Objective: To determine the top 100-ranked (by impact factor) clinical journals' policies toward publishing research previously published on preprint servers (preprints).

Design: Cross sectional. Main outcome measures: Editorial guidelines toward preprints, journal rank by impact factor.

Results: 86 (86%) of the journals examined will consider papers previously published as preprints (preprints), 13 (13%) determine their decision on a case-by-case basis, and 1 (1%) does not allow preprints.

Conclusions: We found wide acceptance of publishing preprints in the clinical research community, although researchers may still face uncertainty that their preprints will be accepted by all of their target journals.

Methods We examined journal policies of the 100 top-ranked clinical journals using the 2018 impact factors as reported by InCites Journal Citation Reports (JCR). First, we examined all journals with an impact factor greater than 5, and then we manually screened by title and category do identify the first 100 clinical journals. We included only those that publish original research. Next, we checked each journal's editorial policy on preprints. We examined, in order, the journal website, the publisher website, the Transpose Database, and the first 10 pages of a Google search with the journal name and the term "preprint." We classified each journal's policy, as shown in this dataset, as allowing preprints, determining based on preprint status on a case-by-case basis, and not allowing any preprints. We collected data on April 23, 2020.

(Full methods can also be found in previously published paper.)

Answers to a survey on gold Open Access run from July to October 2016. The dataset contains 15,235 unique responses from Web of Science published authors. This survey is part of a PhD thesis from the University of Granada in Spain. More details about the study can be found in the full text document, also available in Zenodo.

Following are listed the questions related to the WoS 2016 dataset. Please note that countries with less than 40 answers are listed as "Other" in order to preserve anonymity.

* 1. How many years have you been employed in research?

• Fewer than 5 years
• 5-14 years
• 15-24 years
• 25 years or longer

Many of the questions that follow concern Open Access publishing. For the purposes of this survey, an article is Open Access if its final, peer-reviewed, version is published online by a journal and is free of charge to all users without restrictions on access or use.

* 2. Do any journals in your research field publish Open Access articles?

• Yes
• No
• I do not know

* 3. Do you think your research field benefits, or would benefit from journals that publish Open Access articles?

• Yes
• No
• I have no opinion
• I do not care

* 4. How many peer reviewed research articles (Open Access or not Open Access) have you published in the last five years?

• 1-5
• 6-10
• 11-20
• 21-50
• More than 50

* 5. What factors are important to you when selecting a journal to publish in?

[Each factor may be rated “Extremely important”, “Important”, “Less important” or “Irrelevant”. The factors are presented in random order.]

• Importance of the journal for academic promotion, tenure or assessment
• Recommendation of the journal by my colleagues
• Positive experience with publisher/editor(s) of the journal
• The journal is an Open Access journal
• Relevance of the journal for my community
• The journal fits the policy of my organisation
• Prestige/perceived quality of the journal
• Likelihood of article acceptance in the journal
• Absence of journal publication fees (e.g. submission charges, page charges, colour charges)
• Copyright policy of the journal
• Journal Impact Factor
• Speed of publication of the journal

6. Who usually decides which journals your articles are submitted to? (Choose more than one answer if applicable)

• The decision is my own
• A collective decision is made with my fellow authors
• I am advised where to publish by a senior colleague
• The organisation that finances my research advises me where to publish
• Other (please specify) [Text box follows]

7. Approximately how many Open Access articles have you published in the last five years?

• 0
• 1-5
• 6-10
• More than 10
• I do not know

[If the answer is “0”, the survey jumps to Q10.]

* 8. What publication fee was charged for the last Open Access article you published?

• No charge
• Up to €250 ($275)
• €251-€500 ($275-$550)
• €501-€1000 ($551-$1100)
• €1001-€3000 ($1101-$3300)
• More than €3000 ($3300)
• I do not know

[If the answer is “No charge or I don’t know” the survey jumps to Q20. ]

* 9. How was this publication fee covered? (Choose more than one answer if applicable)

• My research funding includes money for paying such fees
• I used part of my research funding not specifically intended for paying such fees
• My institution paid the fees
• I paid the costs myself
• Other (please specify) [Text box follows]

* 10. How easy is it to obtain funding if needed for Open Access publishing from your institution or the organisation mainly responsible for financing your research?

• Easy
• Difficult
• I have not used these sources

* 11. Listed below are a series of statements, both positive and negative, concerning Open Access publishing. Please indicate how strongly you agree/disagree with each statement.

[Each statement may be rated “Strongly agree”, “Agree”, “Neither agree nor disagree”, “Disagree” or “Strongly disagree”. The statements are presented in random order.]

• Researchers should retain the rights to their published work and allow it to be used by others
• Open Access publishing undermines the system of peer review
• Open Access publishing leads to an increase in the publication of poor quality research
• If authors pay publication fees to make their articles Open Access, there will be less money available for research
• It is not beneficial for the general public to have access to published scientific and medical articles
• Open Access unfairly penalises research-intensive institutions with large publication output by making them pay high costs for publication
• Publicly-funded research should be made available to be read and used without access barrier
• Open Access publishing is more cost-effective than subscription-based publishing and so will benefit public investment in research
• Articles that are available by Open Access are likely to be read and cited more often than those not Open Access

This study and its questionnaire are based on the SOAP Project (http://project-soap.eu). An article describing the highlights of the SOAP Survey is available at: https://arxiv.org/abs/1101.5260. The dataset of the SOAP survey is available at http://bit.ly/gSmm71. A manual describing the SOAP dataset is available at http://bit.ly/gI8nc.

The dataset contains all the data produced running the research software for the study:"Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta".

Disclaimer: these results are not considered to be representative, because we have fount that Mega Journals skewed significantly some of the data. The result datasets without Mega Journals are published here.

Description of datasets:

SSH_Publications_in_OC_Meta_and_Open_Access_status.csv: containing information about OpenCitations Meta coverage of ERIH PLUS Journals as well as their Open Access availability. In this dataset, every row holds data for a Journal of ERIH PLUS also covered by OpenCitations Meta database. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "OC_omid", the internal OpenCitations Meta identifier for the venue; "issn", numbers of publications in each venue; "Open Access", a value to represent if the journal is OA or not, either "True" or "Unknown".

