The National Science Foundation (NSF) Surveys of Public Attitudes monitored the general public's attitudes toward and interest in science and technology. In addition, the survey assessed levels of literacy and understanding of scientific and environmental concepts and constructs, how scientific knowledge and information were acquired, attentiveness to public policy issues, and computer access and usage. Since 1979, the survey was administered at regular intervals (occurring every two or three years), producing 11 cross-sectional surveys through 2001. Data for Part 1 (Survey of Public Attitudes Multiple Wave Data) were comprised of the survey questionnaire items asked most often throughout the 22-year survey series and account for approximately 70 percent of the original questions asked. Data for Part 2, General Social Survey Subsample Data, combine the 1983-1999 Survey of Public Attitudes data with a subsample from the 2002 General Social Survey (GSS) (GENERAL SOCIAL SURVEYS, 1972-2002: [CUMULATIVE FILE] [ICPSR 3728]) and focus solely on levels of education and computer access and usage. Variables for Part 1 include the respondents' interest in new scientific or medical discoveries and inventions, space exploration, military and defense policies, whether they voted in a recent election, if they had ever contacted an elected or public official about topics regarding science, energy, defense, civil rights, foreign policy, or general economics, and how they felt about government spending on scientific research. Respondents were asked how they received information concerning science or news (e.g., via newspapers, magazines, or television), what types of television programming they watched, and what kind of magazines they read. Respondents were asked a series of questions to assess their understanding of scientific concepts like DNA, probability, and experimental methods. Respondents were also asked if they agreed with statements concerning science and technology and how they affect everyday living. Respondents were further asked a series of true and false questions regarding science-based statements (e.g., the center of the Earth is hot, all radioactivity is manmade, electrons are smaller than atoms, the Earth moves around the sun, humans and dinosaurs co-existed, and human beings developed from earlier species of animals). Variables for Part 2 include highest level of math attained in high school, whether the respondent had a postsecondary degree, field of highest degree, number of science-based college courses taken, major in college, household ownership of a computer, access to the World Wide Web, number of hours spent on a computer at home or at work, and topics searched for via the Internet. Demographic variables for Parts 1 and 2 include gender, race, age, marital status, number of people in household, level of education, and occupation.

The researcher survey charted the current practices for preservation, sharing, and reuse of digital data in the field of humanities and health sciences. The survey also charted the respondents' views on open access and the reuse of digital research data, as well as their opinions on how to most effectively spread information about open access and research data management. The survey was a part of the FSD's "National Service Provider for CESSDA: Establish, Expand, Equip" project, in which services are tailored specifically for humanities and health sciences. The project was funded by the Academy of Finland. The questionnaire began with general questions charting the respondents' knowledge about open access and the services provided by the FSD. Next the respondents' views were probed on how digital research data is used at their department/school/unit after research has been completed. The respondents were then asked about barriers relating to archiving and reuse of digital research data as well as the restrictions presented by removal of personal identifiers from data in their field of study. Furthermore, the respondents were asked which factors contribute to the fact that data from completed research projects is not reused in their field of study. In the next part, the respondents were asked about their opinions on how to restrict the use of datasets if doing so was necessary. Next the respondents were thoroughly interviewed about their views on the advantages and disadvantages of increasing the reuse of digital data. Finally, the respondents were asked which channels and methods would be good for sharing information about research data management and open access. The background variables included the respondents' organisation, gender, and field of study.

This is a database snapshot of the iCite web service (provided here as a single zipped CSV file, or compressed, tarred JSON files). In addition, citation links in the NIH Open Citation Collection are provided as a two-column CSV table in open_citation_collection.zip. iCite provides bibliometrics and metadata on publications indexed in PubMed, organized into three modules:Influence: Delivers metrics of scientific influence, field-adjusted and benchmarked to NIH publications as the baseline.Translation: Measures how Human, Animal, or Molecular/Cellular Biology-oriented each paper is; tracks and predicts citation by clinical articlesOpen Cites: Disseminates link-level, public-domain citation data from the NIH Open Citation CollectionDefinitions for individual data fields:pmid: PubMed Identifier, an article ID as assigned in PubMed by the National Library of Medicinedoi: Digital Object Identifier, if availableyear: Year the article was publishedtitle: Title of the articleauthors: List of author namesjournal: Journal name (ISO abbreviation)is_research_article: Flag indicating whether the Publication Type tags for this article are consistent with that of a primary research articlerelative_citation_ratio: Relative Citation Ratio (RCR)--OPA's metric of scientific influence. Field-adjusted, time-adjusted and benchmarked against NIH-funded papers. The median RCR for NIH funded papers in any field is 1.0. An RCR of 2.0 means a paper is receiving twice as many citations per year than the median NIH funded paper in its field and year, while an RCR of 0.5 means that it is receiving half as many citations per year. Calculation details are documented in Hutchins et al., PLoS Biol. 2016;14(9):e1002541.provisional: RCRs for papers published in the previous two years are flagged as "provisional", to reflect that citation metrics for newer articles are not necessarily as stable as they are for older articles. Provisional RCRs are provided for papers published previous year, if they have received with 5 citations or more, despite being, in many cases, less than a year old. All papers published the year before the previous year receive provisional RCRs. The current year is considered to be the NIH Fiscal Year which starts in October. For example, in July 2019 (NIH Fiscal Year 2019), papers from 2018 receive provisional RCRs if they have 5 citations or more, and all papers from 2017 receive provisional RCRs. In October 2019, at the start of NIH Fiscal Year 2020, papers from 2019 receive provisional RCRs if they have 5 citations or more and all papers from 2018 receive provisional RCRs.citation_count: Number of unique articles that have cited this onecitations_per_year: Citations per year that this article has received since its publication. If this appeared as a preprint and a published article, the year from the published version is used as the primary publication date. This is the numerator for the Relative Citation Ratio.field_citation_rate: Measure of the intrinsic citation rate of this paper's field, estimated using its co-citation network.expected_citations_per_year: Citations per year that NIH-funded articles, with the same Field Citation Rate and published in the same year as this paper, receive. This is the denominator for the Relative Citation Ratio.nih_percentile: Percentile rank of this paper's RCR compared to all NIH publications. For example, 95% indicates that this paper's RCR is higher than 95% of all NIH funded publications.human: Fraction of MeSH terms that are in the Human category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)animal: Fraction of MeSH terms that are in the Animal category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)molecular_cellular: Fraction of MeSH terms that are in the Molecular/Cellular Biology category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)x_coord: X coordinate of the article on the Triangle of Biomediciney_coord: Y Coordinate of the article on the Triangle of Biomedicineis_clinical: Flag indicating that this paper meets the definition of a clinical article.cited_by_clin: PMIDs of clinical articles that this article has been cited by.apt: Approximate Potential to Translate is a machine learning-based estimate of the likelihood that this publication will be cited in later clinical trials or guidelines. Calculation details are documented in Hutchins et al., PLoS Biol. 2019;17(10):e3000416.cited_by: PMIDs of articles that have cited this one.references: PMIDs of articles in this article's reference list.Large CSV files are zipped using zip version 4.5, which is more recent than the default unzip command line utility in some common Linux distributions. These files can be unzipped with tools that support version 4.5 or later such as 7zip.Comments and questions can be addressed to iCite@mail.nih.gov

