Legacy product - no abstract available

The data set was used to produce tables and figures in paper.

This dataset is associated with the following publications: Lytle, D., S. Pfaller, C. Muhlen, I. Struewing, S. Triantafyllidou, C. White, S. Hayes, D. King, and J. Lu. A Comprehensive Evaluation of Monochloramine Disinfection on Water Quality, Legionella and Other Important Microorganisms in a Hospital. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 189: 116656, (2021). Lytle, D., C. Formal, K. Cahalan, C. Muhlen, and S. Triantafyllidou. The Impact of Sampling Approach and Daily Water Usage on Lead Levels Measured at the Tap. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 197: 117071, (2021).

description: Paper serves as non-technical introduction to series of papers on the same drinking water study. This dataset is associated with the following publication: Kolpin, D., S. Glassmeyer, and E. Furlong. An introduction to joint research by the USEPA and USGS on contaminants of emerging concern in source and treated drinking waters of the United States. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 579: 1608–1609, (2017).; abstract: Paper serves as non-technical introduction to series of papers on the same drinking water study. This dataset is associated with the following publication: Kolpin, D., S. Glassmeyer, and E. Furlong. An introduction to joint research by the USEPA and USGS on contaminants of emerging concern in source and treated drinking waters of the United States. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 579: 1608–1609, (2017).

Description Data and figures for paper published in PLOS ONE:Larivière, V., Haustein, S. & Mongeon, P. (2015). The oligopoly of academic publishers in the digital era. PLoS ONE, 10(6), e0127502. doi:10.1371/journal.pone.0127502

Abstract. The consolidation of the scientific publishing industry has been the topic of much debate within and outside the scientific community, especially in relation to major publishers’ high profit margins. However, the share of scientific output published in the journals of these major publishers, as well as its evolution over time and across various disciplines, has not yet been analyzed. This paper provides such analysis, based on 45 million documents indexed in the Web of Science over the period 1973-2013. It shows that in both natural and medical sciences (NMS) and social sciences and humanities (SSH), Reed-Elsevier, Wiley-Blackwell, Springer, and Taylor & Francis increased their share of the published output, especially since the advent of the digital era (mid-1990s). Combined, the top five most prolific publishers account for more than 50% of all papers published in 2013. Disciplines of the social sciences have the highest level of concentration (70% of papers from the top five publishers), while the humanities have remained relatively independent (20% from top five publishers). NMS disciplines are in between, mainly because of the strength of their scientific societies, such as the ACS in chemistry or APS in physics. The paper also examines the migration of journals between small and big publishing houses and explores the effect of publisher change on citation impact. It concludes with a discussion on the economics of scholarly publishing.

Academic journals indicators developed from the information contained in the Scopus database (Elsevier B.V.). These indicators can be used to assess and analyze scientific domains.

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

RibFrac dataset is a benchmark for developping algorithms on rib fracture detection, segmentation and classification. We hope this large-scale dataset could facilitate both clinical research for automatic rib fracture detection and diagnoses, and engineering research for 3D detection, segmentation and classification.

Due to size limit of zenodo.org, we split the whole RibFrac Training Set into 2 parts; This is the Training Set Part 1 of RibFrac dataset, including 300 CTs and the corresponding annotations. Files include:

1. ribfrac-train-images-1.zip: 300 chest-abdomen CTs in NII format (nii.gz).
2. ribfrac-train-labels-1.zip: 300 annotations in NII format (nii.gz).
3. ribfrac-train-info-1.csv: labels in the annotation NIIs.
   • public_id: anonymous patient ID to match images and annotations.
   • label_id: discrete label value in the NII annotations.
   • label_code: 0, 1, 2, 3, 4, -1
     • 0: it is background
     • 1: it is a displaced rib fracture
     • 2: it is a non-displaced rib fracture
     • 3: it is a buckle rib fracture
     • 4: it is a segmental rib fracture
     • -1: it is a rib fracture, but we could not define its type due to ambiguity, diagnosis difficulty, etc. Ignore it in the classification task.