SSH_Publications_by_Discipline.csv: containing information about number of publications per discipline (in addition, number of journals per discipline are also included). The dataset has three columns, the first, labeled "Discipline", contains single disciplines of the ERIH classificaton, the second and the third, labeled "Journal_count" and "Publication_count", respectively, the number of Journals and the number of Publications counted for each discipline.

SSH_Publications_and_Journals_by_Country: containing information about number of publications and journals per country. The dataset has three columns, the first, labeled "Country", contains single countries of the ERIH classificaton, the second and the third, labeled "Journal_count" and "Publication_count", respectively, the number of Journals and the number of Publications counted for each discipline.

result_disciplines.json: the dictionary containing all disciplines as key and a list of related ERIH PLUS venue identifiers as value.

result_countries.json: the dictionary containing all countries as key and a list of related ERIH PLUS venue identifiers as value.

duplicate_omids.csv: a dataset containing the duplicated Journal entries in OpenCitations Meta, structured with two columns: "OC_omid", the internal OC Meta identifier; "issn", the issn values associated to that identifier

eu_data.csv: contains the data specific for European countries' SSH Journals covered in OCMeta. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "Original_Title", "Country_of_Publication","ERIH_PLUS_Disciplines", "disc_count", the number of disciplines per Journal.

eu_disciplines_count.csv: containing information about number of publications per discipline and number of journals per discipline of european countries. The dataset has three columns, the first, labeled "Discipline", contains single disciplines of the ERIH classificaton, the second and the third, labeled "Journal_count" and "Publication_count", respectively, the number of Journals and the number of Publications counted for each discipline.

meta_coverage_eu.csv: contains the data specific for European countries' SSH Journals covered in OCMeta. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "OC_omid", the internal OpenCitations Meta identifier for the venue; "issn", numbers of publications in each venue; "Open Access", a value to represent if the journal is OA or not, either "True" or "Unknown".

us_data.csv: contains the data specific for the United States' SSH Journals covered in OCMeta. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "Original_Title", "Country_of_Publication","ERIH_PLUS_Disciplines", "disc_count", the number of disciplines per Journal.

us_disciplines_count.csv: containing information about number of publications per discipline and number of journals per discipline of the United States. The dataset has three columns, the first, labeled "Discipline", contains single disciplines of the ERIH classificaton, the second and the third, labeled "Journal_count" and "Publication_count", respectively, the number of Journals and the number of Publications counted for each discipline.

meta_coverage_us.csv: contains the data specific for the United States' SSH Journals covered in OCMeta. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "OC_omid", the internal OpenCitations Meta identifier for the venue; "issn", numbers of publications in each venue; "Open Access", a value to represent if the journal is OA or not, either "True" or "Unknown".

Abstract of the research:

Purpose: this study aims to investigate the representation and distribution of Social Science and Humanities (SSH) journals within the OpenCitations Meta database, with a particular emphasis on their Open Access (OA) status, as well as their spread across different disciplines and countries. The underlying premise is that open infrastructures play a pivotal role in promoting transparency, reproducibility, and trust in scientific research. Study Design and Methodology: the study is grounded on the premise that open infrastructures are crucial for ensuring transparency, reproducibility, and fostering trust in scientific research. The research methodology involved the use of secondary data sources, namely the OpenCitations Meta database, the ERIH PLUS bibliographic index, and the DOAJ index. A custom research software was developed in Python to facilitate the processing and analysis of the data. Findings: the results reveal that 78.1% of SSH journals listed in the European Reference Index for the Humanities (ERIH-PLUS) are included in the OpenCitations Meta database. The discipline of Psychology has the highest number of publications. The United States and the United Kingdom are the leading contributors in terms of the number of publications. However, the study also uncovers that only 38% of the SSH journals in the OpenCitations Meta database are OA. Originality: this research adds to the existing body of knowledge by providing insights into the representation of SSH in open bibliographic databases and the role of open access in this domain. The study highlights the necessity for advocating OA practices within SSH and the significance of open data for bibliometric studies. It further encourages additional research into the impact of OA on various facets of citation patterns and the factors leading to disparity across disciplinary representation.

Related resources:

Ghasempouri S., Ghiotto M., & Giacomini S. (2023). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - RESEARCH ARTICLE. https://doi.org/10.5281/zenodo.8263908

Ghasempouri, S., Ghiotto, M., Giacomini, S., (2023). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - DATA MANAGEMENT PLAN (Version 4). Zenodo. https://doi.org/10.5281/zenodo.8174644

Ghasempouri, S., Ghiotto, M., Giacomini, S. (2023e). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - PROTOCOL. V.5. (https://dx.doi.org/10.17504/protocols.io.5jyl8jo1rg2w/v5)

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to

establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data

Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered.
Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review:

Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection.
Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation.

See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

Hybrid LCA database generated using ecoinvent and EXIOBASE, i.e., each process of the original ecoinvent database is added new direct inputs (coming from EXIOBASE) deemed missing (e.g., services). Each process of the resulting hybrid database is thus not (or at least less) truncated and the calculated lifecycle emissions/impacts should therefore be closer to reality.

For license reasons, only the added inputs for each process of ecoinvent are provided (and not all the inputs).

Why are there two versions for hybrid-ecoinvent3.5?

One of the version corresponds to ecoinvent hybridized with the normal version of EXIOBASE and the other is hybridized with a capital-endogenized version of EXIOBASE.

What does capital endogenization do?

It matches capital goods formation to the value chains of products where they are required. In a more LCA way of speaking, EXIOBASE in its normal version does not allocate capital use to value chains. It's like if ecoinvent processes had no inputs of buildings, etc. in their unit process inventory. For more detail on this, refer to (Södersten et al., 2019) or (Miller et al., 2019).

So which version do I use?

Using the version "with capitals" gives a more comprehensive coverage. Using the "without capitals" version means that if a process of ecoinvent misses inputs of capital goods (e.g., a process does not include the company laptops of the employees), it won't be added. It comes with its fair share of assumptions and uncertainties however.

Why is it only available for hybrid-ecoinvent3.5?

The work used for capital endogenization is not available for exiobase3.8.1.

How do I use the dataset?

First, to use it, you will need both the corresponding ecoinvent [cut-off] and EXIOBASE [product x product] versions. For the reference year of EXIOBASE to-be-used, take 2011 if using the hybrid-ecoinvent3.5 and 2019 for hybrid-ecoinvent3.6 and 3.7.1.

