDF format. The database offers full text for most of the dissertations added since 1997 and strong retrospective full-text coverage for older graduate works. Full-text dissertations are archived as submitted by the degree-granting institution. Each dissertation published since July 1980 includes a 350-word abstract written by the author. Masters theses published since 1988 include 150-word abstracts. Simple bibliographic citations are available for dissertations dating from 1637. ProQuest Dissertations and Theses Full Text also offers researchers unlimited access to digital copies from their own institutions as well as affordable copies from others.

This dataset is about books. It has 1 row and is filtered where the book is Successful dissertations : the complete guide for education, childhood and early childhood studies students. It features 7 columns including author, publication date, language, and book publisher.

This database is based on the collection of 12,000 overseas Chinese doctoral dissertations stored in the data room of the Sinological Research Center, and edited abstracts, with authors mainly from the United States, Canada, the United Kingdom, the Netherlands.

Abstract: This is the first publication of a source code of the (Learning) Inverse Problem Matching Pursuits ((L)IPMPs) by NS. The software was developed during the PhD-Thesis of NS. The main code is written in C/C++ using the GSL and the NLopt library. The visualization codes are written in Matlab. We advise to start with reading the README file which states related publications for further information, explains the general structure of the source code and how to use it. TechnicalRemarks: Source code of the (L)IPMPs. First published version. Finalized 2020. Other: The authors gratefully acknowledge the financial support by the German Research Foundation (DFG; Deutsche Forschungsgemeinschaft), projects MI 655/7-2 and MI 655/14-1. We are grateful for using the HPC Clusters Horus and Omni maintained by the ZIMT of the University of Siegen for development and numerical experiments.

This is a set of data for an original article for publication in the Journal of Criminology, titled “Disparities in Forensic Science Adoption for Crime Investigation: The Role of Police Demographics.” The data set is int he format of Excel.

This zenodo archive contains data and code that was used to conduct the master thesis.

Overview:

• Code
  • Demand Data Matching
    • Scripts that were used to match the demand data from the Pathway Explorer data Format the the format that is needed as input by PyPSA EUR
    • demand_data_matching.zip
  • Result analysis
    • Scripts that were used to extract the result data from the PyPSA EUR [1] network result files
    • result_analysis_code.zip
• Data
  • Demand Input Data
    • Data that was generated with the Pathway Explorer by CLIMACT [2]
    • Files:
      • pwe_files.zip
  • Result data
    • Output Files from the PyPSA EUR Model including the model config files
    • Files:
      
      • mainResults+network_Sensitivity.zip
      • CC_sensitivity.zip
      • CarbonSeq+fossil_Sensitivity.zip

Licensing

The code that was written in this thesis is published under the MIT License, see file license.txt

All PyPSA EUR datasets are licensed under CC-BY-4.0, see also [3]

CLIMACT's Pathway Explorer is licensed under CC-BY-NC-SA, see also [4]

References:

[1] Brown, T., Victoria, M., Zeyen, E., Hofmann, F., Neumann, F., Frysztacki, M., Hampp, J., Schlachtberger, D., & Hörsch, J. PyPSA-Eur: An open sector-coupled optimisation model of the European energy system (Version 0.9.0) [Computer software]

[2] https://pathwaysexplorer.climact.com/model-resources

[3] https://pypsa-eur.readthedocs.io/en/latest/licenses.html

[4] https://pathwaysexplorer.climact.com/legal

This dataset provides input data for generation expansion planning using stochastic programming, including uncertainty in hourly demand and generation availability. It contains:

• Input data for the distribution case study, designed for specific test scenarios.

• Input data for the European case study, based on ENTSO-E published data.

• Output files from experiments on the two-dimensional, distribution, and European case studies.