About this dataset

Context

The dataset tabulates the Tuscaloosa population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Tuscaloosa across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Tuscaloosa was 110,602, a 1.39% increase year-by-year from 2021. Previously, in 2021, Tuscaloosa population was 109,082, an increase of 4.67% compared to a population of 104,214 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Tuscaloosa increased by 31,687. In this period, the peak population was 110,602 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Tuscaloosa is shown in this column.
• Year on Year Change: This column displays the change in Tuscaloosa population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Tuscaloosa Population by Year. You can refer the same here

Version information CARDIO:DE 1.1 Major review of medication annotations by two physicians with improved IAA scores Fixed minor relation annotation bugs Fixed minor section annotation bugs Unified corpus format to UIMA CAS (XML 1.1) Removed CARDIO:DE_EXP. Now all annotations available in CARDIO:DE (Previous version: CARDIO:DE 1.01) Abstract: We present CARDIO:DE, the first freely available and distributable large German clinical corpus from the cardiovascular domain. CARDIO:DE encompasses 500 clinical routine German doctor’s letters from Heidelberg University Hospital, which were manually annotated. Our prospective study design complies well with current data protection regulations and allows us to keep the original structure of clinical documents consistent. In order to ease access to our corpus, we manually de-identified all letters. To enable various information extraction tasks the temporal information in the documents was preserved. We added two high-quality manual annotation layers to CARDIO:DE, (1) medication information and (2) CDA-compliant section classes. To the best of our knowledge, CARDIO:DE is the first freely available and distributable German clinical corpus in the cardiovascular domain. In summary, our corpus offers unique opportunities for collaborative and reproducible research on natural language processing models for German clinical texts. If you make use of the corpus, please cite the following resource: Richter-Pechanski, P. et al. CARDIO:DE. heiData https://doi.org/10.11588/data/AFYQDY (2022). If you cite the related manuscript published at Scientific Data, please cite: Richter-Pechanski, P., Wiesenbach, P., Schwab, D.M. et al. A distributable German clinical corpus containing cardiovascular clinical routine doctor’s letters. Sci Data 10, 207 (2023). https://doi.org/10.1038/s41597-023-02128-9. CARDIO:DE contains manual gold standard annotations of: medication information (ActiveIng, Dosage, Drug, Duration, Form, Frequency, Reason, Route, Strength) section types (Abschluss, Anamnese, Anrede, Diagnosen, AufnahmeMedikation, Befunde, EchoBefunde, AktuellDiagnosen, EntlassMedikation, KuBefunde, Labor, Mix, AllergienUnverträglichkeitenRisiken, Zusammenfassung) Information about how to access the data, see Terms -> Terms of Use section.

About this dataset

Context

The dataset tabulates the Kenefic population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Kenefic across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Kenefic was 153, a 2.68% increase year-by-year from 2021. Previously, in 2021, Kenefic population was 149, an increase of 0.68% compared to a population of 148 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Kenefic decreased by 25. In this period, the peak population was 218 in the year 2019. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Kenefic is shown in this column.
• Year on Year Change: This column displays the change in Kenefic population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Kenefic Population by Year. You can refer the same here

This description is part of the blog post "Systematic Literature Review of teaching Open Science" https://sozmethode.hypotheses.org/839

According to my opinion, we do not pay enough attention to teaching Open Science in higher education. Therefore, I designed a seminar to teach students the practices of Open Science by doing qualitative research.About this seminar, I wrote the article ”Teaching Open Science and qualitative methods“. For the article ”Teaching Open Science and qualitative methods“, I started to review the literature on ”Teaching Open Science“. The result of my literature review is that certain aspects of Open Science are used for teaching. However, Open Science with all its aspects (Open Access, Open Data, Open Methodology, Open Science Evaluation and Open Science Tools) is not an issue in publications about teaching.

Based on this insight, I have started a systematic literature review. I realized quickly that I need help to analyse and interpret the articles and to evaluate my preliminary findings. Especially different disciplinary cultures of teaching different aspects of Open Science are challenging, as I myself, as a social scientist, do not have enough insight to be able to interpret the results correctly. Therefore, I would like to invite you to participate in this research project!