In the four datasets of this package, only added inputs are given (i.e. inputs from EXIOBASE added to ecoinvent processes). Ecoinvent and EXIOBASE processes/sectors are not included, for copyright issues. You thus need both ecoinvent and EXIOBASE to calculate life cycle emissions/impacts.

Module to get ecoinvent in a Python format: https://github.com/majeau-bettez/ecospold2matrix (make sure to take the most up-to-date branch)

Module to get EXIOBASE in a Python format: https://github.com/konstantinstadler/pymrio (can also be installed with pip)

If you want to use the "with capitals" version of the hybrid database, you also need to use the capital endogenized version of EXIOBASE, available here: https://zenodo.org/record/3874309. Choose the pxp version of the year you plan to study (which should match with the year of the EXIOBASE version). You then need to normalize the capital matrix (i.e., divide by the total output x of EXIOBASE). Then, you simply add the normalized capital matrix (K) to the technology matrix (A) of EXIOBASE (see equation below).

Once you have all the data needed, you just need to apply a slightly modified version of the Leontief equation:

(\begin{equation} \textbf{q}^{hyb} = \begin{bmatrix} \textbf{C}^{lca}\cdot\textbf{S}^{lca} & \textbf{C}^{io}\cdot\textbf{S}^{io} \end{bmatrix} \cdot \left( \textbf{I} - \begin{bmatrix} \textbf{A}^{lca} & \textbf{C}^{d} \ \textbf{C}^{u} & \textbf{A}^{io}+\textbf{K}^{io} \end{bmatrix} \right) ^{-1} \cdot \left( \begin{bmatrix} \textbf{y}^{lca} \ 0 \end{bmatrix} \right) \end{equation})

qhyb gives the hybridized impact, i.e., the impacts of each process including the impacts generated by their new inputs.

Clca and Cio are the respective characterization matrices for ecoinvent and EXIOBASE.

Slca and Sio are the respective environmental extension matrices (or elementary flows in LCA terms) for ecoinvent and EXIOBASE.

I is the identity matrix.

Alca and Aio are the respective technology matrices for ecoinvent and EXIOBASE (the ones loaded with ecospold2matrix and pymrio).

Kio is the capital matrix. If you do not use the endogenized version, do not include this matrix in the calculation.

Cu (or upstream cut-offs) is the matrix that you get in this dataset.

Cd (or downstream cut-offs) is simply a matrix of zeros in the case of this application.

Finally you define your final demand (or functional unit/set of functional units for LCA) as ylca.

Can I use it with different versions/reference years of EXIOBASE?

Technically speaking, yes it will work, because the temporal aspect does not intervene in the determination of the hybrid database presented here. However, keep in mind that there might be some inconsistencies. For example, you would need to multiply each of the inputs of the datasets by a factor to account for inflation. Prices of ecoinvent (which were used to compile the hybrid databases, for all versions presented here) are defined in €2005.

What are the weird suite of numbers in the columns?

Ecoinvent processes are identified through unique identifiers (uuids) to which metadata (i.e., name, location, price, etc.) can be retraced with the appropriate metadata files in each dataset package.

Why is the equation (I-A)-1 and not A-1 like in LCA?

IO and LCA have the same computational background. In LCA however, the convention is to represents outputs and inputs in the technology matrix. That's why there is a diagonal of 1s (the outputs, i.e. functional units) and negative values elsewhere (inputs). In IO, the technology matrix does not include outputs and only registers inputs as positive values. In the end, it is just a convention difference. If we call T the technology matrix of LCA and A the technology matrix of IO we have T = I-A. When you load ecoinvent using ecospold2matrix, the resulting version of ecoinvent will already be in IO convention and you won't have to bother with it.

Pymrio does not provide a characterization matrix for EXIOBASE, what do I do?

You can find an up-to-date characterization matrix (with Impact World+) for environmental extensions of EXIOBASE here: https://zenodo.org/record/3890339

If you want to match characterization across both EXIOBASE and ecoinvent (which you should do), here you can find a characterization matrix with Impact World+ for ecoinvent: https://zenodo.org/record/3890367

It's too complicated...

The custom software that was used to develop these datasets already deals with some of the steps described. Go check it out: https://github.com/MaximeAgez/pylcaio. You can also generate your own hybrid version of ecoinvent using this software (you can play with some parameters like correction for double counting, inflation rate, change price data to be used, etc.). As of pylcaio v2.1, the resulting hybrid database (generated directly by pylcaio) can be exported to and manipulated in brightway2.

Where can I get more information?

The whole methodology is detailed in (Agez et al., 2021).

Dataset name: asppl_dataset_v2.csv

Version: 2.0

Dataset period: 06/07/2018 - 01/14/2022

Dataset Characteristics: Multivalued

Number of Instances: 8118

Number of Attributes: 9

Missing Values: Yes

Area(s): Health and education

Sources:

• Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a);

• Brazilian Occupational Classification (CBO) (Brasil, 2022b);

• National Registry of Health Establishments (CNES) (Brasil, 2022c);

• Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e).

Description: The data contained in the asppl_dataset_v2.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health Care for People Deprived of Freedom.” The course is available on the AVASUS (Brasil, 2022a). This dataset provides elementary data for analyzing the course’s impact and reach and the profile of its participants. In addition, it brings an update of the data presented in work by Valentim et al. (2021).

Table 1: Description of AVASUS dataset features.

Attributes	Description	datatype	Value
gender	Gender of the course participant.	Categorical.	Feminino / Masculino / Não Informado. (In English, Female, Male or Uninformed)
course_progress	Percentage of completion of the course.	Numerical.	Range from 0 to 100.
course_evaluation	A score given to the course by the participant.	Numerical.	0, 1, 2, 3, 4, 5 or NaN.
evaluation_commentary	Comment made by the participant about the course.	Categorical.	Free text or NaN.
region	Brazilian region in which the participant resides.	Categorical.	Brazilian region according to IBGE: Norte, Nordeste, Centro-Oeste, Sudeste or Sul (In English North, Northeast, Midwest, Southeast or South).
CNES	The CNES code refers to the health establishment where the participant works.	Numerical.	CNES Code or NaN.
health_care_level	Identification of the health care network level for which the course participant works.	Categorical.	“ATENCAO PRIMARIA”, “MEDIA COMPLEXIDADE”, “ALTA COMPLEXIDADE”, and their possible combinations. (In English "PRIMARY HEALTH CARE", "SECONDARY HEALTH CARE" AND "TERTIARY HEALTH CARE")
year_enrollment	Year in which the course participant registered.	Numerical.	Year (YYYY).
CBO	Participant occupation.	Categorical.	Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem afiliação formal.” (In English “Individual without formal affiliation.”)

