This file contains participant response data to Likert scale, open-ended responses and self-reported time taken to complete various tasks related to the extraction exercise. This Excel file also contains: 1) Examples of the Interactive HAWC Visuals that can be created after extracting data into the template. 2) The Initial Post-Extraction Survey Tool ("Survey 1") 3) The Final Post-Pilot Survey Tool ("Survey 2") 4) Survey 2 Results: Willingness to Consider Structured Data During Publication Process (Table 2) 5) Survey 1 Results: Participant Self-Reported Time Spent Performing Various Pilot Tasks (Table 3) 6) Survey 1 Results: Summary of Technical Assistance Provided by Team Members (Table 4) 7) Survey 2 Results: Participant Responses Describing Pilot's Impact on Future Research Activities (Table 5) 8) Survey 1 Results: Initial Survey Likert Scale Results (Table 6) 9) Repeat Extraction: Comparison of the First and Second Data Extraction Experience (Among the Same Participant) 10) Survey 1 Results: Problematic & Easy Fields to Extract. This dataset is associated with the following publication: Wilkins, A., P. Whaley, A. Persad, I. Druwe, J. Lee, M. Taylor, A. Shapiro, N. Blanton, C. Lemeris, and K. Thayer. Assessing author willingness to enter study information into structured data templates as part of the manuscript submission process: A pilot study. Heliyon. Elsevier B.V., Amsterdam, NETHERLANDS, 8(3): 1-9, (2022).

Three tables and one figure of this paper. Table 1 is a summary of results of implementation of data sharing policies at Elsevier, 2017–2018. Over 2,200 journals were eligible for data sharing roll-out and their editors consulted for the advised policy to be instated. Table 2 shows deposition of data during manuscript submission to Mendeley Data Repository per subject category, 2017–2018. Table 3 is a roadmap to implement FAIR data support at Elsevier: high level overview of steps necessary to support FAIR data creation and sharing. Shaded cells (green to red) refl ect if implementation is in the future (red) or already been initiated (yellow), or otherwise are live (green). Note that the status of these implementations is subject to change as we are continuously revising our implementations with input from all stakeholders in the research community. Figure 1 shows the “data Maslow hierarchy” visualizing the components of data sharing.

Secondary Data from Insights from Publishing Open Data in Industry-Academia Collaboration

Authors

Per Erik Strandberg [1], Philipp Peterseil [2], Julian Karoliny [3], Johanna Kallio [4], and Johannes Peltola [4].

[1] Westermo Network Technologies AB (Sweden).
[2] Johannes Kepler University Linz (Austria)
[3] Silicon Austria Labs GmbH (Austria).
[4] VTT Technical Research Centre of Finland Ltd. (Finland).

Description

This data is to accompany a paper submitted to Elsevier's data in brief in 2024, with the title Insights from Publishing Open Data in Industry-Academia Collaboration.

Tentative Abstract: Effective data management and sharing are critical success factors in industry-academia collaboration. This paper explores the motivations and lessons learned from publishing open data sets in such collaborations. Through a survey of participants in a European research project that published 13 data sets, and an analysis of metadata from almost 281 thousand datasets in Zenodo, we collected qualitative and quantitative results on motivations, achievements, research questions, licences and file types. Through inductive reasoning and statistical analysis we found that planning the data collection is essential, and that only few datasets (2.4%) had accompanying scripts for improved reuse. We also found that authors are not well aware of the importance of licences or which licence to choose. Finally, we found that data with a synthetic origin, collected with simulations and potentially mixed with real measurements, can be very meaningful, as predicted by Gartner and illustrated by many datasets collected in our research project.

Secondary data from Survey

The file survey.txt contains secondary data from a survey of participants that published open data sets in the 3-year European research project InSecTT.

Secondary data from Zenodo

The file secondary_data_zenodo.json contains secondary data from an analysis of data sets published in Zenodo. It is accompanied with a py-file and a ipynb-file to serve as examples.

License

This data is licenced with the Creative Commons Attribution 4.0 International license. You are free to use the data if you attribute the authors. Read the license text for details.