I am now looking for people who would like to join a collaborative process to further explore and write the systematic literature review on “Teaching Open Science“. Because I want to turn this project into a Massive Open Online Paper (MOOP). According to the 10 rules of Tennant et al (2019) on MOOPs, it is crucial to find a core group that is enthusiastic about the topic. Therefore, I am looking for people who are interested in creating the structure of the paper and writing the paper together with me. I am also looking for people who want to search for and review literature or evaluate the literature I have already found. Together with the interested persons I would then define, the rules for the project (cf. Tennant et al. 2019). So if you are interested to contribute to the further search for articles and / or to enhance the interpretation and writing of results, please get in touch. For everyone interested to contribute, the list of articles collected so far is freely accessible at Zotero: https://www.zotero.org/groups/2359061/teaching_open_science. The figure shown below provides a first overview of my ongoing work. I created the figure with the free software yEd and uploaded the file to zenodo, so everyone can download and work with it:

To make transparent what I have done so far, I will first introduce what a systematic literature review is. Secondly, I describe the decisions I made to start with the systematic literature review. Third, I present the preliminary results.

Systematic literature review – an Introduction

Systematic literature reviews “are a method of mapping out areas of uncertainty, and identifying where little or no relevant research has been done.” (Petticrew/Roberts 2008: 2). Fink defines the systematic literature review as a “systemic, explicit, and reproducible method for identifying, evaluating, and synthesizing the existing body of completed and recorded work produced by researchers, scholars, and practitioners.” (Fink 2019: 6). The aim of a systematic literature reviews is to surpass the subjectivity of a researchers’ search for literature. However, there can never be an objective selection of articles. This is because the researcher has for example already made a preselection by deciding about search strings, for example “Teaching Open Science”. In this respect, transparency is the core criteria for a high-quality review.

In order to achieve high quality and transparency, Fink (2019: 6-7) proposes the following seven steps:

Selecting a research question.

Selecting the bibliographic database.

Choosing the search terms.

Applying practical screening criteria.

Applying methodological screening criteria.

Doing the review.

Synthesizing the results.

I have adapted these steps for the “Teaching Open Science” systematic literature review. In the following, I will present the decisions I have made.

Systematic literature review – decisions I made

Research question: I am interested in the following research questions: How is Open Science taught in higher education? Is Open Science taught in its full range with all aspects like Open Access, Open Data, Open Methodology, Open Science Evaluation and Open Science Tools? Which aspects are taught? Are there disciplinary differences as to which aspects are taught and, if so, why are there such differences?

Databases: I started my search at the Directory of Open Science (DOAJ). “DOAJ is a community-curated online directory that indexes and provides access to high quality, open access, peer-reviewed journals.” (https://doaj.org/) Secondly, I used the Bielefeld Academic Search Engine (base). Base is operated by Bielefeld University Library and “one of the world’s most voluminous search engines especially for academic web resources” (base-search.net). Both platforms are non-commercial and focus on Open Access publications and thus differ from the commercial publication databases, such as Web of Science and Scopus. For this project, I deliberately decided against commercial providers and the restriction of search in indexed journals. Thus, because my explicit aim was to find articles that are open in the context of Open Science.

Search terms: To identify articles about teaching Open Science I used the following search strings: “teaching open science” OR teaching “open science” OR teach „open science“. The topic search looked for the search strings in title, abstract and keywords of articles. Since these are very narrow search terms, I decided to broaden the method. I searched in the reference lists of all articles that appear from this search for further relevant literature. Using Google Scholar I checked which other authors cited the articles in the sample. If the so checked articles met my methodological criteria, I included them in the sample and looked through the reference lists and citations at Google Scholar. This process has not yet been completed.

Practical screening criteria: I have included English and German articles in the sample, as I speak these languages (articles in other languages are very welcome, if there are people who can interpret them!). In the sample only journal articles, articles in edited volumes, working papers and conference papers from proceedings were included. I checked whether the journals were predatory journals – such articles were not included. I did not include blogposts, books or articles from newspapers. I only included articles that fulltexts are accessible via my institution (University of Kassel). As a result, recently published articles at Elsevier could not be included because of the special situation in Germany regarding the Project DEAL (https://www.projekt-deal.de/about-deal/). For articles that are not freely accessible, I have checked whether there is an accessible version in a repository or whether preprint is available. If this was not the case, the article was not included. I started the analysis in May 2019.

Methodological criteria: The method described above to check the reference lists has the problem of subjectivity. Therefore, I hope that other people will be interested in this project and evaluate my decisions. I have used the following criteria as the basis for my decisions: First, the articles must focus on teaching. For example, this means that articles must describe how a course was designed and carried out. Second, at least one aspect of Open Science has to be addressed. The aspects can be very diverse (FOSS, repositories, wiki, data management, etc.) but have to comply with the principles of openness. This means, for example, I included an article when it deals with the use of FOSS in class and addresses the aspects of openness of FOSS. I did not include articles when the authors describe the use of a particular free and open source software for teaching but did not address the principles of openness or re-use.

Doing the review: Due to the methodical approach of going through the reference lists, it is possible to create a map of how the articles relate to each other. This results in thematic clusters and connections between clusters. The starting point for the map were four articles (Cook et al. 2018; Marsden, Thompson, and Plonsky 2017; Petras et al. 2015; Toelch and Ostwald 2018) that I found using the databases and criteria described above. I used yEd to generate the network. „yEd is a powerful desktop application that can be used to quickly and effectively generate high-quality diagrams.” (https://www.yworks.com/products/yed) In the network, arrows show, which articles are cited in an article and which articles are cited by others as well. In addition, I made an initial rough classification of the content using colours. This classification is based on the contents mentioned in the articles’ title and abstract. This rough content classification requires a more exact, i.e., content-based subdivision and evaluation by others, who are experts in the respective fields/disciplines.