If you find this work useful in your research, please acknowledge the RibFrac project teams in the paper and cite this project as:

Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020). (DOI)

or using bibtex

@article{ribfrac2020,
title={Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet},
author={Jin, Liang and Yang, Jiancheng and Kuang, Kaiming and Ni, Bingbing and Gao, Yiyi and Sun, Yingli and Gao, Pan and Ma, Weiling and Tan, Mingyu and Kang, Hui and Chen, Jiajun and Li, Ming},
journal={EBioMedicine},
year={2020},
publisher={Elsevier}
}

The RibFrac dataset is a research effort of thousands of hours by experienced radiologists, computer scientists and engineers. We kindly ask you to respect our effort by appropriate citation and keeping data license.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

description: Contains data related to Arsenate and Arsenite injections into chlorinated distribution system simulator. Contains data related to model to predict arsenate and arsenite aqueous and wall concentrations within a chlorinated water distribution system. This dataset is associated with the following publication: Burkhardt, J., J. Szabo, S. Klosterman, J. Hall, and R. Murray. Modeling Fate and Transport of Arsenic in a Chlorinated Distribution System. ENVIRONMENTAL MODELLING AND SOFTWARE. Elsevier Science Ltd, New York, NY, USA, 93(1): 322-331, (2017).; abstract: Contains data related to Arsenate and Arsenite injections into chlorinated distribution system simulator. Contains data related to model to predict arsenate and arsenite aqueous and wall concentrations within a chlorinated water distribution system. This dataset is associated with the following publication: Burkhardt, J., J. Szabo, S. Klosterman, J. Hall, and R. Murray. Modeling Fate and Transport of Arsenic in a Chlorinated Distribution System. ENVIRONMENTAL MODELLING AND SOFTWARE. Elsevier Science Ltd, New York, NY, USA, 93(1): 322-331, (2017).

This FAIRsharing record describes: The Information for Authors page contains a broad range of guidelines for publishing in The Lancet. With regards to data deposition, novel gene sequences should be deposited in a public database (GenBank, EMBL, or DDBJ), and the accession number provided. Authors of microarray papers should include in their submission the information recommended by the MIAME guidelines. Authors should also submit their experimental details to one of the publicly available databases: ArrayExpress or GEO.

This repository contains supplementary materials for the following journal paper:

Valdemar Švábenský, Jan Vykopal, Pavel Seda, Pavel Čeleda. Dataset of Shell Commands Used by Participants of Hands-on Cybersecurity Training. In Elsevier Data in Brief. 2021. https://doi.org/10.1016/j.dib.2021.107398

How to cite

If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).

@article{Svabensky2021dataset, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and Seda, Pavel and \v{C}eleda, Pavel}, title = {{Dataset of Shell Commands Used by Participants of Hands-on Cybersecurity Training}}, journal = {Data in Brief}, publisher = {Elsevier}, volume = {38}, year = {2021}, issn = {2352-3409}, url = {https://doi.org/10.1016/j.dib.2021.107398}, doi = {10.1016/j.dib.2021.107398}, }

The data were collected using a logging toolset referenced here.

Attached content

Dataset (data.zip). The collected data are attached here on Zenodo. A copy is also available in this repository.

Analytical tools (toolset.zip). To analyze the data, you can instantiate the toolset or this project for ELK.

Version history

Version 1 (https://zenodo.org/record/5137355) contains 13446 log records from 175 trainees. These data are precisely those that are described in the associated journal paper. Version 1 provides a snapshot of the state when the article was published.

Version 2 (https://zenodo.org/record/5517479) contains 13446 log records from 175 trainees. The data are unchanged from Version 1, but the analytical toolset includes a minor fix.

Version 3 (https://zenodo.org/record/6670113) contains 21762 log records from 275 trainees. It is a superset of Version 2, with newly collected data added to the dataset.

