Summary dataset for habitat modeling manuscript published in STOTEN 2022. This dataset is associated with the following publication: Mitchell, M.E., T. Newcomer-Johnson, J. Christensen, W. Crumpton, S. Richmond, B. Dyson, T.J. Canfield, M. Helmers, D. Lemke, M. Lechtenberg, D. Green, and K.J. Forshay. Potential of water quality wetlands to mitigate habitat losses from agricultural drainage modernization. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 838 part4: 156358, (2022).

This entry contains code and data that was used in the publication: "Adoption of Transparency and Openness Promotion (TOP) guidelines across journals" submitted in Publications journal. #IDEA: This project was about analyzing policies of two thousand journals within the framework of eight TOP standards: data citation, transparency of data, material, code and design and analysis, replication, plan and study pre-registration, and two effective interventions: “Registered reports” and “Open science badges”. # MATERIALS & METHODS We downloaded the TOP Factor (v33, 2022-08-29 3:12 PM) metric from the https://osf.io/kgnva/files/osfstorage/5e13502257341901c3805317 website and analyzed its content with an in-house R script (in this repo): 1) SCRIPT: fig1_Analyzing_journals_policies_and_TOP_guidelines.R 2) SCRIPT: Figure2a_b_TOP_impl_journal_statistist_0_1_piechart_barplot.R In order to get statistics about implementation of the TOP guidelines across discipline-specific journals, we extracted information about journal’s disciplines from the Scopus content database. We downloaded SCOPUS content coverage from the https://www.elsevier.com/solutions/scopus/how-scopus-works/content?dgcid=RN_AGCM_Sourced_300005030 (existJuly2022.xlsx) and used the first Sheet. We identified match between those 2 tables: 3) SCRIPT: Rscript_overlapping_TOP_dataframe_and_SCOPUS_db.R And resulted in Overlap_SCOPUS_TOP.rds file And performed visualization and statistics: 4) SCRIPT: Fig_3_Tab2_Defining_science_disciplines_plus_plot.R #RESULTS Submitted to Publications 30.9.2022.

The United Nations Sustainable Development Goals (SDGs) challenge the global community to build a world where no one is left behind.

Since 2018, Elsevier has generated SDG search queries to help researchers and institutions track and demonstrate progress toward the SDG targets. In the past 5 years, these queries, along with the university’s own data and evidence supporting progress and contributions to the particular SDG outside of research-based metrics, are used for the THE Impact Rankings.

For 2023, the SDGs use the exact same search query and ML algorithm as the Elsevier 2022 SDG mappings, with only minor modifications to five SDGs, namely SDG 1, 4, 5, 7 and 14. In these cases, the queries were shortened by removing exclusion lists based on journal identifiers. These exclusion lists often contained thousands of items to filter out content in journals that were not core to the SDGs.

To replicate the effect of these journal exclusions, sets of keywords were used to closely mimic the effects the journal exclusions had on the SDG content, while greatly reducing the overall query size and complexity. By following this approach, we were able to limit the changes to the publications in each SDG by less than 2 percent for most SDGs, while reducing the query size by 50 percent or more.

These shortened queries also have the added benefit of running faster in Scopus, allowing further analysis of the SDG data to be done more easily.

For each SDG, the full search query, along with further details about the top keyphrases, subfields, journals and keyphrases are available for download.

ADAM-Data-Repository This repository contains all the data needed to run the case studies for the ADAM manuscript. ## Biogas production The directory "biogas" contains all data for the biogas production case studies (Figs 13 and 14). Specifically, "biogas/biogas_x" contains the data files for the scenario where "x" is the corresponding Renewable Energy Certificates (RECs) value. ## Plastic waste recycling The directory "plastic_waste" contains all data for the plastic waste recycling case studies (Figs 15 and 16). Different scenarios share the same supply, technology site, and technology candidate data, as specified by the "csv" files under "plastic_waste". Each scenario has a different demand data file, which is contained in "plastic_waste/Elec_price" and "plastic_waste/PET_price". ## How to run the case studies In order to run the case studies, one can create a new model in ADAM and upload appropriate CSV file at each step (e.g. upload biogas/biogas_0/supplydata197.csv in step 2 where supply data are specified). This dataset is associated with the following publication: Hu, Y., W. Zhang, P. Tominac, M. Shen, D. Göreke, E. Martín-Hernández, M. Martín, G.J. Ruiz-Mercado, and V.M. Zavala. ADAM: A web platform for graph-based modeling and optimization of supply chains. COMPUTERS AND CHEMICAL ENGINEERING. Elsevier Science Ltd, New York, NY, USA, 165: 107911, (2022).