Dataset name: prison_syphilis_and_population_brazil.csv

Dataset period: 2017 - 2020

Dataset Characteristics: Multivalued

Number of Instances: 6

Number of Attributes: 13

Missing Values: No

Source:

• National Penitentiary Department (DEPEN) (Brasil, 2022d);

Description: The data contained in the prison_syphilis_and_population_brazil.csv dataset (see Table 2) originate from the National Penitentiary Department Information System (SISDEPEN) (Brasil, 2022d). This dataset provides data on the population and prevalence of syphilis in the Brazilian prison system. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil.

Table 2: Description of DEPEN dataset Features.

Attributes	Description	datatype	Value
Region	Brazilian region in which the participant resides. In addition, the sum of the regions, which refers to Brazil.	Categorical.	Brazil and Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South.
syphilis_2017	Number of syphilis cases in the prison system in 2017.	Numerical.	Number of syphilis cases.
syphilis_rate_2017	Normalized rate of syphilis cases in 2017.	Numerical.	Syphilis case rate.
syphilis_2018	Number of syphilis cases in the prison system in 2018.	Numerical.	Number of syphilis cases.
syphilis_rate_2018	Normalized rate of syphilis cases in 2018.	Numerical.	Syphilis case rate.
syphilis_2019	Number of syphilis cases in the prison system in 2019.	Numerical.	Number of syphilis cases.
syphilis_rate_2019	Normalized rate of syphilis cases in 2019.	Numerical.	Syphilis case rate.
syphilis_2020	Number of syphilis cases in the prison system in 2020.	Numerical.	Number of syphilis cases.
syphilis_rate_2020	Normalized rate of syphilis cases in 2020.	Numerical.	Syphilis case rate.
pop_2017	Prison population in 2017.	Numerical.	Population number.
pop_2018	Prison population in 2018.	Numerical.	Population number.
pop_2019	Prison population in 2019.	Numerical.	Population number.
pop_2020	Prison population in 2020.	Numerical.	Population number.

Dataset name: students_cumulative_sum.csv

Dataset period: 2018 - 2020

Dataset Characteristics: Multivalued

Number of Instances: 6

Number of Attributes: 7

Missing Values: No

Source:

• Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a);

• Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e).

Description: The data contained in the students_cumulative_sum.csv dataset (see Table 3) originate mainly from AVASUS (Brasil, 2022a). This dataset provides data on the number of students by region and year. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil. We used population data estimated by the IBGE (Brasil, 2022e) to calculate the rate.

Table 3: Description of Students dataset Features.

Research dissemination and knowledge translation are imperative in social work. Methodological developments in data visualization techniques have improved the ability to convey meaning and reduce erroneous conclusions. The purpose of this project is to examine: (1) How are empirical results presented visually in social work research?; (2) To what extent do top social work journals vary in the publication of data visualization techniques?; (3) What is the predominant type of analysis presented in tables and graphs?; (4) How can current data visualization methods be improved to increase understanding of social work research? Method: A database was built from a systematic literature review of the four most recent issues of Social Work Research and 6 other highly ranked journals in social work based on the 2009 5-year impact factor (Thomson Reuters ISI Web of Knowledge). Overall, 294 articles were reviewed. Articles without any form of data visualization were not included in the final database. The number of articles reviewed by journal includes : Child Abuse & Neglect (38), Child Maltreatment (30), American Journal of Community Psychology (31), Family Relations (36), Social Work (29), Children and Youth Services Review (112), and Social Work Research (18). Articles with any type of data visualization (table, graph, other) were included in the database and coded sequentially by two reviewers based on the type of visualization method and type of analyses presented (descriptive, bivariate, measurement, estimate, predicted value, other). Additional revi ew was required from the entire research team for 68 articles. Codes were discussed until 100% agreement was reached. The final database includes 824 data visualization entries.

This dataset was created in the context of the project: " Current trends in scientific research on global warming: A bibliometric analysis (2005-2014)".

Global warming is a topic of increasing public importance, but there have not been published scientometric studies on this topic. The objective of this paper is to contribute to a better understanding of the scientific knowledge in global warming and his effect, as well as to investigate its evolution through the published papers included in Web of Science database. Items under study were collected from Web of Science database from Thomson Reuters. A bibliometric and social network analyses was performed to obtain indicators of scientific productivity, impact and collaboration between researchers, institutions and countries. A subject analysis was also carried out taking into account the key words assigned to papers and subject areas of journals. 1,672 articles were analysed since 2005 until 2014. The most productive journals were Journal of Climate (n=95) and Geophysical Resarch Letters (n=78). The most frequent keywords have been Climate Change (n=722), Model (n=216) and Temperature (n=196). The network of collaboration between countries shows the central position of the United States, together with other leading countries such as United Kingdom, Germany, France and Peoples Republic of China. The research on global warming had grown steadily during the last decade. A vast amount of journals from several subject areas publishes the papers on the topic, including journals of general purpose with high impact factor. Almost all the countries have USA as the main country with which one collaborates. The analysis of key words shows that topics related with climate change, impact, temperature, models and variability are the most important concerns on global warming.

The dataset consist of the following:

1) The list of papers included in the analyses: Papers.xlsx

This file contains 1672 titles, each line representing a paper (including title of the paper, journal ISSN and year of publication).

2) The list of authors: Authors.xlsx

This file contains all 4488 authors, each line representing an author (including full name, total number of papers and year of publication).

3) The list of scientific journals: Journals.xlsx

This file containts all 687 journals, each line representing a journal (including name of the journal, ISSN, total number of papers and year of publication).

4) The list of countries: Country.xlsx

This file contains all 84 countries, each line representing a country (including country name, total number of papers, total number of citations, and number of citations per paper).

5) The list of keywords: Keywords.xlsx

This file contains all 6422 keywords, each line representing a keyword (including keywords, number of papers and year of publication)

5-YIF=5-year impact factor, N=number of scientific articles published, ISSN=International Serial Standard Number,aJournals not indexed in the Web of Knowledge databases.*Without 5-year impact factor, values correspond to impact factor of 2010.