Science is becoming more and more international breaking down walls in its’ pursuit for high impact. Despite geographical location and distance are still standing as major barriers for scientific collaboration, little is known whether high impact collaborations are similarly constrained by geography like collaborations with an average impact. To address this question, we analyze Web of Science data on international collaboration between global leader cities in science production. We report increasing intensity of international city-city collaboration and find that average distance of collaboration of the strongest connections has slightly increased but distance-decay has remained stable over the last three decades. However, high impact collaborations span large distances by following similar distance decay. This finding suggests that a larger geographical reach of research collaboration should be aimed for to support high impact science. The creation of European Research Area was effective action that has deepened of intracontinental research collaborations and the position of the European Union (EU) in global science. Yet, our results provide new evidence that global scientific leaders are not collaborative enough in carrying out their big science projects.

This is a database snapshot of the iCite web service (provided here as a single zipped CSV file, or compressed, tarred JSON files). In addition, citation links in the NIH Open Citation Collection are provided as a two-column CSV table in open_citation_collection.zip. iCite provides bibliometrics and metadata on publications indexed in PubMed, organized into three modules:Influence: Delivers metrics of scientific influence, field-adjusted and benchmarked to NIH publications as the baseline.Translation: Measures how Human, Animal, or Molecular/Cellular Biology-oriented each paper is; tracks and predicts citation by clinical articlesOpen Cites: Disseminates link-level, public-domain citation data from the NIH Open Citation CollectionDefinitions for individual data fields:pmid: PubMed Identifier, an article ID as assigned in PubMed by the National Library of Medicinedoi: Digital Object Identifier, if availableyear: Year the article was publishedtitle: Title of the articleauthors: List of author namesjournal: Journal name (ISO abbreviation)is_research_article: Flag indicating whether the Publication Type tags for this article are consistent with that of a primary research articlerelative_citation_ratio: Relative Citation Ratio (RCR)--OPA's metric of scientific influence. Field-adjusted, time-adjusted and benchmarked against NIH-funded papers. The median RCR for NIH funded papers in any field is 1.0. An RCR of 2.0 means a paper is receiving twice as many citations per year than the median NIH funded paper in its field and year, while an RCR of 0.5 means that it is receiving half as many citations per year. Calculation details are documented in Hutchins et al., PLoS Biol. 2016;14(9):e1002541.provisional: RCRs for papers published in the previous two years are flagged as "provisional", to reflect that citation metrics for newer articles are not necessarily as stable as they are for older articles. Provisional RCRs are provided for papers published previous year, if they have received with 5 citations or more, despite being, in many cases, less than a year old. All papers published the year before the previous year receive provisional RCRs. The current year is considered to be the NIH Fiscal Year which starts in October. For example, in July 2019 (NIH Fiscal Year 2019), papers from 2018 receive provisional RCRs if they have 5 citations or more, and all papers from 2017 receive provisional RCRs. In October 2019, at the start of NIH Fiscal Year 2020, papers from 2019 receive provisional RCRs if they have 5 citations or more and all papers from 2018 receive provisional RCRs.citation_count: Number of unique articles that have cited this onecitations_per_year: Citations per year that this article has received since its publication. If this appeared as a preprint and a published article, the year from the published version is used as the primary publication date. This is the numerator for the Relative Citation Ratio.field_citation_rate: Measure of the intrinsic citation rate of this paper's field, estimated using its co-citation network.expected_citations_per_year: Citations per year that NIH-funded articles, with the same Field Citation Rate and published in the same year as this paper, receive. This is the denominator for the Relative Citation Ratio.nih_percentile: Percentile rank of this paper's RCR compared to all NIH publications. For example, 95% indicates that this paper's RCR is higher than 95% of all NIH funded publications.human: Fraction of MeSH terms that are in the Human category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)animal: Fraction of MeSH terms that are in the Animal category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)molecular_cellular: Fraction of MeSH terms that are in the Molecular/Cellular Biology category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)x_coord: X coordinate of the article on the Triangle of Biomediciney_coord: Y Coordinate of the article on the Triangle of Biomedicineis_clinical: Flag indicating that this paper meets the definition of a clinical article.cited_by_clin: PMIDs of clinical articles that this article has been cited by.apt: Approximate Potential to Translate is a machine learning-based estimate of the likelihood that this publication will be cited in later clinical trials or guidelines. Calculation details are documented in Hutchins et al., PLoS Biol. 2019;17(10):e3000416.cited_by: PMIDs of articles that have cited this one.references: PMIDs of articles in this article's reference list.Large CSV files are zipped using zip version 4.5, which is more recent than the default unzip command line utility in some common Linux distributions. These files can be unzipped with tools that support version 4.5 or later such as 7zip.Comments and questions can be addressed to iCite@mail.nih.gov