{"The datasets included here have been used in the research paper titled “Vehicle-to-home operation and multi-location charging of electric vehicles for energy cost optimization of households with photovoltaic system and battery energy storage,” published in the 2024 issue of the Renewable Energy journal, Volume 221, Elsevier. Detailed descriptions of the datasets and the methods used to create them can be found in a paper titled “Travel datasets to analyze the impacts of Vehicle-to-Home operation and multi-location charging of electric vehicles on household energy cost,” which has been submitted to Elsevier’s Data-in-Brief journal. It is currently under review."}

Supplementary data materials for submission: "Unit Commitment in a Federalized Power Market: A Mixed Integer Programming Approach"

Corresponding manuscript of research article has been submitted to journal.

Constituent Subfolders: * Final Results and Data: which contains the prepared input data in form of Compiled RE Data_Final and final results data obtained from the GAMS optimisation solver in Results * Code - The computational model was programmed to execute in GAMS Optimisation solver. This folder contains relevant programming files

Journal of Dermatological Science Impact Factor 2024-2025 - ResearchHelpDesk - The Journal of Dermatological Science accepts online submissions only. EES is a web-based submission and review system. Authors may submit manuscripts and track their progress through the system to publication. Reviewers can download manuscripts and submit their opinions to the editor. Editors can manage the whole submission/review/revise/publish process. The Journal of Dermatological Science publishes high quality peer-reviewed manuscripts covering the entire scope of dermatology, from molecular studies to clinical investigations. Laboratory and clinical studies which provide new information will be reviewed expeditiously and published in a timely manner. The Editor and his Editorial Board especially encourage the publication of research based on a process of bilateral feedback between the clinic and the laboratory, in which incompletely understood clinical phenomena are examined in the laboratory and the knowledge thus acquired is directly reapplied in the clinic. This continuous feedback will refine and expand our understanding of both clinical and scientific domains. Although the Journal is the official organ of the Japanese Society for Investigative Dermatology, it serves as an international forum for the work of all dermatological scientists. With an internationally renowned Editorial Board, the Journal maintains high scientific standards in the evaluation and publication of manuscripts. The Journal also publishes invited reviews, commentaries, meeting announcements and book reviews. Letters to the Editor reporting new results or even negative scientific data, if they contribute to advances in dermatology are encouraged. Letters to the Editor should be less than 1000 words with up to 2 figures or tables. Abstracting and Indexing Science Citation Index Web of Science Embase BIOSIS Citation Index PubMed/Medline Abstracts on Hygiene and Communicable Diseases Elsevier BIOBASE Biological Abstracts BIOSIS Previews Chemical Abstracts Current Awareness in Biological Sciences Current Contents Embase Index Veterinarius Inpharma Weekly Medical and Surgical Dermatology PharmacoEconomics and Outcomes News Protozoological Abstracts Reactions Weekly Review of Medical and Veterinary Entomology Review of Aromatic and Medicinal Plants Review of Medical and Veterinary Mycology Sugar Industry Abstracts Veterinary Bulletin Wheat, Barley and Triticale Abstracts Abstracts of Mycology Horticultural Science Abstracts Review of Agricultural Entomology CABI Information Cancerlit Global Health Inside Conferences ISI Science Citation Index MANTIS Social SciSearch TOXFILE BIOSIS Toxicology SIIC Data Bases Elsevier BIOBASE Current Contents - Clinical Medicine Scopus

The datasets included here have been used in the research paper titled “Techno-economic modelling for energy cost minimisation of a university campus to support electric vehicle charging with photovoltaic capacity optimisation,” published in the December 2023 issue of the Renewable Energy journal, Volume 219, Part 1, Elsevier. Detailed descriptions of the datasets and the methods used to create them can be found in a paper titled “Travel datasets for analysing the electric vehicle charging demand in a university campus,” which has been submitted to Elsevier’s Data-in-Brief journal. It is currently under review.