Acupuncture publications in PubMed for 5-year intervals between 1995 and 2014.

Few paired lake-watershed studies examine long term effects of climate on the ecosystem function of lakes in a hydrological context. We use thirty-two years of hydrological and biogeochemical data from a high-elevation site in the Sierra Nevada of California to characterize variation in snowmelt in relation to climate variability, and explore the impact on factors affecting phytoplankton biomass. The magnitude of accumulated winter snow, quantified through basin-wide estimates of snow water equivalent (SWE), was the most important climate factor controlling variation in the timing and rate of spring snowmelt. Variations in SWE and snowmelt led to significant differences in lake flushing rate, water temperature, and nitrate concentrations across years. On average in dry years, snowmelt started 25 days earlier and proceeded 7 mm/d slower, and the lake began the ice-free season with nitrate concentrations ~2 uM higher and water temperatures 9 C warmer than in wet years. Flushing rates in wet years were 2.5 times larger than dry years. Consequently, particulate organic matter concentrations, a proxy for phytoplankton biomass, were 5 – 6 uM higher in dry years. There was a temporal trend of increase in particulate organic matter across dry years that corresponded to lake warming independent of variation in SWE. These results suggest that phytoplankton biomass is increasing as a result of both interannual variability in precipitation and long term warming trends. Our study underscores the need to account for local-scale catchment variability that may affect the accumulation of winter snowpack when predicting climate responses in lakes.

Update — December 7, 2014. – Evidence-based medicine (EBM) is not working for many reasons, for example: 1. Incorrect in their foundations (paradox): hierarchical levels of evidence are supported by opinions (i.e., lowest strength of evidence according to EBM) instead of real data collected from different types of study designs (i.e., evidence). http://dx.doi.org/10.6084/m9.figshare.1122534 2. The effect of criminal practices by pharmaceutical companies is only possible because of the complicity of others: healthcare systems, professional associations, governmental and academic institutions. Pharmaceutical companies also corrupt at the personal level, politicians and political parties are on their payroll, medical professionals seduced by different types of gifts in exchange of prescriptions (i.e., bribery) which very likely results in patients not receiving the proper treatment for their disease, many times there is no such thing: healthy persons not needing pharmacological treatments of any kind are constantly misdiagnosed and treated with unnecessary drugs. Some medical professionals are converted in K.O.L. which is only a puppet appearing on stage to spread lies to their peers, a person supposedly trained to improve the well-being of others, now deceits on behalf of pharmaceutical companies. Probably the saddest thing is that many honest doctors are being misled by these lies created by the rules of pharmaceutical marketing instead of scientific, medical, and ethical principles. Interpretation of EBM in this context was not anticipated by their creators. “The main reason we take so many drugs is that drug companies don’t sell drugs, they sell lies about drugs.” ―Peter C. Gøtzsche “doctors and their organisations should recognise that it is unethical to receive money that has been earned in part through crimes that have harmed those people whose interests doctors are expected to take care of. Many crimes would be impossible to carry out if doctors weren’t willing to participate in them.” —Peter C Gøtzsche, The BMJ, 2012, Big pharma often commits corporate crime, and this must be stopped. Pending (Colombia): Health Promoter Entities (In Spanish: EPS ―Empresas Promotoras de Salud).

1. Misinterpretations New technologies or concepts are difficult to understand in the beginning, it doesn’t matter their simplicity, we need to get used to new tools aimed to improve our professional practice. Probably the best explanation is here in these videos (credits to Antonio Villafaina for sharing these videos with me). English https://www.youtube.com/watch?v=pQHX-SjgQvQ&w=420&h=315 Spanish https://www.youtube.com/watch?v=DApozQBrlhU&w=420&h=315 ----------------------- Hypothesis: hierarchical levels of evidence based medicine are wrong Dear Editor, I have data to support the hypothesis described in the title of this letter. Before rejecting the null hypothesis I would like to ask the following open question:Could you support with data that hierarchical levels of evidence based medicine are correct? (1,2) Additional explanation to this question: – Only respond to this question attaching publicly available raw data.– Be aware that more than a question this is a challenge: I have data (i.e., evidence) which is contrary to classic (i.e., McMaster) or current (i.e., Oxford) hierarchical levels of evidence based medicine. An important part of this data (but not all) is publicly available. References
2. Ramirez, Jorge H (2014): The EBM challenge. figshare. http://dx.doi.org/10.6084/m9.figshare.1135873
3. The EBM Challenge Day 1: No Answers. Competing interests: I endorse the principles of open data in human biomedical research Read this letter on The BMJ – August 13, 2014.http://www.bmj.com/content/348/bmj.g3725/rr/762595Re: Greenhalgh T, et al. Evidence based medicine: a movement in crisis? BMJ 2014; 348: g3725. _ Fileset contents Raw data: Excel archive: Raw data, interactive figures, and PubMed search terms. Google Spreadsheet is also available (URL below the article description). Figure 1. Unadjusted (Fig 1A) and adjusted (Fig 1B) PubMed publication trends (01/01/1992 to 30/06/2014). Figure 2. Adjusted PubMed publication trends (07/01/2008 to 29/06/2014) Figure 3. Google search trends: Jan 2004 to Jun 2014 / 1-week periods. Figure 4. PubMed publication trends (1962-2013) systematic reviews and meta-analysis, clinical trials, and observational studies.
   Figure 5. Ramirez, Jorge H (2014): Infographics: Unpublished US phase 3 clinical trials (2002-2014) completed before Jan 2011 = 50.8%. figshare.http://dx.doi.org/10.6084/m9.figshare.1121675 Raw data: "13377 studies found for: Completed | Interventional Studies | Phase 3 | received from 01/01/2002 to 01/01/2014 | Worldwide". This database complies with the terms and conditions of ClinicalTrials.gov: http://clinicaltrials.gov/ct2/about-site/terms-conditions Supplementary Figures (S1-S6). PubMed publication delay in the indexation processes does not explain the descending trends in the scientific output of evidence-based medicine. Acknowledgments I would like to acknowledge the following persons for providing valuable concepts in data visualization and infographics:
4. Maria Fernanda Ramírez. Professor of graphic design. Universidad del Valle. Cali, Colombia.
5. Lorena Franco. Graphic design student. Universidad del Valle. Cali, Colombia. Related articles by this author (Jorge H. Ramírez)
6. Ramirez JH. Lack of transparency in clinical trials: a call for action. Colomb Med (Cali) 2013;44(4):243-6. URL: http://www.ncbi.nlm.nih.gov/pubmed/24892242
7. Ramirez JH. Re: Evidence based medicine is broken (17 June 2014). http://www.bmj.com/node/759181
8. Ramirez JH. Re: Global rules for global health: why we need an independent, impartial WHO (19 June 2014). http://www.bmj.com/node/759151
9. Ramirez JH. PubMed publication trends (1992 to 2014): evidence based medicine and clinical practice guidelines (04 July 2014). http://www.bmj.com/content/348/bmj.g3725/rr/759895 Recommended articles
10. Greenhalgh Trisha, Howick Jeremy,Maskrey Neal. Evidence based medicine: a movement in crisis? BMJ 2014;348:g3725
11. Spence Des. Evidence based medicine is broken BMJ 2014; 348:g22
12. Schünemann Holger J, Oxman Andrew D,Brozek Jan, Glasziou Paul, JaeschkeRoman, Vist Gunn E et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies BMJ 2008; 336:1106
13. Lau Joseph, Ioannidis John P A, TerrinNorma, Schmid Christopher H, OlkinIngram. The case of the misleading funnel plot BMJ 2006; 333:597
14. Moynihan R, Henry D, Moons KGM (2014) Using Evidence to Combat Overdiagnosis and Overtreatment: Evaluating Treatments, Tests, and Disease Definitions in the Time of Too Much. PLoS Med 11(7): e1001655. doi:10.1371/journal.pmed.1001655
15. Katz D. A-holistic view of evidence based medicinehttp://thehealthcareblog.com/blog/2014/05/02/a-holistic-view-of-evidence-based-medicine/ ---