The current Version 4 (https://zenodo.org/record/8136017) contains 21459 log records from 275 trainees. Compared to Version 3, we cleaned 303 invalid/duplicate command records.

description: The dataset contains two XRF images of iron and uranium distribution on plant roots and a database of XANES data used to produce XANES spectra figure for Figure 7 in the published paper. This dataset is associated with the following publication: Kaplan, D., R. Kukkadapu, J. Seaman, B. Arey, A. Dohnalkova, S. Buettner, D. Li, T. Varga, K. Scheckel, and P. Jaffe. Iron Mineralogy and Uranium-Binding Environment in the Rhizosphere of a Wetland Soil. D. Barcelo SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 569: 53-64, (2016).; abstract: The dataset contains two XRF images of iron and uranium distribution on plant roots and a database of XANES data used to produce XANES spectra figure for Figure 7 in the published paper. This dataset is associated with the following publication: Kaplan, D., R. Kukkadapu, J. Seaman, B. Arey, A. Dohnalkova, S. Buettner, D. Li, T. Varga, K. Scheckel, and P. Jaffe. Iron Mineralogy and Uranium-Binding Environment in the Rhizosphere of a Wetland Soil. D. Barcelo SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 569: 53-64, (2016).

Country scientific indicators developed from the information contained in the Scopus® database (Elsevier B.V.). These indicators can be used to assess and analyze scientific domains. Country rankings may be compared or analysed separately. Indicators offered for each country: H Index, Documents, Citations, Citation per Document and Citable Documents.

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.

description: Data corresponding to the figures in the paper. This dataset is associated with the following publication: Kaminski, M., C. Mertz, L. Ortega, and N. Kivenas. Sorption of Radionuclides to Building Materials and its Removal Using Simple Wash Solutions. Journal of Environmental Chemical Engineering. Elsevier B.V., Amsterdam, NETHERLANDS, ., (2016).; abstract: Data corresponding to the figures in the paper. This dataset is associated with the following publication: Kaminski, M., C. Mertz, L. Ortega, and N. Kivenas. Sorption of Radionuclides to Building Materials and its Removal Using Simple Wash Solutions. Journal of Environmental Chemical Engineering. Elsevier B.V., Amsterdam, NETHERLANDS, ., (2016).

Dataset of the research paper: Machine Learning for Software Engineering: A Tertiary Study

Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009–2022, covering 6,117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions including: conducting further empirical validation and industrial studies on ML; reconsidering deficient SE methods; documenting and automating data collection and pipeline processes; reexamining how industrial practitioners distribute their proprietary data; and implementing incremental ML approaches.

The following data and source files are included.

review-protocol.md: The protocol employed in this tertiary study

data/

dl-search/

input/

acm_comput_surveys_overviews.bib: Surveys of ACM Computing Surveys journal

acm_comput_surveys_overviews_titles.txt: Titles of surveys

acm_comput_ml_surveys.bib: Machine learning (ML)-related surveys of ACM Computing Surveys journal

acm_comput_ml_surveys_titles.txt: Titles of ML-related surveys

dl_search_queries.txt: Search queries applied to IEEE Xplore, ACM Digital Library, and Elsevier Scopus

ml_keywords.txt: ML-related keywords extracted from ML-related survey titles and used in the search queries

se_keywords.txt: Software Engineering (SE)-related keywords derived from the 15 SWEBOK Knowledge Areas (KAs—except for Computing Foundations, Mathematical Foundations, and Engineering Foundations) and used in the search queries

secondary_studies_keywords.txt: Survey-related keywords composed of the 15 keywords introduced in the tertiary study on SLRs in SE by Kitchenham et al. (2010), and the survey titles, and used in the search queries

output/

acm/

acm{1–9}.bib: Search results from ACM Digital Library

ieee.csv: Search results from IEEE Xplore

scopus_analyze_year.csv: Yearly distribution of ML and SE documents extracted from Scopus's Analyze search results page

scopus.csv: Search results from Scopus

study-selection/

backward_snowballing.csv: Additional secondary studies found through the backward snowballing process

backward_snowballing_references.csv: References of quality-accepted secondary studies

cohen_kappa_agreement.csv: Inter-rater reliability of reviewers in study selection

dl_search_results.csv: Aggregated search results of all three digital libraries

forward_snowballing_reviewer_{1,2}.csv: Divided forward snowballing citations of quality-accepted studies assessed by reviewer 1 and 2, correspondingly, based on IC/EC