This repository includes the data associated to the numerical examples in the 'Applications of an isogeometric indirect boundary element method and the importance of accurate geometrical representation in acoustic problems' article submitted to the Engineering Analysis with Boundary Elements Elsevier journal. The data comprise of the CAD models, NURBS models to be solved by the isogeometric boundary element method (IGiBEM) with the proposed method in the article, meshes to be solved indirect boundary element method (iBEM) with LMS Virtual.Lab 13.7v, and the results of the numerical examples: 1) vibrating cube, 2) car hood and 3) loudspeaker. The IGiBEM software is under private license of KU Leuven and Siemens Industry Software NV, therefore, it is not shared in this repository.

This data set is for a submission of a manuscript to a peer-reviewed journal. A Rapid Viability Polymerase Chain Reaction (RV-PCR) method for detection of Yersinia pestis in water samples was developed under a research project. An final internal report for this work was cleared in the STICS in 2016. This manuscript contains a subset of data from the final report. This dataset is associated with the following publication: Shah, S., S. Kane, T. Alfaro, and A.M. Erler. Development of a Rapid Viability Polymerase Chain Reaction Method for Detection of Yersinia pestis in Water. JOURNAL OF MICROBIOLOGICAL METHODS. Elsevier Science Ltd, New York, NY, USA, 162: 21-27, (2019).

Journal of Dermatological Science Acceptance Rate - ResearchHelpDesk - The Journal of Dermatological Science accepts online submissions only. EES is a web-based submission and review system. Authors may submit manuscripts and track their progress through the system to publication. Reviewers can download manuscripts and submit their opinions to the editor. Editors can manage the whole submission/review/revise/publish process. The Journal of Dermatological Science publishes high quality peer-reviewed manuscripts covering the entire scope of dermatology, from molecular studies to clinical investigations. Laboratory and clinical studies which provide new information will be reviewed expeditiously and published in a timely manner. The Editor and his Editorial Board especially encourage the publication of research based on a process of bilateral feedback between the clinic and the laboratory, in which incompletely understood clinical phenomena are examined in the laboratory and the knowledge thus acquired is directly reapplied in the clinic. This continuous feedback will refine and expand our understanding of both clinical and scientific domains. Although the Journal is the official organ of the Japanese Society for Investigative Dermatology, it serves as an international forum for the work of all dermatological scientists. With an internationally renowned Editorial Board, the Journal maintains high scientific standards in the evaluation and publication of manuscripts. The Journal also publishes invited reviews, commentaries, meeting announcements and book reviews. Letters to the Editor reporting new results or even negative scientific data, if they contribute to advances in dermatology are encouraged. Letters to the Editor should be less than 1000 words with up to 2 figures or tables. Abstracting and Indexing Science Citation Index Web of Science Embase BIOSIS Citation Index PubMed/Medline Abstracts on Hygiene and Communicable Diseases Elsevier BIOBASE Biological Abstracts BIOSIS Previews Chemical Abstracts Current Awareness in Biological Sciences Current Contents Embase Index Veterinarius Inpharma Weekly Medical and Surgical Dermatology PharmacoEconomics and Outcomes News Protozoological Abstracts Reactions Weekly Review of Medical and Veterinary Entomology Review of Aromatic and Medicinal Plants Review of Medical and Veterinary Mycology Sugar Industry Abstracts Veterinary Bulletin Wheat, Barley and Triticale Abstracts Abstracts of Mycology Horticultural Science Abstracts Review of Agricultural Entomology CABI Information Cancerlit Global Health Inside Conferences ISI Science Citation Index MANTIS Social SciSearch TOXFILE BIOSIS Toxicology SIIC Data Bases Elsevier BIOBASE Current Contents - Clinical Medicine Scopus

Water is vital for life and local water pollution can damage the environment and affect human health. Governments and private institutions monitor and regulate water quality to protect the environment and populations. The consequences of pollution can reach far and wide, costing companies significant amounts in cleanup costs and loss of reputation. Most countries have official accredited laboratories and sampling teams that use varied technology, global expertise and local knowledge to provide water quality monitoring for different types of water and different and varied sampling locations. However, one of the main problems associated with monitoring and assessing water quality and meeting minimum standards of potability or usability is the analysis of samples based on local data. The problem lies in the fact that in many cases the data, due to the methodology or technique used or the expertise of the human resource that handles the samples, ends up configured in sets that have a large amount of missing information or data without information. This implies a problem depending on the analysis to be carried out. If you want to estimate a water quality index based on the samples, then you may have biased calculations due to the loss of information.