This data product consists of measurements from rate counters. The rate count data are diagnostic fields, have uncalibrated energy ranges, and wrap near or above 16384 counts/s. The rate count values are stored as 4 s accumulations of counts.The BARREL Mission was a multiple-balloon investigation designed to study electron losses from Earth's Radiation Belts. Selected as a NASA Living with a Star Mission of Opportunity, BARREL was designed to augment the Radiation Belt Storm Probes, RBSP, mission by providing measurements of the spatial and temporal variations of electron precipitation from the radiation belts. The RBSP mission has since been renamed the Van Allen Probes mission. Each BARREL balloon carried an X-ray spectrometer to measure the bremsstrahlung X-rays produced by precipitating relativistic electrons as they collide with neutrals in the atmosphere, and a DC magnetometer to measure ULF-timescale variations of the magnetic field. BARREL observations collected near latitudes close to either the antarctic and arctic circles at stratospheric altitudes at about 30 km. The BARREL instrumentation provided the first balloon measurements of relativistic electron precipitation while comprehensive in situ measurements of both plasma waves and energetic particles were available. Also, the BARREL data has been used to characterize the spatial scale of precipitation at relativistic energies.The initial pair of balloon campaigns that were conducted initially during the Austral summer months of January and February of 2013 and 2014 with launches from two stations located in Antarctica: the British base located at Halley Bay on the Brunt Ice Shelf and the South African SANAE IV base (SANAE stand for South African National Antarctic Expedition) located in Vesleskarvet, Queen Maud Land. For the 2013 and 2014 the balloon campaigns, the launch plan was designed to maintain an array with about five payloads spread across about six hours of magnetic local time, MLT, in the region that magnetically maps to the radiation belts. Thus, the BARREL balloon constellation constituted an evolving and slowly moving array able to study relativistic electron precipitation from the radiation belts.Later campaigns were undertaken in 2015 and 2016 from the Esrange Space Center located in Kiruna, Sweden. The 2015 and 2016 campaigns were undertaken in coordination with the Van Allen Probes mission, the European Incoherent Scatter Scientific Association, EISCAT, incoherent scatter radar system, and other ground and space based instruments. Seven balloon launches occurred during the August 2015 BARREL campaign. A total of eight flights occurred during August 2016.Summing over the four BARREL campaigns, over 50 small, approximately 20 kg, stratospheric balloons were successively launched. The website creeated and hosted by A.J. Halford (see Information URL below) reports that: "By the end of the campaigns, there were over 90 researchers coordinating on a daily basis with the BARREL team working on 7 different satellite missions, 1 other balloon mission, and way too many ground based instruments to count." Although the BARREL mission launched only balloons during the years from 2013 to 2016, research using data collected on these flights is ongoing, so stay tuned for updates! All data and analysis software are freely available to the scientific community.The information listed above in this resource description was compiled by referencing several BARREL related resources including primarily the Millan et al. (2013) Space Science Reviews publication, the BARREL at Dartmouth mission web site, and the website maintained by A.J. Halford.The current release of all BARREL CDF data products are Version 10 files.BARREL will make all its scientific data products quickly and publicly available but all users are expected to read and follow the BARREL Data Usage Policy listed below.BARREL Data Usage PolicyBARREL data products are made freely available to the public and every effort is made to ensure that these products are of the highest quality. However, there may occasionally be issues with either the instruments or data processing that affect the accuracy of data. When possible, a quality flag is included in higher level data products, and known issues are posted in the BARREL data repository. You are also strongly encouraged to follow the guidelines below if you are planning a publication or presentation in which BARREL data are used. This will help you ensure that your science results are valid. Users should always use the highest version numbers of data and analysis tools. Browse/quick-look plots are not intended for science analysis or publication and should not be used for those purposes without consent of the principal investigator, PI. Users should notify the BARREL PI of the data use and investigation objectives. This will ensure that you are using the data appropriately and have the most recent version of the data or analysis routines. Additionally, if a BARREL team member is already working on a similar or related topic, they may be able to contribute intellectually. If BARREL team members are not part of the author list, then users should Credit/Acknowledge the BARREL team as follows: We acknowledge the BARREL team (PI: Robyn Millan) for use of BARREL data. Users are also requested to provide the PI with a copy of each manuscript that uses BARREL data upon submission of that manuscript for consideration of publication. On publication, the citation should be transmitted to the PI.The BARREL PI can be contacted at: Robyn.Millan@dartmouth.edu.An online copy of the BARREL Data Usage Policy document can be found at: https://barrel.rmillan.host.dartmouth.edu/documents/data.use.policy.pdf.

This is a database snapshot of the iCite web service (provided here as a single zipped CSV file, or compressed, tarred JSON files). In addition, citation links in the NIH Open Citation Collection are provided as a two-column CSV table in open_citation_collection.zip. iCite provides bibliometrics and metadata on publications indexed in PubMed, organized into three modules:

Influence: Delivers metrics of scientific influence, field-adjusted and benchmarked to NIH publications as the baseline.

Translation: Measures how Human, Animal, or Molecular/Cellular Biology-oriented each paper is; tracks and predicts citation by clinical articles

Open Cites: Disseminates link-level, public-domain citation data from the NIH Open Citation Collection

Definitions for individual data fields:

pmid: PubMed Identifier, an article ID as assigned in PubMed by the National Library of Medicine

doi: Digital Object Identifier, if available

year: Year the article was published

title: Title of the article

authors: List of author names

journal: Journal name (ISO abbreviation)

is_research_article: Flag indicating whether the Publication Type tags for this article are consistent with that of a primary research article

relative_citation_ratio: Relative Citation Ratio (RCR)--OPA's metric of scientific influence. Field-adjusted, time-adjusted and benchmarked against NIH-funded papers. The median RCR for NIH funded papers in any field is 1.0. An RCR of 2.0 means a paper is receiving twice as many citations per year than the median NIH funded paper in its field and year, while an RCR of 0.5 means that it is receiving half as many citations per year. Calculation details are documented in Hutchins et al., PLoS Biol. 2016;14(9):e1002541.

provisional: RCRs for papers published in the previous two years are flagged as "provisional", to reflect that citation metrics for newer articles are not necessarily as stable as they are for older articles. Provisional RCRs are provided for papers published previous year, if they have received with 5 citations or more, despite being, in many cases, less than a year old. All papers published the year before the previous year receive provisional RCRs. The current year is considered to be the NIH Fiscal Year which starts in October. For example, in July 2019 (NIH Fiscal Year 2019), papers from 2018 receive provisional RCRs if they have 5 citations or more, and all papers from 2017 receive provisional RCRs. In October 2019, at the start of NIH Fiscal Year 2020, papers from 2019 receive provisional RCRs if they have 5 citations or more and all papers from 2018 receive provisional RCRs.

citation_count: Number of unique articles that have cited this one

citations_per_year: Citations per year that this article has received since its publication. If this appeared as a preprint and a published article, the year from the published version is used as the primary publication date. This is the numerator for the Relative Citation Ratio.

field_citation_rate: Measure of the intrinsic citation rate of this paper's field, estimated using its co-citation network.

expected_citations_per_year: Citations per year that NIH-funded articles, with the same Field Citation Rate and published in the same year as this paper, receive. This is the denominator for the Relative Citation Ratio.