The Regional Snowfall Index (RSI) is an index of significant snowstorms that impact the eastern two thirds of the U.S. The RSI ranks snowstorm impacts on a scale from 1 to 5, similar to the Fujita scale for tornadoes or the Saffir-Simpson scale for hurricanes. NCEI has analyzed and assigned RSI values to over 500 storms going as far back as 1900. New storms are added operationally. As such, RSI puts the regional impacts of snowstorms into a century-scale historical perspective. The RSI differs from other indices because it includes population. RSI is based on the spatial extent of the storm, the amount of snowfall, and the juxtaposition of these elements with population. The area and population are cumulative values above regional specific thresholds. For example, the thresholds for the Southeast are 2", 5", 10", and 15" of snowfall while the thresholds for the Northeast are 4", 10", 20", and 30" of snowfall. Population information ties the index to societal impacts. Currently, the index uses population based on the 2000 Census. The RSI is an evolution of the Northeast Snowfall Impact Scale (NESIS) which NCDC (the precursor to NCEI) began producing operationally in 2005. While NESIS was developed for storms that had a major impact in the Northeast, it includes the impact of snow on other regions as well. It can be thought of as a quasi-national index that is calibrated to Northeast snowstorms. By contrast, the RSI is a regional index; a separate index is produced for each of the six NCDC climate regions in the eastern two-thirds of the nation. The indices are calculated in a similar fashion to NESIS, but our experience has led us to propose a change in the methodology. The new indices require region-specific parameters and thresholds for the calculations. For details on how RSI is calculated, see Squires et al. 2011.

Top ten journals most commonly published in over the period 2007–2016 and 5-year impact factor.

We have acquired a new tobacco-weed dataset using a Mavic Mini drone. Eight fields of tobacco crop are captured in Mardan, Khyber Pakhtunkhwa, Pakistan. At different growth stages these eight fields are captured at crop age of 15 to 40 days approximately. Data is captured at 1920×1080-pixel resolution. Dataset is captured at an average altitude of 4 meters with ground sampling distance of 0.1 cm/pixel.

Find Details in attached Research papers

Citation Request: if you use these datasets in your research or projects by any means, please cite following publications.

1) Patch-wise weeds coarse segmentation mask from aerial imagery of sesame crop (Published in Computers and Electronics in Agriculture 2022, HEC Recognized W category, Impact factor 6.757, Q1) 2) Towards automated weed detection through two-stage semantic segmentation of tobacco and weed pixels in aerial Imagery (Published in Smart Agricultural Technology (A companion journal of Computers and Electronics in Agriculture)) 3) A Patch-Image Based Classification Approach for Detection of Weeds in Sugar Beet Crop (Published in IEEE Access, Impact factor 3.1, Q1)

Acknowledgement Request This work is funded by the Higher Education Commission of Pakistan and the National center for Robotics and Automation (DF-1009–31). Please Acknowledge.

Steps to Access Mendeley datasets 1. Click on the link 2. The link with ask you to sign in or register with institutional email. 3. Use your institutional/organization email to register and then sign in. 4. Once sign in, dataset will be visible in compressed folders 5. Download and unzip/umcompress folder 6. Use dataset in your research as you see fit (folders contains original images, and their labeled groundtruths, along with binary vegetation masks. In groundtruths background have label value of 0, crop have label 1 and weeds have label of 2. maskref subfolders shows labelled data for visualization)

Find More datasets and published articles in Related Links

The empirical scaling factors can be used to guide the plausible range of changes in the climate variables within impact-adaption-vulnerability assessment under climate change. The gridded dataset of all the scaling factors are produced at the spatial resolution of 0.5o×0.5o with the name ScalingFactor_0.5. In the dataset, there are 6 subfolders named as prcp, pexm, pet, tas, tasmax and tasmin representing scaling factors of precipitation, extreme high daily rainfall, potential evapotranspiration, mean temperature, maximum temperature and minimum temperature. In the subfolder, each text file represents the scaling factors of each GCM model and each run.