study_selection_reviewer_{1,2}.csv: Divided search results assessed by reviewer 1 and 2, correspondingly, based on IC/EC

quality-assessment/

dare_assessment.csv: Quality assessment (QA) of selected secondary studies based on the Database of Abstracts of Reviews of Effects (DARE) criteria by York University, Centre for Reviews and Dissemination

quality_accepted_studies.csv: Details of quality-accepted studies

studies_for_review.bib: Bibliography details and QA scores of selected secondary studies

data-extraction/

further_research.csv: Recommendations for further research of quality-accepted studies

further_research_general.csv: The complete list of associated studies for each general recommendation

knowledge_areas.csv: Classification of quality-accepted studies using the SWEBOK KAs and subareas

ml_techniques.csv: Classification of the quality-accepted studies based on a four-axis ML classification scheme, along with extracted ML techniques employed in the studies

primary_studies.csv: Details of reviewed primary studies by the quality-accepted secondary

research_methods.csv: Citations of the research methods employed by the quality-accepted studies

research_types_methods.csv: Research types and methods employed by the quality-accepted studies

src/

data-analysis.ipynb: Analysis of data extraction results (data preprocessing, top authors and institutions, study types, yearly distribution of publishers, QA scores, and SWEBOK KAs) and creation of all figures included in the study

scopus-year-analysis.ipynb: Yearly distribution of ML and SE publications retrieved from Elsevier Scopus

study-selection-preprocessing.ipynb: Processing of digital library search results to conduct the inter-rater reliability estimation and study selection process

Dataset for research paper "Unmanned Aerial System (UAS) observations of water surface elevation in a small stream: comparison of radar altimetry, LIDAR and photogrammetry techniques", published in Remote Sensing of Environment 2019, Elsevier Journal.

Dataset includes:

-MATLAB_codes.rar: zip file that contains MATLAB codes. MAIN.m is the main code, it refers to external functions that are included in the zip file. MAIN.m plots the figures of the paper in which we compare radar, LIDAR and photogrammetry and computes statistics of table 3 (table of the paper)

-zip file WL_observations_Aomose.zip contains LIDAR, radar, and photogrammetry observations to be loaded by MATLAB code MAIN.m

-the LIDAR Digital Surface Model (DSM_final.tif) retrieved in the stream Amose Å

-the photogrammetry Digital Elevation Model (DEM_nov.tif) and orthomosaic (orthomosaic_nov.tif)

Plastic accounts for 80% of material waste in the ocean. The field of marine microplastic research is relatively new and is growing rapidly, in terms of published papers as well as institutions and countries conducting research. To combat plastic pollution, there is sufficient evidence that policies can lead to reduced plastic production and consumption both locally and globally. We aim to understand how marine plastics research and policies have grown and spread. Specifically, we used scientometric and spatial diffusion methods to best explain how ideas (in this case science and policy) clustered and spread geographically through time. We performed systematic literature searches to determine the spatial and temporal growth of marine microplastic publications and national plastic policies from 1900-2019. We found that more countries adopted national plastic policies than those that have conducted marine plastic research. Doubling times of each temporal growth rate analyzed (research paper, institution, country, and national policy) ranged from 1.1 – 4.05 years. Further, each temporal growth rate had a break point where doubling time changed significantly. Marine microplastic research has grown exponentially since 2006, and the topics of inquiry have increased steadily. Marine microplastic publication spread at the institution level is best explained by a hybrid of expansion and relocation diffusion while national plastic policy spread is best explained by expansion diffusion. Marine microplastic research activity was not a good indicator of a country’s resources or motivation toward national plastic policies.

Methods Marine plastic peer reviewed paper selection: Growth of marine microplastic (MP) publications was compared to other types of plastic research by performing a systematic literature search of peer-reviewed papers from Scopus, Elsevier’s abstract and citation database, in April 2020. The search used five sets of keywords: marine AND plastic*, marine AND “plastic bag*”, marine AND “single use plastic*,” marine AND microbead*, and marine AND microplastic*. The asterisk at the end of a word ensured both the singular and plural forms were considered. Within each of these sets of keywords the “analyze search results” feature was used in Scopus to record the quantity of papers published annually and cumulative number of papers published by country for 1900-2019. We note that many early papers studying mussel feeding physiology used poly-microbeads since the 1980s but were not included in any of the keyword searches. Papers were randomly spot-checked to ensure they fit within the keywords, if they did not, they were removed from our selection.