A Large-scale Dataset of STEM Science as PROCESS, METHOD, MATERIAL, and DATA Named Entities This repository hosts data as a follow-up study to the following publications D'Souza, J., Hoppe, A., Brack, A., Jaradeh, M., Auer, S., & Ewerth, R. (2020). The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources. In Proceedings of The 12th Language Resources and Evaluation Conference (pp. 2192–2203). European Language Resources Association. Brack, A., D’Souza, J., Hoppe, A., Auer, S., Ewerth, R. (2020). Domain-Independent Extraction of Scientific Concepts from Research Articles. In: , et al. Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12035. Springer, Cham. https://doi.org/10.1007/978-3-030-45439-5_17 Supporting dataset link https://data.uni-hannover.de/dataset/stem-ecr-v1-0 Description Roughly 60,000 titles and abstracts of scholarly articles with the CC-BY redistributable license were downloaded from Elsevier. The articles spanned 10 STEM domains which were the most prolific on Elsevier viz., Agriculture, Astronomy, Biology, Chemistry, Computer Science, Earth Science, Engineering, Material Science, and Mathematics. The STEM NER system reported in the publication above was applied on these articles. An automatically extracted dataset of 4 typed entities, viz., Process, Method, Material, and Data was created. What this repository contains?

These data are an update to 2012-2020 Greenhouse Gas National- and State-Level Emission Totals by Industry (https://doi.org/10.23719/1529805). Data for 2021 and 2022 are added. The primary emissions source was updated to use the U.S. GHG Inventory 2024 report. The industry classifications were updated to use 2017 North American Industry Classification Codes. Totals by industry and state are not included. The code used to generate the datasets is available in the FLOWSA v2.0.3 tool (https://github.com/USEPA/flowsa/tree/v2.0.3). Values are given in total kilograms emitted for the given year, sector and location. Please see the related publication for more background information. This dataset is associated with the following publication: Young, B., C. Birney, and W.W. Ingwersen. Dataset of 2012-2020 U.S. National- and State-Level Greenhouse Gas Emissions by Sector. Data in Brief. Elsevier B.V., Amsterdam, NETHERLANDS, 53: 110173, (2024).

This dataset contains one of the main outputs of a series of studies on international migration among German-affiliated researchers based on Scopus bibliometric data. The migration flows are inferred from the changes of affiliation addresses in Scopus publications from 1996-2020. Scopus data is owned and maintained by Elsevier.

This dataset is provided under a CC BY-NC-SA Creative Commons v 4.0 license (Attribution-NonCommercial-ShareAlike). This means that other individuals may remix, tweak, and build upon these data non-commercially, as long as they provide citations to this data repository (https://doi.org/10.6084/m9.figshare.18433139) and the two referenced articles listed below, and license the new creations under identical terms.

For more details about the study, please refer to the following two articles.

Zhao, X., Aref, S., Zagheni, E., & Stecklov, G., Return migration of German-affiliated researchers: analyzing departure and return by gender, cohort, and discipline using Scopus bibliometric data 1996–2020. Scientometrics (2022). https://doi.org/10.1007/s11192-022-04351-4

Zhao, X., Aref, S., Zagheni, E., & Stecklov, G., International migration in academia and citation performance: An analysis of German-affiliated researchers by gender and discipline using Scopus publications 1996-2020. In: Glänzel W, Heeffer S, Chi PS, et al (eds) Proceedings of the 18th International Conference on Scientometrics and Informetrics. ISSI, Leuven, p 1369–1380, (2021) https://arxiv.org/abs/2104.12380, https://kuleuven.app.box.com/s/kdhn54ndlmwtil3s4aaxmotl9fv9s329

The dataset is provided in a comma-separated values file (.csv file). Each row represents the international movement of a Scopus-published researcher from a country (Source) to another country (Target) in a specific year (move_year). The most likely gender and the most likely discipline for each researchers is inferred using data-driven methods as described in Zhao et al. (2022).

Description of variables (columns of the csv file): "Source": the country where the researcher has moved from "Target": the country where the researcher has moved to "move_year": inferred year of the move "gender": inferred gender "discipline": inferred discipline

The binary genders inferred and used in our analysis do not refer directly to the sex of the researchers, assigned at birth or self-chosen; nor do they refer to the socially assigned or self-chosen genders of the authors.

The data can be used to produce migration models or possibly other measures, estimates, and analyses.

This data is associated with the manuscript: "Transport and fate of aqueous film forming foam in an urban estuary" published in Environmental Pollution, vol. 300, 01May22. https://doi.org/10.1016/j.envpol.2022.118963. This dataset is associated with the following publication: Katz, D., J. Sullivan, K. Rosa, C. Gardiner, A. Robuck, R. Lohmann, C. Kincaid, and M. Cantwell. Transport and Fate of Aqueous Film Forming Foam in an Urban Estuary. ENVIRONMENTAL POLLUTION. Elsevier Science Ltd, New York, NY, USA, 300: 118963, (2022).

EPA generated data included satellite detections of cyanobacteria frequency that was previously published in https://www.sciencedirect.com/science/article/pii/S1470160X21004878. All other data and analysis was through co-authors at University of Wisconsin-Madison. This dataset is associated with the following publication: Zhang, J., D.J. Phaneuf, and B. Schaeffer. Property values and cyanobacterial algal blooms: Evidence from satellite monitoring of Inland Lakes. ECOLOGICAL ECONOMICS. Elsevier Science Ltd, New York, NY, USA, 199: 107481, (2022).