The data material was collected in a controlled experiment that investigated the ability of laypeople to visually assess blood loss and to examine factors that may impact accuracy and the classification of injury severity. A total of 125 laypeople watched 78 short videos each of individuals experiencing a hemorrhage. Victim gender, volume of blood lost, and camera perspective were systematically manipulated in the videos. The data set consists of four variables: volume estimate, volume error, response time, and classification. Each variable has a separate sheet in the excel document. The data is from 125 individuals, each listed on a separate row with a unique ID for each individual. Each sheet includes the participant ID (anonymous number), age in years, participant sex (0 = male, 1 = female), perspective of the video clip (0 = top view, 1 = front view), and then one column for each victim gender and loss volume combination (24 total). The column label consists of M or F for male and female victim, followed by underscore and the loss volume (e.g., M_50 for male victim with 50 ml of blood loss, or F_1100 for a female victim with 1100 ml of blood loss). Volume estimates are the participants' estimate of blood loss in ml. Volume error is the estimate minus the true value, in ml. Response time is the time it took for participants to classify the bleeding as life-threatening or not, in seconds. Classification is a value from 0 to 1 for the proportion of times the participant classified that particular gender-volume combination as depicting a life-threatening blood loss . Detta dataset samlades in för en studie som undersökte lekmäns förmåga att göra bedömningar av storlek och allvarlighet av blodförlust. Data samlades in från 125 personer som bedömde blodförlusten på patienter i 78 filmer. Patientens kön, blodförlustvolymen, och kameraperspektivet manipulerade systematiskt. Detta dataset har fyra variabler: volume estimate, volume error, response time, och classification. Varje variabel återfinns på varsin flik i excel-arket. Det är 125 individer listade, en för varje rad och med ett unikt, anonymt ID. Tre individer saknar data för de variabler som ingår i detta dataset. Varje flik har också en kolumn för deltagarens kön (0 = man, 1 = kvinna), ålder i år, och perspektivet på de videofilmer deltagaren såg på (0 = vy uppifrån, 1 = vy framifrån). För varje variabel finns därefter 24 kolumner med data för alla kombinationer av den skadade patientens kön (man eller kvinna) och de 12 blodförlustvolymer som användes (från 0 till 1900 ml). Varje kolumn har ett namn som beskriver kombinationen enligt följande struktur: M eller F för patientens kön (male och female, respektive), understreck, därefter en siffra som visar blodförlust i ml. T.ex. är M_50 en manlig patient med 50 ml blodförlust och F_1100 en kvinnlig patient med 1100 ml blodförlust. Variabeln volume estimate är deltagarens uppskattning av blodförlust i ml. Variabeln volume error är uppskattningen minus den sanna volymen, i ml. Variablen response time är tiden det tog för deltagaren att klassa blödningen som antingen livshotande eller icke-livshotande, i sekunder. Variabeln classification är ett värde från 0 till 1 som visar hur frekvent deltagaren klassade just den kön-volym kombinationen som en livshotande blödning (av tre gånger). Participants took part in a controlled experiment in which they viewed a series of 78 five-second film clips featuring a person with a simulated bleeding. They were asked to, as quickly as possible, classify the video as showing a life-threatening or not life-threatening bleeding using a keyboard response. After each video, the participant was asked to estimate how large the bleeding was, classify the severity of the injury, and, if they classified the video as showing a life-threatening bleeding, to estimate how minutes it would take for the victim to die from their bleeding. The participants also completed a basic demographics questionnaire at the end of the experiment. The entire experiment took between 40 to 60 minutes to complete. The participants were students at a large south-eastern university in the USA. Participants with prior medical training or stop the bleed education were excluded. Thus, all participants were medical laypeople without prior experience. The variables varied in the videos were victim sex (male or female), blood volume (ml of blood on the floor), and rate of blood flow (in ml per minute). Further, two video sets were created, one with a top-view (camera placed above the victim) or front view (camera placed facing the victim from the front). In each video, the victim was dressed in blue, hydrophobic scrubs and were seated against a white wall. The simulated wound was not visible. The actors were positioned such that the blood flowed down their thigh and pooled between their legs. The same male and female patient actor were used for all videos. The flow rates used were 80, 200, and 400 ml/minute. The blood volumes used were 0, 50, 100, 150, 200, 300, 400, 500, 700, 900, 1100, and 1900 ml. The combination of three flow rates, 13 blood volumes, and two genders meant that there was 78 videos in total. For the current dataset, the data has been collapsed across flow rate, and the volume 0 has been excluded, meaning that there are 24 combinations (2 genders x 12 volumes). The dataset includes the response time for the initial classification, the classification response, the volume estimate, and the volume error (calculated as the difference between the true amount and the estimated amount of blood loss). Deltagarna deltog i ett kontrollerat experiment där de tittade på en serie med 78 stycken filmklipp, 5 sekunder långa, som visade en person med en simulerad blödning. De ombads att så snabbt som möjligt klassificera videon som en livshotande eller inte livshotande blödning genom att trycka på en tangent på tangentbordet. Efter varje filmklipp ombads deltagaren att uppskatta hur stor blödningen var, klassificera allvarligheten av skadan samt, om de klassificerade videon som en livshotande blödning, att uppskatta hur många minuter det skulle ta för offret att dö från blödningen. Deltagarna fyllde också i ett demografiskt frågeformulär i slutet av experimentet. Hela experimentet tog mellan 40 och 60 minuter att genomföra. Deltagarna var studenter vid ett stort universitet i sydöstra USA. Deltagare med tidigare medicinsk utbildning eller stop the bleed-utbildning uteslöts. Således var alla deltagare medicinska noviser utan tidigare erfarenhet. Variablerna som varierades i filmerna var patientens kön (man eller kvinna), blodvolym (ml blod på golvet) och flödeshastigheten på blödningen (i ml per minut). Vidare skapades två videouppsättningar, en med en vy uppifrån (kamera placerad ovanför patienten) eller framifrån (kamera placerad mot patienten framifrån). I varje video var patienten klädd i blå, hydrofoba scrubs och satt mot en vit vägg. Det simulerade såret var inte synligt. Skådespelarna var placerade så att blodet flödade nedåt låret och bildade en pöl mellan benen. Samma manliga och kvinnliga skådespelare användes för alla videor. De använda flödeshastigheterna var 80, 200 och 400 ml / minut. De använda blodvolymerna var 0, 50, 100, 150, 200, 300, 400, 500, 700, 900, 1100 och 1900 ml. Kombinationen av tre flöden, 13 blodvolymer och två kön innebar att det var 78 videoklipp totalt. För detta dataset har data kollapsats över flödeshastigheten och volymen 0 har uteslutits, vilket innebär att det finns 24 kombinationer (2 kön x 12 volymer). Data inkluderar responstiden för den initiala klassificeringen, klassificeringssvaret, volymuppskattningen och volymfelet (beräknat som skillnaden mellan den verkliga mängden och den uppskattade mängden blodförlust).