This dataset has been used for the generation of the manuscript: Efficient improvement for water quality analysis with large amount of missing data. D. Sierra-Porta,M. Tobón-Ospino. This manuscript is being submitted to Sustainable Production and Consumption (2022 Elsevier), Publication of the Institution of Chemical Engineers.

The Excel® spreadsheets below are submitted in support of data published in the manuscript, Yu, S., Garrabrants, A.C., DeLapp, R.C., Hubner, T., Thorneloe, S.A., and Kosson, D.S., From leaching data to release estimates: Screening and scenario assessments of electric arc furnace (EAF) slag under unencapsulated use. Journal of Hazardous Materials 479, 135522, 2024. The following spreadsheets document the final preparation of data for publication in manuscript figures and supplemental materials. LXS-Pueblo EAF Slag_Screening (1-Dec-2023) LXS-Pueblo EAF Slag_Scenario (2-Dec-2023) (update 6-Mar-2024) LXS-Pueblo EAF Slag_Scenario Wet-Dry (2-Dec-2023) LXS-Pueblo EAF Slag_Depth (2-Dec-2023). This dataset is associated with the following publication: Yu, S., A.C. Garrabrants, R.C. DeLapp, T. Hubner, S.A. Thorneloe, and D.S. Kosson. From Leaching Data to Release Estimates: Screening and Scenario Assessments of Electric Arc Furnace (EAF) Slag Under Unencapsulated Use. JOURNAL OF HAZARDOUS MATERIALS. Elsevier Science Ltd, New York, NY, USA, 479: 135522, (2024).

The dataset has been used as a part of IEEE ISBI 2021 Challenge SegPC-2021. The Challenge portal is available at: https://segpc-2021.grand-challenge.org/SegPC-2021/

The data folder contains the training set, the validation set, and the final test set, same as used for the challenge. The GT is also provided for train set and validation set but not for test set.

The test set performance can be evaluated using the leaderboard at: https://segpc-2021.grand-challenge.org/evaluation/final-test-phase/leaderboard/

Data source

Microscopic images were captured from bone marrow aspirate slides of patients diagnosed with Multiple Myeloma (MM), a type of white blood cancer. Slides were stained using Jenner-Giemsa stain, and plasma cells, which are cells of interest, must be segmented. Images were captured in raw BMP format using two cameras- 1) with a size of 2040x1536 pixels using cellSens software Version 2.1 (Olympus) attached to the microscope and 2) at a size of 1920x2560pixels from a Nikon camera attached to the microscope.

Data pre-processing methods

We have used stain color normalization using our in-house pipeline. This paper has been published recently in Medical Image Analysis Journal, Elsevier (https://doi.org/10.1016/j.media.2020.101788). The challenge participants will have to cite the papers that list this process.

Training Dataset

The dataset consists of a total of 298 images. For each image in subfolder x (under the train folder), GT corresponding to the cell of interest has been provided in subfolder y (under the folder train). There might be other cells also, but GT is provided for only cells of interest. During evaluation also, the performance will be tested only on the cells of interest. Hence, the ranking will be based only on the algorithm's performance on the cells of interest. The cells of interest are pre-identified at our end, and the evaluation code will consider only such cells. Participants are free to use any external datasets for training purposes. The training can be carried out either using the whole images or the whole image's patches. However, the evaluation will be on the whole microscopic images.

Validation Dataset

The dataset consists of a total of 200 images.

Final Phase Dataset

The final test dataset is now available. The dataset consists of a total of 277 images.

AIM

The algorithm should be able to segment each instance of the cell (nucleus + cytoplasm) of interest. Please assign the following labels to each class: Background: '0' Cytoplasm: '1' Nucleus: '2' We will provide the format of the submission file at the starting of the validation phase (Phase 2).

Important

The goal is to segment the cell of interest. In a particular microscopic image not all the instances are cells of interest. We have provided the GT for only the cells of interest. In the evaluation also the performance will be calculated using the segmented masks of the cells of interest only. Our evaluation algorithm will take care of that. You can either chose to prepare the algorithm only with the cells of interest (whose GT is provided) or with all the cells. In either case, it should work well on the cells of interest in validation and test set.

