Lancaster University Showcase

This study draws on a panel dataset from the World Development Indicators (WDI) covering 1995 to 2022 across diverse developing regions: Sub-Saharan Africa, South Asia, Latin America & the Caribbean, East Asia & Pacific, Middle East & North Africa, and Europe & Central Asia. High-income countries (those with a GNI per capita above $14,005) are filtered out, sharpening the analysis on the development gap. The Human Development Index (HDI) serves as the dependent variable given its comprehensive portrayal of multidimensional development (education, health, income), superior to purely economic indicators like GNI despite omitting political dimensions (UNDP, 2022). Core explanatory variables include educational attainment (school life expectancy), institutional quality (derived and adjusted from Worldwide Governance Indicators), GDP per capita, unemployment rate, investment (gross fixed capital formation), and life expectancy. Statistically insignificant or highly collinear variables were omitted to ensure methodological parsimony

The Grant Proposal Game: An Experimental Study on Herding and Divergent Behaviors in Competition Experimental Economics By Mugerman, Winter, Yafeh Overview The files provided contain the data and code (Excel and Stata) required for the replication of all the tables and figures contained in the body of our paper, as well as the raw data for the alternative experiment presented in Appendix B. Data Availability and Provenance Statements The experiment was conducted using oTree. Participants were recruited through the online platform Prolific, which also collected general and anonymous demographic data handled payment at the end of the experiment. Upon enrolling in the experiment, each participant received a link to join the session, and all players in a session played simultaneously. Participation was fully anonymous, both with regard to other participants and with regard to the researchers. Full details of the experimental instructions and procedure are provided in a separate document, as well as in our full research paper (under Section 3 – “Experiment” and Appendix A – “Experiment Screenshots”). The “Coin Experiment” in Appendix B was also conducted using oTree. Participants were recruited from among the student body of the Hebrew University of Jerusalem, and were supervised via a Zoom meeting. The participant pool was balanced in terms of gender, and all participants were required to have a proficient command of the Hebrew language. Session scheduling ensured consistency in terms of weekdays and hours to minimize potential external influences. Multiple supervisors were present throughout the sessions to provide instructions, address queries, and ensure compliance with the experiment's guidelines. Further details are provided in a separate document, and in Appendix B of the full research paper. Simulation data was generated by the authors of the paper using Excel. The authors of this paper hold exclusive rights to the use and distribution of all data used as part of this research. Any additional use of the data by third parties requires the written approval of the authors.

The dataset consists of three files: The "Jane and Jill" oTree experiment data The Prolific demographic data The Coin Lab experiment data

Supplementary material for the pre-print paper "Hybrid Safe Reinforcement Learning: Tackling Distribution Shift and Outliers with the Student-t's Process." submitted to the Journal of Neurcomputing (November 2024).

LUSC (lung squamous cell carcinoma) Dataset used for demonstrating CanSeer is available as "Supplementary Dataset", and all the input files, output files, analyses, parameters, code and other findings are given in "Supplementary Data"

This is the Supplementary dataset for the PLOS ONE manuscript, "Parametric analysis on the global design of flexible riser under different environmental conditions using OrcaFlex". It holds some files with more description on the work carried out and source files for the results, as they exist in the paper too. Permission was obtained to add an image used to illustrate ship motion. I would have also added the permission obtained to reuse a ship image as well as the source file for it, but due to personal information protection, it was removed. The main software used in this investigation is OrcaFlex so we made the results and discussions on it. There is an OrcaFlex main file which is also publicly available that was added but it is different from the base case files used. However, this can be used to develop the model using the materials shared in the manuscript. I also added the source files for all the images in the paper including the images in TIFF and DOC formats. We thank all the reviewers and technical support on this paper.