nih_percentile: Percentile rank of this paper's RCR compared to all NIH publications. For example, 95% indicates that this paper's RCR is higher than 95% of all NIH funded publications.

human: Fraction of MeSH terms that are in the Human category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)

animal: Fraction of MeSH terms that are in the Animal category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)

molecular_cellular: Fraction of MeSH terms that are in the Molecular/Cellular Biology category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)

x_coord: X coordinate of the article on the Triangle of Biomedicine

y_coord: Y Coordinate of the article on the Triangle of Biomedicine

is_clinical: Flag indicating that this paper meets the definition of a clinical article.

cited_by_clin: PMIDs of clinical articles that this article has been cited by.

apt: Approximate Potential to Translate is a machine learning-based estimate of the likelihood that this publication will be cited in later clinical trials or guidelines. Calculation details are documented in Hutchins et al., PLoS Biol. 2019;17(10):e3000416.

cited_by: PMIDs of articles that have cited this one.

references: PMIDs of articles in this article's reference list.

Large CSV files are zipped using zip version 4.5, which is more recent than the default unzip command line utility in some common Linux distributions. These files can be unzipped with tools that support version 4.5 or later such as 7zip.

Comments and questions can be addressed to iCite@mail.nih.gov

These data have been collected by the United States Geological Survey’s Northeast Amphibian Research and Monitoring Initiative (USGS NEARMI) scientists. We collected these data to examine if Bsal was introduced to a site where there was a known pet release in Western, Massachusetts.

Access to the CCNDC data requires successful completion and execution of a Data Use Agreement (DUA). The CCNDC consortium will approve access to data and/or images for research purposes only. The CCNDC consortium will review the Access Request and the proposed DUA of each recipient requesting data and determine whether to provide access based on the expectations outlined in the following pages. These expectations include the protection of data privacy, confidentiality, and security. In the event that requests raise particular concerns related to privacy and confidentiality, risks to populations or groups, or other concerns, the CCNDC consortium will consult with other experts as appropriate. The CCDNC consortium reserves the right to suspend or revoke approved access requests at any time if concerns arise regarding the appropriateness of data usage by recipient or recipient's compliance with the DUA.

Geographic and Magnetic Coordinates: The ephemeris data products, which include the balloon epoch time, latitude, longitude, and altitude, are each returned from the payload once every 4s. Geographic coordinates are obtained from an onboard Global Positioning System, GPS, unit. Magnetic coordinates are derived by using the International Radiation Belt Environment Modeling, IRBEM, FORTRAN library.The BARREL Mission was a multiple-balloon investigation designed to study electron losses from Earth's Radiation Belts. Selected as a NASA Living with a Star Mission of Opportunity, BARREL was designed to augment the Radiation Belt Storm Probes, RBSP, mission by providing measurements of the spatial and temporal variations of electron precipitation from the radiation belts. The RBSP mission has since been renamed the Van Allen Probes mission. Each BARREL balloon carried an X-ray spectrometer to measure the bremsstrahlung X-rays produced by precipitating relativistic electrons as they collide with neutrals in the atmosphere, and a DC magnetometer to measure ULF-timescale variations of the magnetic field. BARREL observations collected near latitudes close to either the antarctic and arctic circles at stratospheric altitudes at about 30 km. The BARREL instrumentation provided the first balloon measurements of relativistic electron precipitation while comprehensive in situ measurements of both plasma waves and energetic particles were available. Also, the BARREL data has been used to characterize the spatial scale of precipitation at relativistic energies.The initial pair of balloon campaigns that were conducted initially during the Austral summer months of January and February of 2013 and 2014 with launches from two stations located in Antarctica: the British base located at Halley Bay on the Brunt Ice Shelf and the South African SANAE IV base (SANAE stand for South African National Antarctic Expedition) located in Vesleskarvet, Queen Maud Land. For the 2013 and 2014 the balloon campaigns, the launch plan was designed to maintain an array with about five payloads spread across about six hours of magnetic local time, MLT, in the region that magnetically maps to the radiation belts. Thus, the BARREL balloon constellation constituted an evolving and slowly moving array able to study relativistic electron precipitation from the radiation belts.Later campaigns were undertaken in 2015 and 2016 from the Esrange Space Center located in Kiruna, Sweden. The 2015 and 2016 campaigns were undertaken in coordination with the Van Allen Probes mission, the European Incoherent Scatter Scientific Association, EISCAT, incoherent scatter radar system, and other ground and space based instruments. Seven balloon launches occurred during the August 2015 BARREL campaign. A total of eight flights occurred during August 2016.Summing over the four BARREL campaigns, over 50 small, approximately 20 kg, stratospheric balloons were successively launched. The website creeated and hosted by A.J. Halford (see Information URL below) reports that: "By the end of the campaigns, there were over 90 researchers coordinating on a daily basis with the BARREL team working on 7 different satellite missions, 1 other balloon mission, and way too many ground based instruments to count." Although the BARREL mission launched only balloons during the years from 2013 to 2016, research using data collected on these flights is ongoing, so stay tuned for updates! All data and analysis software are freely available to the scientific community.The information listed above in this resource description was compiled by referencing several BARREL related resources including primarily the Millan et al. (2013) Space Science Reviews publication, the BARREL at Dartmouth mission web site, and the website maintained by A.J. Halford.The current release of all BARREL CDF data products are Version 10 files.BARREL will make all its scientific data products quickly and publicly available but all users are expected to read and follow the BARREL Data Usage Policy listed below.BARREL Data Usage PolicyBARREL data products are made freely available to the public and every effort is made to ensure that these products are of the highest quality. However, there may occasionally be issues with either the instruments or data processing that affect the accuracy of data. When possible, a quality flag is included in higher level data products, and known issues are posted in the BARREL data repository. You are also strongly encouraged to follow the guidelines below if you are planning a publication or presentation in which BARREL data are used. This will help you ensure that your science results are valid. Users should always use the highest version numbers of data and analysis tools. Browse/quick-look plots are not intended for science analysis or publication and should not be used for those purposes without consent of the principal investigator, PI. Users should notify the BARREL PI of the data use and investigation objectives. This will ensure that you are using the data appropriately and have the most recent version of the data or analysis routines. Additionally, if a BARREL team member is already working on a similar or related topic, they may be able to contribute intellectually. If BARREL team members are not part of the author list, then users should Credit/Acknowledge the BARREL team as follows: We acknowledge the BARREL team (PI: Robyn Millan) for use of BARREL data. Users are also requested to provide the PI with a copy of each manuscript that uses BARREL data upon submission of that manuscript for consideration of publication. On publication, the citation should be transmitted to the PI.The BARREL PI can be contacted at: Robyn.Millan@dartmouth.edu.An online copy of the BARREL Data Usage Policy document can be found at: https://barrel.rmillan.host.dartmouth.edu/documents/data.use.policy.pdf.