Submission and Evaluation Scripts

The submission and evaluation scripts can be found in the folder. Use 'submission.py' to make the submission file. Provide a source and destination directory. Leave rest as default. The source directory should contain the predictions in the same format as the training dataset. For example, if the test image is 100.bmp, and you have segmented 4 instances, then there have to be four images, each containing a single instance, and following the naming convention: 100_1.bmp, 100_2.bmp, 100_3.bmp, and 100_4.bmp. The predictions can follow any order. The 'submission.py' will generate a '.txt'. Rename this file 'submission.txt'. Zip this 'submission.txt' file and upload on the challenge portal. We will use 'evaluate.py' to generate the evaluation score.

Please cite the dataset as: Anubha Gupta, Ritu Gupta, Shiv Gehlot, Shubham Goswami, April 29, 2021, "SegPC-2021: Segmentation of Multiple Myeloma Plasma Cells in Microscopic Images", IEEE Dataport, doi: https://dx.doi.org/10.21227/7np1-2q42.

BibTex @data{segpc2021, doi = {10.21227/7np1-2q42}, url = {https://dx.doi.org/10.21227/7np1-2q42}, author = {Anubha Gupta; Ritu Gupta; Shiv Gehlot; Shubham Goswami }, publisher = {IEEE Dataport}, title = {SegPC-2021: Segmentation of Multiple Myeloma Plasma Cells in Microscopic Images}, year = {2021} }

IMPORTANT: If you use this dataset, please cite below publications- 1. Anubha Gupta, Rahul Duggal, Shiv Gehlot, Ritu Gupta, Anvit Mangal, Lalit Kumar, Nisarg Thakkar, and Devprakash Satpathy, "GCTI-SN: Geometry-Inspired Chemical and Tissue Invariant Stain Normalization of Microscopic Medical Imag...

The Excel® spreadsheets below are submitted in support of data published in the manuscript, Yu, S., Garrabrants, A.C., DeLapp, R.C., Hubner, T., Thorneloe, S.A., and Kosson, D.S., Evaluation of testing approaches for constituent leaching from electric arc furnace (EAF) slags. Journal of Environmental Management 373, 123892, 2025. The following spreadsheets document the final preparation of data for publication in manuscript figures and supplemental materials. • LXS-ANCCap_Pueblo_EAF Titration_Final • LXS-GM_Pueblo_Single Batch (12-Oct-2023) FINAL for MANUSCRIPT • LXS-GM_Pueblo_EAF Slag Fraction Analysis (27-Sp-2023). This dataset is associated with the following publication: Yu, S., A.C. Garrabrants, R.C. DeLapp, T. Hubner, S.A. Thorneloe-Howard, and D.S. Kosson. Evaluation and Testing Approaches for Constituent Leaching from Electric Arc Furnace (EAF) Slags. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 373: 123892, (2025).

This package of supplementary research data and appendices are made available in connection with the research article described below and submitted for publication (currently under review) in the Cities journal (Cities | Journal | ScienceDirect.com by Elsevier).

The dataset includes bibliometric analysis source files obtained from the Scopus and Web of Science academic databases, which served as input for the analysis process leading to the findings reported in the associated research article. Additionally, it contains R programming language script files used in the analysis, along with step-by-step instructions for conducting the analysis.