Spreadsheets for the 7 scenarios investigated in the article: "Aspen Plus® process simulation model of the biomass ash-based treatment of anaerobic digestate for production of fertilizer and upgradation of biogas". A comparison of the following 7 process strategies is mentioned in the above article: Case 1 is the foundation case (labelled as untreated MD) that served as benchmark and it implied the production of MD using the original PSM of Rajendran et al. [11]. In Case 2 a stream of pure hydrochloric acid was incorporated at a 0.1000 times the flowrate of the MD towards the ionization reactor (1HCl:10MD). In Case 3 a stream of hydrochloric acid was incorpo-rated at 0.1176 times the flowrate of the MD towards the stoichiometric-equilibria reactor (1HCl:8.5MD). In Case 4 a stream of hydrochloric acid was incorporated at 0.1212 times the flowrate of the MD towards the ionization reactor (1HCl:8.25MD). The remaining 3 cases are built on Case 3 (i.e. considering the previous acidification of the MD with the dose of 3.18 mEq HCl/g). In Case 5 the stream of SSA (Table 1) was incorpo-rated at 0.0040 times the flowrate of the MD towards the stoichiometric-equilibria reactor (1SSA:1.76HCl:15MD). In Case 6 the stream of ash (Table 1) was incorporated at 0.0060 times the flowrate of the MD towards the ionization reactor (1SSA:1.18HCl:10MD). In Case 7 the stream of ash was incorporated at 0.0080 times the flowrate of the DM to the stoi-chiometric-equilibria reactor (1SSA:0.88HCl:7.5MD). Additionally, MS Word file summarizes the most relevant data: Table S1, Mass balance of the nutrients monitored in the Aspen Plus® simulations. (1/3); Table S2, Mass balance of the nutrients monitored in the Aspen Plus® simulations. (2/3); Table S3, Mass balance of the nutrients monitored in the Aspen Plus® simulations. (3/3); Table S4, List of the components in the PSM of Rajendran et al. [11]. (1/3); Table S5, List of the components in the PSM of Rajendran et al. [11]. (2/3); Table S6, List of the components in the PSM of Rajendran et al. [11]. (3/3).

These datasets merge the information at the 4-digit industry level on the number of mechanical and electrical engineers with the total factor productivity measures, both from the Bureau of Labor Statistics. They are divided into three different datasets as each treated industry needed a different control group. There is also data regarding the status of firms in the industry (number of firms, establishments, firms size, etc.)

Data associated with "Long-term temporal trends in gastrointestinal parasite infection in wild Soay sheep", published in the journal Parasitology. Data consist of samples collected from individuals in the Augusts of 1988-2018 and the prevalence and abundance of different parasites: fec (strongyles), foc (coccidia), nematodirus, trichuris, capillaria, and moniezia. The suffic "-prev" indicates that this is a variable indicating the presence or absence of a given parasite. The "anthelmintic" variable is a binary variable stating whether or not an animal had been treated with an anthelmintic in the 12 months prior to sample collection. SOAY SHEEP PROJECT DATA REUSE: The attached file(s) contain data derived from the long term field project monitoring individual Soay sheep on St Kilda and their environment. This is a request to please let us know if you use them. Several people have spent the best part of their careers collecting the data. If you plan to analyse the data, there are a number of reasons why it would be very helpful if you could contact Dan Nussey (dan.nussey@ed.ac.uk) before doing so. [NB. If you are interested in analysing the detailed project data in any depth you may find it helpful to have our full relational database rather than the file(s) available here. If so, then we have a simple process for bringing you onto the project as a collaborator.] 1) The data can be subject to change due to updates in the pedigree, merging of records, occasional errors and so on. 2) The data are complex and workers who do not know the study system may benefit from advice when interpreting it. 3) At any one time a number of people within the existing project collaboration are analysing data from this project. Someone else may already be conducting the analysis you have in mind and it is desirable to prevent duplication of effort. 4) In order to maintain funding for the project(s), every few years we have to write proposals for original analyses to funding agencies. It is therefore very helpful for those running the project to know what data analyses are in progress. 5) Individual identifiers may vary relative to other data archives from papers using the individual-level data.

This dataset on the scientometrics of “Environmental Valuation” is presented. It shows supplementary data that includes author details, publication data, funding information, affiliations, keywords, word clouds and retrieved data from Web of Science (WoS) and SCOPUS databases. This dataset was used for the paper by applying Scientometric science, which is based on bibliometric analysis. The results were used for establishing research patterns, visualisation data and identifying progress on “Environmental Valuation” . The data were also included as supplementary data subjected to a scientometric study which looked at various parameters like publication years, authorship, and publication's country base to understudy the research pattern. See the full paper in: Olukolajo, M.A., Oyetunji A.K., Amaechi, C.V. (2022). A Scientometric Review Of Environmental Valuation Research With a Path For The Future. Heliyon. 2022.