This data product consists of measurements from rate counters. The rate count data are diagnostic fields, have uncalibrated energy ranges, and wrap near or above 16384 counts/s. The rate count values are stored as 4 s accumulations of counts.The BARREL Mission was a multiple-balloon investigation designed to study electron losses from Earth's Radiation Belts. Selected as a NASA Living with a Star Mission of Opportunity, BARREL was designed to augment the Radiation Belt Storm Probes, RBSP, mission by providing measurements of the spatial and temporal variations of electron precipitation from the radiation belts. The RBSP mission has since been renamed the Van Allen Probes mission. Each BARREL balloon carried an X-ray spectrometer to measure the bremsstrahlung X-rays produced by precipitating relativistic electrons as they collide with neutrals in the atmosphere, and a DC magnetometer to measure ULF-timescale variations of the magnetic field. BARREL observations collected near latitudes close to either the antarctic and arctic circles at stratospheric altitudes at about 30 km. The BARREL instrumentation provided the first balloon measurements of relativistic electron precipitation while comprehensive in situ measurements of both plasma waves and energetic particles were available. Also, the BARREL data has been used to characterize the spatial scale of precipitation at relativistic energies.The initial pair of balloon campaigns that were conducted initially during the Austral summer months of January and February of 2013 and 2014 with launches from two stations located in Antarctica: the British base located at Halley Bay on the Brunt Ice Shelf and the South African SANAE IV base (SANAE stand for South African National Antarctic Expedition) located in Vesleskarvet, Queen Maud Land. For the 2013 and 2014 the balloon campaigns, the launch plan was designed to maintain an array with about five payloads spread across about six hours of magnetic local time, MLT, in the region that magnetically maps to the radiation belts. Thus, the BARREL balloon constellation constituted an evolving and slowly moving array able to study relativistic electron precipitation from the radiation belts.Later campaigns were undertaken in 2015 and 2016 from the Esrange Space Center located in Kiruna, Sweden. The 2015 and 2016 campaigns were undertaken in coordination with the Van Allen Probes mission, the European Incoherent Scatter Scientific Association, EISCAT, incoherent scatter radar system, and other ground and space based instruments. Seven balloon launches occurred during the August 2015 BARREL campaign. A total of eight flights occurred during August 2016.Summing over the four BARREL campaigns, over 50 small, approximately 20 kg, stratospheric balloons were successively launched. The website creeated and hosted by A.J. Halford (see Information URL below) reports that: "By the end of the campaigns, there were over 90 researchers coordinating on a daily basis with the BARREL team working on 7 different satellite missions, 1 other balloon mission, and way too many ground based instruments to count." Although the BARREL mission launched only balloons during the years from 2013 to 2016, research using data collected on these flights is ongoing, so stay tuned for updates! All data and analysis software are freely available to the scientific community.The information listed above in this resource description was compiled by referencing several BARREL related resources including primarily the Millan et al. (2013) Space Science Reviews publication, the BARREL at Dartmouth mission web site, and the website maintained by A.J. Halford.The current release of all BARREL CDF data products are Version 10 files.BARREL will make all its scientific data products quickly and publicly available but all users are expected to read and follow the BARREL Data Usage Policy listed below.BARREL Data Usage PolicyBARREL data products are made freely available to the public and every effort is made to ensure that these products are of the highest quality. However, there may occasionally be issues with either the instruments or data processing that affect the accuracy of data. When possible, a quality flag is included in higher level data products, and known issues are posted in the BARREL data repository. You are also strongly encouraged to follow the guidelines below if you are planning a publication or presentation in which BARREL data are used. This will help you ensure that your science results are valid. Users should always use the highest version numbers of data and analysis tools. Browse/quick-look plots are not intended for science analysis or publication and should not be used for those purposes without consent of the principal investigator, PI. Users should notify the BARREL PI of the data use and investigation objectives. This will ensure that you are using the data appropriately and have the most recent version of the data or analysis routines. Additionally, if a BARREL team member is already working on a similar or related topic, they may be able to contribute intellectually. If BARREL team members are not part of the author list, then users should Credit/Acknowledge the BARREL team as follows: We acknowledge the BARREL team (PI: Robyn Millan) for use of BARREL data. Users are also requested to provide the PI with a copy of each manuscript that uses BARREL data upon submission of that manuscript for consideration of publication. On publication, the citation should be transmitted to the PI.The BARREL PI can be contacted at: Robyn.Millan@dartmouth.edu.An online copy of the BARREL Data Usage Policy document can be found at: https://barrel.rmillan.host.dartmouth.edu/documents/data.use.policy.pdf.