Journal of Dermatological Science Abstract & Indexing - ResearchHelpDesk - The Journal of Dermatological Science accepts online submissions only. EES is a web-based submission and review system. Authors may submit manuscripts and track their progress through the system to publication. Reviewers can download manuscripts and submit their opinions to the editor. Editors can manage the whole submission/review/revise/publish process. The Journal of Dermatological Science publishes high quality peer-reviewed manuscripts covering the entire scope of dermatology, from molecular studies to clinical investigations. Laboratory and clinical studies which provide new information will be reviewed expeditiously and published in a timely manner. The Editor and his Editorial Board especially encourage the publication of research based on a process of bilateral feedback between the clinic and the laboratory, in which incompletely understood clinical phenomena are examined in the laboratory and the knowledge thus acquired is directly reapplied in the clinic. This continuous feedback will refine and expand our understanding of both clinical and scientific domains. Although the Journal is the official organ of the Japanese Society for Investigative Dermatology, it serves as an international forum for the work of all dermatological scientists. With an internationally renowned Editorial Board, the Journal maintains high scientific standards in the evaluation and publication of manuscripts. The Journal also publishes invited reviews, commentaries, meeting announcements and book reviews. Letters to the Editor reporting new results or even negative scientific data, if they contribute to advances in dermatology are encouraged. Letters to the Editor should be less than 1000 words with up to 2 figures or tables. Abstracting and Indexing Science Citation Index Web of Science Embase BIOSIS Citation Index PubMed/Medline Abstracts on Hygiene and Communicable Diseases Elsevier BIOBASE Biological Abstracts BIOSIS Previews Chemical Abstracts Current Awareness in Biological Sciences Current Contents Embase Index Veterinarius Inpharma Weekly Medical and Surgical Dermatology PharmacoEconomics and Outcomes News Protozoological Abstracts Reactions Weekly Review of Medical and Veterinary Entomology Review of Aromatic and Medicinal Plants Review of Medical and Veterinary Mycology Sugar Industry Abstracts Veterinary Bulletin Wheat, Barley and Triticale Abstracts Abstracts of Mycology Horticultural Science Abstracts Review of Agricultural Entomology CABI Information Cancerlit Global Health Inside Conferences ISI Science Citation Index MANTIS Social SciSearch TOXFILE BIOSIS Toxicology SIIC Data Bases Elsevier BIOBASE Current Contents - Clinical Medicine Scopus

This dataset includes 1032 runs from a biomass downdraft gasifier integrated with power production unit (BG-PP) that is fed by 86 different types of biomasses from different groups (e.g. wood and woody biomasses, herbaceous and agricultural biomasses, animal biomasses, mixed biomasses and contaminated biomasses) and under various operating conditions. The dataset covers elemental and proximate analysis of various biomasses, operation conditions and the net output power from the BG-PP in each case/run. This article is via our another Elsevier journal as a co-submission, titled “Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant”. In fact, this dataset has been used to train and test the developed Artificial neural network modeling of a downdraft BG-PP in our paper.

Abstract

We developed a methodology to detect the oldest scholarly reference added to Wikipedia articles by which a certain paper is uniquely identifiable as the "first appearance of the scholarly reference." We identified the first appearances of 923,894 scholarly references (611,119 unique DOIs) in180,795 unique pages on English Wikipedia as of March 1, 2017, and stored them in the dataset. Moreover, we assessed the precision of the dataset, which was and it was a high precision regardless of the research field.

Data Records

The data format of the dataset is JSON lines, where each line is a single record. In this dataset, we detected the first appearance of each scholarly reference added to Wikipedia articles. If there are multiple references corresponding to the same paper on the same page, only the oldest one is collected. Sample of the record is the following.

doi -- DOI corresponding to the paper (String), e.g., "10.1006/anbe.1996.0497"

paper_type -- Document type of the paper (String), e.g., "journal-article"

paper_container_title -- Journal title, book title, or proceedings title (Array of String), e.g., ["Animal Behaviour"]

paper_publisher -- Publisher name (String), e.g., "Elsevier BV"

paper_title -- Paper title (Array of String), e.g., ["Push or pull: an experimental study on imitation in marmosets"]

paper_published_year -- Published year (String), e.g., "1997"

paper_issue -- Issue number (String), e.g., "4"

paper_volume -- Volume number (String), e.g., "54"

paper_page -- Page numbers (String), e.g., "817-831"

paper_author -- Authors information consisted of given and family names, sequences (order in author names), and affiliations (Array of JSON), e.g., [{"given":"THOMAS", "family":"BUGNYAR", "sequence":"first", "affiliation":[]}, {"given":"LUDWIG", "family":"HUBER", "sequence":"additional", "affiliation":[]}]

issn -- ISSN related to the paper (Array of String), e.g., ["0003-3472"]

