Lancaster University Showcase

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"

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.

This dataset on the scientometrics of Plastic Pollution 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 developmental issues on marine hoses. The data on Plastic Pollution were also included, as the data were 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: Amaechi, C.V. (2022). Sustainable control alternatives for Plastic Pollution with implications of COVID-19. Heliyon. 2022.

This dataset on the scientometrics of teaching in HEA- group learning (Part 2) is presented. It shows supplementary Data that includes keywords, word clouds and retrieved data from SCOPUS database. This dataset was used for the paper by applying Scientometric science, which is based on bibliometric analysis. The data included different parameters like publication authors, and publication's geographical location by country to investigate the research pattern. See the full paper in: Amaechi, C.V.; Amaechi, E.C.; Onumonu, U.P.; Kgosiemang, I.M. Systematic review and Annotated Bibliography on Teaching in Higher Education Academy via Group Learning to adapt with COVID-19. Education Sciences 2022, 16, under review.

This dataset on the scientometrics of teaching in HEA- online learning (Part 1) is presented. It shows supplementary Data that includes keywords, word clouds and retrieved data from SCOPUS database. This dataset was used for the paper by applying Scientometric science, which is based on bibliometric analysis. The data included different parameters like publication authors, and publication's geographical location by country to investigate the research pattern. See the full paper in: Amaechi, C.V.; Amaechi, E.C.; Oyetunji, A.K.; Kgosiemang, I.M. Scientific Review and Annotated Bibliography of Teaching in Higher Education Academy (HEA) on Online Learning: Adapting to the COVID-19 Pandemic. Sustainability 2022, 14, accepted.

This dataset is on study is on the for the structural damage of flexible riser's armour. It includes the raw data for the simulation conducted on the finite element model (FEM) using ANSYS Mechanical, images and video output from the simulation

This dataset on the scientometrics of composite risers is presented. It shows supplementary Data that includes keywords, word clouds and retrieved data from SCOPUS and Web of Science databases. This dataset was used for the paper by applying Scientometric science, which is based on bibliometric analysis. VOS viewer was used for establishing research patterns, visualising maps and identifying transcendental issues on composite risers. The data on composite risers were also included, as the data were subjected to a scientometric study which looked at different parameters like publication authors, and publication's geographical location by country to investigate the research pattern. See the full paper in: Amaechi, C.V., et al. (2022). A scientometric review on mapping research knowledge for composite marine risers. JNGSE 2022, under review.