France Employment: Payroll: IF: Oth Professional, Scientific & Technical data was reported at 98,453.000 Person in 2016. This records an increase from the previous number of 96,929.000 Person for 2015. France Employment: Payroll: IF: Oth Professional, Scientific & Technical data is updated yearly, averaging 93,963.000 Person from Dec 1989 (Median) to 2016, with 28 observations. The data reached an all-time high of 101,568.000 Person in 2001 and a record low of 81,491.000 Person in 1992. France Employment: Payroll: IF: Oth Professional, Scientific & Technical data remains active status in CEIC and is reported by French National Institute for Statistics and Economic Studies. The data is categorized under Global Database’s France – Table FR.G017: Employment: Payroll Workers: by Region and Industry: NAF Rev. 2.

About this dataset

Context

The dataset tabulates the Mount Healthy population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Mount Healthy across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Mount Healthy was 6,871, a 0.94% decrease year-by-year from 2021. Previously, in 2021, Mount Healthy population was 6,936, a decline of 0.74% compared to a population of 6,988 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Mount Healthy increased by 536. In this period, the peak population was 6,988 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Mount Healthy is shown in this column.
• Year on Year Change: This column displays the change in Mount Healthy population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Mount Healthy Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the United States population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of United States across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of United States was 333,287,557, a 0.38% increase year-by-year from 2021. Previously, in 2021, United States population was 332,031,554, an increase of 0.16% compared to a population of 331,511,512 in 2020. Over the last 20 plus years, between 2000 and 2022, population of United States increased by 51,125,146. In this period, the peak population was 333,287,557 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the United States is shown in this column.
• Year on Year Change: This column displays the change in United States population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for United States Population by Year. You can refer the same here

This is a database snapshot of the iCite web service (provided here as a single zipped CSV file, or compressed, tarred JSON files). In addition, citation links in the NIH Open Citation Collection are provided as a two-column CSV table in open_citation_collection.zip. iCite provides bibliometrics and metadata on publications indexed in PubMed, organized into three modules:Influence: Delivers metrics of scientific influence, field-adjusted and benchmarked to NIH publications as the baseline.Translation: Measures how Human, Animal, or Molecular/Cellular Biology-oriented each paper is; tracks and predicts citation by clinical articlesOpen Cites: Disseminates link-level, public-domain citation data from the NIH Open Citation CollectionDefinitions for individual data fields:pmid: PubMed Identifier, an article ID as assigned in PubMed by the National Library of Medicinedoi: Digital Object Identifier, if availableyear: Year the article was publishedtitle: Title of the articleauthors: List of author namesjournal: Journal name (ISO abbreviation)is_research_article: Flag indicating whether the Publication Type tags for this article are consistent with that of a primary research articlerelative_citation_ratio: Relative Citation Ratio (RCR)--OPA's metric of scientific influence. Field-adjusted, time-adjusted and benchmarked against NIH-funded papers. The median RCR for NIH funded papers in any field is 1.0. An RCR of 2.0 means a paper is receiving twice as many citations per year than the median NIH funded paper in its field and year, while an RCR of 0.5 means that it is receiving half as many citations per year. Calculation details are documented in Hutchins et al., PLoS Biol. 2016;14(9):e1002541.provisional: RCRs for papers published in the previous two years are flagged as "provisional", to reflect that citation metrics for newer articles are not necessarily as stable as they are for older articles. Provisional RCRs are provided for papers published previous year, if they have received with 5 citations or more, despite being, in many cases, less than a year old. All papers published the year before the previous year receive provisional RCRs. The current year is considered to be the NIH Fiscal Year which starts in October. For example, in July 2019 (NIH Fiscal Year 2019), papers from 2018 receive provisional RCRs if they have 5 citations or more, and all papers from 2017 receive provisional RCRs. In October 2019, at the start of NIH Fiscal Year 2020, papers from 2019 receive provisional RCRs if they have 5 citations or more and all papers from 2018 receive provisional RCRs.citation_count: Number of unique articles that have cited this onecitations_per_year: Citations per year that this article has received since its publication. If this appeared as a preprint and a published article, the year from the published version is used as the primary publication date. This is the numerator for the Relative Citation Ratio.field_citation_rate: Measure of the intrinsic citation rate of this paper's field, estimated using its co-citation network.expected_citations_per_year: Citations per year that NIH-funded articles, with the same Field Citation Rate and published in the same year as this paper, receive. This is the denominator for the Relative Citation Ratio.nih_percentile: Percentile rank of this paper's RCR compared to all NIH publications. For example, 95% indicates that this paper's RCR is higher than 95% of all NIH funded publications.human: Fraction of MeSH terms that are in the Human category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)animal: Fraction of MeSH terms that are in the Animal category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)molecular_cellular: Fraction of MeSH terms that are in the Molecular/Cellular Biology category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)x_coord: X coordinate of the article on the Triangle of Biomediciney_coord: Y Coordinate of the article on the Triangle of Biomedicineis_clinical: Flag indicating that this paper meets the definition of a clinical article.cited_by_clin: PMIDs of clinical articles that this article has been cited by.apt: Approximate Potential to Translate is a machine learning-based estimate of the likelihood that this publication will be cited in later clinical trials or guidelines. Calculation details are documented in Hutchins et al., PLoS Biol. 2019;17(10):e3000416.cited_by: PMIDs of articles that have cited this one.references: PMIDs of articles in this article's reference list.Large CSV files are zipped using zip version 4.5, which is more recent than the default unzip command line utility in some common Linux distributions. These files can be unzipped with tools that support version 4.5 or later such as 7zip.Comments and questions can be addressed to iCite@mail.nih.gov

About this dataset

Context

The dataset tabulates the Lima town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Lima town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Lima town was 4,140, a 0.34% decrease year-by-year from 2021. Previously, in 2021, Lima town population was 4,154, an increase of 0.10% compared to a population of 4,150 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Lima town decreased by 469. In this period, the peak population was 4,609 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Lima town is shown in this column.
• Year on Year Change: This column displays the change in Lima town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Lima town Population by Year. You can refer the same here