research_field -- Research fields from ESI categories (Array of String), e.g., ["PLANT & ANIMAL SCIENCE"]

page_id -- Page id (String), e.g., "577858"

page_title -- Page title (String), e.g., "Imitation"

revision_id -- Revision id (String), e.g., "203309031"

revision_timestamp -- Revision timestamp (String), e.g., "2008-04-04 15:54:09 UTC"

revision_comment -- Revision comment (edit summary) (String), e.g., "/* Animal Behaviour */"

editor_name -- Wikipedia editor's name (String), e.g., "Nicemr"

editor_type -- Type of the editor (String), e.g., "User"

References

Kikkawa, J., Takaku, M. & Yoshikane, F. Dataset of first appearances of the scholarly bibliographic references on Wikipedia articles (submitted to Scientific Data).

FUNDING

JSPS KAKENHI Grant Number JP20K12543

JSPS KAKENHI Grant Number JP21K21303

✅ Journal of Dermatological Science ISSN - ResearchHelpDesk - The Journal of Dermatological Science accepts online submissions only. EES is a web-based submission and review system. Authors may submit manuscripts and track their progress through the system to publication. Reviewers can download manuscripts and submit their opinions to the editor. Editors can manage the whole submission/review/revise/publish process. The Journal of Dermatological Science publishes high quality peer-reviewed manuscripts covering the entire scope of dermatology, from molecular studies to clinical investigations. Laboratory and clinical studies which provide new information will be reviewed expeditiously and published in a timely manner. The Editor and his Editorial Board especially encourage the publication of research based on a process of bilateral feedback between the clinic and the laboratory, in which incompletely understood clinical phenomena are examined in the laboratory and the knowledge thus acquired is directly reapplied in the clinic. This continuous feedback will refine and expand our understanding of both clinical and scientific domains. Although the Journal is the official organ of the Japanese Society for Investigative Dermatology, it serves as an international forum for the work of all dermatological scientists. With an internationally renowned Editorial Board, the Journal maintains high scientific standards in the evaluation and publication of manuscripts. The Journal also publishes invited reviews, commentaries, meeting announcements and book reviews. Letters to the Editor reporting new results or even negative scientific data, if they contribute to advances in dermatology are encouraged. Letters to the Editor should be less than 1000 words with up to 2 figures or tables. Abstracting and Indexing Science Citation Index Web of Science Embase BIOSIS Citation Index PubMed/Medline Abstracts on Hygiene and Communicable Diseases Elsevier BIOBASE Biological Abstracts BIOSIS Previews Chemical Abstracts Current Awareness in Biological Sciences Current Contents Embase Index Veterinarius Inpharma Weekly Medical and Surgical Dermatology PharmacoEconomics and Outcomes News Protozoological Abstracts Reactions Weekly Review of Medical and Veterinary Entomology Review of Aromatic and Medicinal Plants Review of Medical and Veterinary Mycology Sugar Industry Abstracts Veterinary Bulletin Wheat, Barley and Triticale Abstracts Abstracts of Mycology Horticultural Science Abstracts Review of Agricultural Entomology CABI Information Cancerlit Global Health Inside Conferences ISI Science Citation Index MANTIS Social SciSearch TOXFILE BIOSIS Toxicology SIIC Data Bases Elsevier BIOBASE Current Contents - Clinical Medicine Scopus

The Scopus database (Elsevier) was used to collect all relevant studies for this bibliometric analysis. Data was obtained as a .csv file; it was downloaded from Scopus and was exported by SciVal to Microsoft Excel for a presentation using tables for more detailed analysis. The citations and the number of papers for the most productive institutions, authors, countries, and journals publishing scientific papers were analyzed on the use of gamma knife radiosurgery for brain tumors.The Scopus database (Elsevier) was used to collect all relevant studies for this bibliometric analysis. Data was obtained as a .csv file; it was downloaded from Scopus and was exported by SciVal to Microsoft Excel for a presentation using tables for more detailed analysis. The citations and the number of papers for the most productive institutions, authors, countries, and journals publishing scientific papers were analyzed on the use of gamma knife radiosurgery for brain tumors.