Metadata from marine MP papers were collected from a systematic literature search of peer-reviewed papers from Web of Science in April 2020. The search criteria used were the keywords marine AND microplastic* and all years (1900-2019), the same as the Scopus search. Publishing date, institution of lead author (including latitude and longitude), country of lead author, journal, and title were collected. Papers addressing non-marine MP topics (e.g. table salt or freshwater), highlights, commentary, news features, correspondences, opinion, and review papers were removed. Each marine MP paper was categorized based on focus topic: chemistry, environment, organism, policy, or review. If a paper studied multiple focus topics, only the predominate one was recorded. Organism papers were further categorized into functional groups: bacteria, fungus, invertebrate, small vertebrate, large vertebrate, macroalgae, phytoplankton, and zooplankton (includes fish larvae). If a paper studied multiple organisms, all were categorized by functional group and included.

National plastic policy selection: To evaluate plastic policy growth and diffusion, a systematic literature search for national plastic policies implemented through 2019 was conducted. Policy data was collected from Xanthos and Walker (2017), Schnurr et al. (2018), Lam et al. (2018), Plastic Policy Inventory from Duke’s Nicholas Institute for Environmental Policy Solutions (2020), and news articles from Wikipedia’s “phase-out of lightweight plastic bags” page (April 2020). Country, implementation year, type (plastic bag, microbead, single use plastic; SUP), and level (levy, ban) were recorded. All policies were cross-validated with an internet news search and policies that failed cross-validation were not included. Voluntary national plastic levies and bans were not included. Policies were evaluated at a national level, where countries with multiple levels or types of policies were only counted once in analyses.

ABSTRACT Background: Dystonia is a heterogeneous disorder that, when refractory to medical treatment, may have a favorable response to deep brain stimulation (DBS). A practical way to have an overview of a research domain is through a bibliometric analysis, as it makes it more accessible for researchers and others outside the field to have an idea of its directions and needs. Objective: To analyze the 100 most cited articles in the use of DBS for dystonia treatment in the last 30 years. Methods: The research protocol was performed in June 2019 in Elsevier’s Scopus database, by retrieving the most cited articles regarding DBS in dystonia. We analyzed authors, year of publication, country, affiliation, and targets of DBS. Results: Articles are mainly published in Movement Disorders (19%), Journal of Neurosurgery (9%), and Neurology (9%). European countries offer significant contributions (57% of our sample). France (192.5 citations/paper) and Germany (144.1 citations/paper) have the highest citation rates of all countries. The United States contributes with 31% of the articles, with 129.8 citations/paper. The publications are focused on General outcomes (46%), followed by Long-term outcomes (12.5%), and Complications (11%), and the leading type of dystonia researched is idiopathic or inherited, isolated, segmental or generalized dystonia, with 27% of articles and 204.3 citations/paper. Conclusions: DBS in dystonia research is mainly published in a handful of scientific journals and focused on the outcomes of the surgery in idiopathic or inherited, isolated, segmental or generalized dystonia, and with globus pallidus internus as the main DBS target.

Paleomagnetic, rock magnetic, or geomagnetic data found in the MagIC data repository from a paper titled: Pesonen, L.J. and Neuvonen, K.J. (1981). Palaeomagnetism of the Baltic Shield - implications for Precambrian tectonics. Precambrian Plate Tectonics (ed.Kroner,A., Elsevier) 623-648.

This data is associated with the following research paper:

Champoux et al 2020. An investigation of physiological effects of the Deepwater Horizon oil spill on a long-distance migratory seabird, the Northern Gannet. Marine Pollution Bulletin Volume 153, April 2020, 110953. https://doi.org/10.1016/j.marpolbul.2020.110953 https://authors.elsevier.com/a/1aaCz,ashxl6c