This dataset outlines a proposed set of core, minimal metadata elements that can be used to describe biomedical datasets, such as those resulting from research funded by the National Institutes of Health. It can inform efforts to better catalog or index such data to improve discoverability. The proposed metadata elements are based on an analysis of the metadata schemas used in a set of NIH-supported data sharing repositories. Common elements from these data repositories were identified, mapped to existing data-specific metadata standards from to existing multidisciplinary data repositories, DataCite and Dryad, and compared with metadata used in MEDLINE records to establish a sustainable and integrated metadata schema. From the mappings, we developed a preliminary set of minimal metadata elements that can be used to describe NIH-funded datasets. Please see the readme file for more details about the individual sheets within the spreadsheet.

ToolRM Training Dataset

  📖 Dataset Description

This is a version of the training data utilized for ToolRM, a collection of outcome reward models specifically designed for evaluating and improving function-calling capabilities in large language models. It consists of ~459K examples, where each example includes a user-assistant conversation, available tool specifications, and a pair of correct and incorrect tool calls. The incorrect calls were generated by prompting 9… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/ToolRM-train-data.

This dataset contains information on air pollution exposure, social determinants of health, dementia outcomes, and related confounders from the National Health and Aging Trends Study. This dataset is not publicly accessible because: This data was not produced by the EPA and is not owned by the EPA. It can be accessed through the following means: The data can be accessed by contacting the corresponding author listed in the manuscript. Format: The data is in tabular format with rows corresponding to observations and columns corresponding to outcomes, confounders, and exposures. This dataset is associated with the following publication: Frndak, S., Z. Deng, C. Ward-Caviness, I. Gorski-Steiner, R. Thorpe, and A. Dickerson. Risk of Dementia Due to Co-Exposure to Air Pollution and Neighborhood Disadvantage. ENVIRONMENTAL RESEARCH. Elsevier B.V., Amsterdam, NETHERLANDS, 251(Part 2): 118709, (2024).

About this dataset

Context

The dataset tabulates the Azusa population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Azusa. The dataset can be utilized to understand the population distribution of Azusa by age. For example, using this dataset, we can identify the largest age group in Azusa.

Key observations

The largest age group in Azusa, CA was for the group of age 20 to 24 years years with a population of 4,973 (10.08%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Azusa, CA was the 85 years and over years with a population of 407 (0.83%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Azusa is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Azusa total population. 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 Azusa Population by Age. You can refer the same here

Please cite the following paper when using this dataset: N. Thakur, “Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions,” Preprints, 2022, DOI: 10.20944/preprints202206.0383.v1 Abstract The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and use cases in assisted living, military, healthcare, firefighting, and industries. With the projected increase in the diverse uses of exoskeletons in the next few years in these application domains and beyond, it is crucial to study, interpret, and analyze user perspectives, public opinion, reviews, and feedback related to exoskeletons, for which a dataset is necessary. The Internet of Everything era of today's living, characterized by people spending more time on the Internet than ever before, holds the potential for developing such a dataset by mining relevant web behavior data from social media communications, which have increased exponentially in the last few years. Twitter, one such social media platform, is highly popular amongst all age groups, who communicate on diverse topics including but not limited to news, current events, politics, emerging technologies, family, relationships, and career opportunities, via tweets, while sharing their views, opinions, perspectives, and feedback towards the same. Therefore, this work presents a dataset of about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. Instructions: This dataset contains about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. The dataset contains only tweet identifiers (Tweet IDs) due to the terms and conditions of Twitter to re-distribute Twitter data only for research purposes. They need to be hydrated to be used. The process of retrieving a tweet's complete information (such as the text of the tweet, username, user ID, date and time, etc.) using its ID is known as the hydration of a tweet ID. The Hydrator application (link to download the application: https://github.com/DocNow/hydrator/releases and link to a step-by-step tutorial: https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets) or any similar application may be used for hydrating this dataset. Data Description This dataset consists of 7 .txt files. The following shows the number of Tweet IDs and the date range (of the associated tweets) in each of these files. Filename: Exoskeleton_TweetIDs_Set1.txt (Number of Tweet IDs – 22945, Date Range of Tweets - July 20, 2021 – May 21, 2022) Filename: Exoskeleton_TweetIDs_Set2.txt (Number of Tweet IDs – 19416, Date Range of Tweets - Dec 1, 2020 – July 19, 2021) Filename: Exoskeleton_TweetIDs_Set3.txt (Number of Tweet IDs – 16673, Date Range of Tweets - April 29, 2020 - Nov 30, 2020) Filename: Exoskeleton_TweetIDs_Set4.txt (Number of Tweet IDs – 16208, Date Range of Tweets - Oct 5, 2019 - Apr 28, 2020) Filename: Exoskeleton_TweetIDs_Set5.txt (Number of Tweet IDs – 17983, Date Range of Tweets - Feb 13, 2019 - Oct 4, 2019) Filename: Exoskeleton_TweetIDs_Set6.txt (Number of Tweet IDs – 34009, Date Range of Tweets - Nov 9, 2017 - Feb 12, 2019) Filename: Exoskeleton_TweetIDs_Set7.txt (Number of Tweet IDs – 11351, Date Range of Tweets - May 21, 2017 - Nov 8, 2017) Here, the last date for May is May 21 as it was the most recent date at the time of data collection. The dataset would be updated soon to incorporate more recent tweets.