SPID is a comprehensive dataset composed of synthetic particle image velocimetry (PIV) image pairs and their corresponding exact optical flow computations. It serves as a valuable resource for researchers and practitioners in the field. The dataset is organized into three subsets: training, validation, and test, distributed in a ratio of 70%, 15%, and 15%, respectively.

Each subset within SPID consists of an input denoted as "x", which comprises synthetic image pairs. These image pairs provide the necessary context for the optical flow computations. Additionally, an output termed "y" is provided, which represents the exact optical flow calculated for each image pair. Notably, the images within the dataset are single-channel, and the optical flow is decomposed into its u and v components.

The shape of the input subsets in SPID is given by (number of samples, number of frames, image width, image height, number of channels), representing the dimensions of the input data. On the other hand, the shape of the output subsets is given by (number of samples, velocity components, image width, image height), denoting the shape of the optical flow data.

It is important to mention that SPID dataset is a preprocessed version of the Raw Synthetic Particle Image Dataset (RSPID), ensuring improved usability and reliability. Moreover, the dataset is packaged as a NumPy compressed NPZ file, which conveniently stores the inputs and outputs as separate NumPy NPZ files with the labels train, validation and test as acess keys. This format simplifies data extraction and integration into machine learning frameworks and libraries, facilitating seamless usage of the dataset.

SPID incorporates various factors that impact PIV analysis to provide a comprehensive and realistic simulation. The dataset includes image pairs with an image width of 665 pixels and an image height of 630 pixels, ensuring a high level of detail and accuracy with an 8-bit depth. It incorporates different particle radii (1, 2, 3, and 4 pixels) and particle densities (15, 17, 20, 23, 25, and 32 particles) to capture diverse particle configurations.

To simulate real-world scenarios, SPID introduces displacement variations through the delta x factor, ranging from 0.05% to 0.25%. Noise levels (1, 5, 10, and 15) are also incorporated to mimic practical PIV measurements with varying degrees of noise. Furthermore, out-of-plane motion effects are considered with standard deviations of 0.01, 0.025, and 0.05 to assess their impact on optical flow accuracy.

The dataset covers a wide range of flow patterns encountered in fluid dynamics. It includes Rankine uniform, Rankine vortex, parabolic, stagnation, shear, and decaying vortex flows, allowing for comprehensive testing and evaluation of PIV algorithms across different scenarios.

By leveraging the SPID dataset, researchers can develop and validate PIV algorithms and techniques under various challenging conditions. Its realistic and diverse simulation of particle image velocimetry scenarios makes it an invaluable tool for advancing the field and improving the accuracy and reliability of optical flow computations.

This record contains raw data related to article “Incidence and predictors of hepatocellular carcinoma in patients with autoimmune hepatitis"

Abstract

Background and aims: Autoimmune hepatitis (AIH) is a rare chronic liver disease of unknown aetiology; the risk of hepatocellular carcinoma (HCC) remains unclear and risk factors are not well-defined. We aimed to investigate the risk of HCC across a multicentre AIH cohort and to identify predictive factors.

Methods: We performed a retrospective, observational, multicentric study of patients included in the International Autoimmune Hepatitis Group Retrospective Registry. The assessed clinical outcomes were HCC development, liver transplantation, and death. Fine and Gray regression analysis stratified by centre was applied to determine the effects of individual covariates; the cumulative incidence of HCC was estimated using the competing risk method with death as a competing risk.

Results: A total of 1,428 patients diagnosed with AIH from 1980 to 2020 from 22 eligible centres across Europe and Canada were included, with a median follow-up of 11.1 years (interquartile range 5.2-15.9). Two hundred and ninety-three (20.5%) patients had cirrhosis at diagnosis. During follow-up, 24 patients developed HCC (1.7%), an incidence rate of 1.44 cases/1,000 patient-years; the cumulative incidence of HCC increased over time (0.6% at 5 years, 0.9% at 10 years, 2.7% at 20 years, and 6.6% at 30 years of follow-up). Patients who developed cirrhosis during follow-up had a significantly higher incidence of HCC. The cumulative incidence of HCC was 2.6%, 4.6%, 5.6% and 6.6% at 5, 10, 15, and 20 years after the development of cirrhosis, respectively. Obesity (hazard ratio [HR] 2.94, p = 0.04), cirrhosis (HR 3.17, p = 0.01), and AIH/PSC variant syndrome (HR 5.18, p = 0.007) at baseline were independent risk factors for HCC development.

Conclusions: HCC incidence in AIH is low even after cirrhosis development and is associated with risk factors including obesity, cirrhosis, and AIH/PSC variant syndrome.

Impact and implications: The risk of developing hepatocellular carcinoma (HCC) in individuals with autoimmune hepatitis (AIH) seems to be lower than for other aetiologies of chronic liver disease. Yet, solid data for this specific patient group remain elusive, given that most of the existing evidence comes from small, single-centre studies. In our study, we found that HCC incidence in patients with AIH is low even after the onset of cirrhosis. Additionally, factors such as advanced age, obesity, cirrhosis, alcohol consumption, and the presence of the AIH/PSC variant syndrome at the time of AIH diagnosis are linked to a higher risk of HCC. Based on these findings, there seems to be merit in adopting a specialized HCC monitoring programme for patients with AIH based on their individual risk factors.

DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

COVID-19 cases, tests, and associated deaths from COVID-19 that have been reported among Connecticut residents. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the daily COVID-19 update.

The case rate per 100,000 includes probable and confirmed cases. Probable and confirmed are defined using the CSTE case definition, which is available online: https://cdn.ymaws.com/www.cste.org/resource/resmgr/2020ps/Interim-20-ID-01_COVID-19.pdf

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics

Data are reported daily, with timestamps indicated in the daily briefings posted at: portal.ct.gov/coronavirus. Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: Due to an issue with the town-level data dated 1/17/2021, the data was temporarily unavailable; as of 11:19 AM on 1/19/2021 the data has been restored.

As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differ from the timestamp in DPH's daily PDF reports.

Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.

On 5/16/2022, 8,622 historical cases were included in the data. The date range for these cases were from August 2021 – April 2022.”