The study titled "Effect of CSR on customer loyalty with the mediating role of corporate reputation: the case of commercial banks in Ethiopia" aims to investigate the relationship between Corporate Social Responsibility (CSR) initiatives, corporate reputation, and customer loyalty within the context of commercial banks in Ethiopia. The research is predominantly explanatory in nature, seeking to understand how CSR practices impact customer loyalty through the mediating role of corporate reputation.Data for this study was collected using a structured questionnaire as the data collection instrument. The respondents involved in the study were customers of commercial banks in Ethiopia, with a total sample size of 790 individuals. Purposive sampling techniques were employed to select respondents who have direct experience with the services provided by commercial banks.The analysis of the data was conducted using the AMOS structural equation model in conjunction with SPSS software to test and evaluate the hypotheses formulated in the study. The data description includes detailed information on the demographic characteristics of the respondents, allowing for a comprehensive understanding of the sample profile.Furthermore, the data collected encompasses various dimensions related to CSR initiatives, including economic, legal, ethical, and philanthropic aspects. Additionally, corporate reputation and customer loyalty related data are included to assess the impact of CSR on customer loyalty through the mediating role of corporate reputation. This comprehensive dataset provides valuable insights into the relationships between CSR practices, corporate reputation, and customer loyalty in the context of commercial banks in Ethiopia.

The USDA-Agricultural Research Service carried out a water productivity field trial for irrigated maize (Zea mays L.) at the Limited Irrigation Research Farm (LIRF) facility in northeastern Colorado in 2008 through 2011. The dataset includes daily measurements of irrigation, precipitation, soil water storage, and plant growth; daily estimates of crop evapotranspiration; and seasonal measurement of crop water use and crop yield. Soil parameters and hourly and daily weather data are also provided. The dataset can be useful to validate and refine maize crop models. The data are presented in spreadsheet format. The primary data files are the four annual LIRF Maize 20xx.xlsx files that include the daily water balance and phenology, final yield and biomass data, and crop management logs. Annual LIRF Weather 20xx.xlsx files provide hourly and daily weather parameters including reference evapotranspiration. The LIRF Soils.xlsx file gives soil parameters. Each spreadsheet contains a Data Descriptions worksheet that provides worksheet or column specific information. Comments are embedded in cells with specific information. A LIRF photos.pdf file provides images of the experimental area, measurement processes and crop conditions. Photo credit Peggy Greb, ARS; copyright-free, public domain copyright policy. Resources in this dataset:Resource Title: LIRF Weather 2008. File Name: LIRF Weather 2008.xlsxResource Description: LIRF hourly and daily weather data for 2008Resource Title: LIRF Weather 2009. File Name: LIRF Weather 2009.xlsxResource Description: LIRF hourly and daily weather data for 2009Resource Title: LIRF Weather 2010. File Name: LIRF Weather 2010.xlsxResource Description: LIRF hourly and daily weather data for 2010Resource Title: LIRF Weather 2011. File Name: LIRF Weather 2011.xlsxResource Description: LIRF hourly and daily weather data for 2011Resource Title: LIRF Soils. File Name: LIRF Soils.xlsxResource Description: LIRF soil maps, soil texture, moisture retention, and chemical constituentsResource Title: LIRF Photo Log. File Name: LIRF Photo Log.pdfResource Description: Photos of the LIRF Water Productivity field trials and instrumentation.Resource Title: Data Dictionaries. File Name: DataDictionary r1.xlsxResource Description: Data descriptions of all the data resources (also included in their respective data files).Resource Title: LIRF Methodology. File Name: LIRF Methodology r1.pdfResource Description: Description of data files, data, and data collection methodology for the LIRF 2008-2011 Water Productivity field trials.Resource Title: LIRF Maize 2008. File Name: LIRF Maize 2008 r1.xlsxResource Description: Water balance and yield data for 2008 LIRF field trialResource Title: LIRF Maize 2009. File Name: LIRF Maize 2009 r1.xlsxResource Description: Water balance and yield data for 2009 LIRF field trialResource Title: LIRF Maize 2010. File Name: LIRF Maize 2010 r1.xlsxResource Description: Water balance and yield data for 2010 LIRF field trialResource Title: LIRF Maize 2011. File Name: LIRF Maize 2011 r1.xlsxResource Description: Water balance and yield data for 2011 LIRF field trial

About this dataset

Context

The dataset tabulates the Brownstown population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Brownstown. The dataset can be utilized to understand the population distribution of Brownstown by age. For example, using this dataset, we can identify the largest age group in Brownstown.

Key observations

The largest age group in Brownstown, IN was for the group of age 70-74 years with a population of 384 (12.77%), according to the 2021 American Community Survey. At the same time, the smallest age group in Brownstown, IN was the 80-84 years with a population of 82 (2.73%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Brownstown is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Brownstown total population. 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 Brownstown Population by Age. You can refer the same here

Recent USDA/ARS patented technologies on crop production and protection that are available for licensing are described, including summary, contact, benefits, and applications. Updated June 2018. Resources in this dataset:Resource Title: Crop Production and Protection - Available Technologies, June 2018. File Name: Crop Production and Protection.pptxResource Description: Slides presenting title, contact, docket number(s), description, image, benefits, and applications of each new technology.Resource Title: Patented Technologies Data Dictionary. File Name: patented-technologies-data-dictionary.csvResource Description: Defines fields, data type, allowed values etc. in patented technology tables.Resource Title: Crop Production and Protection - June 2018. File Name: Crop_Production_and_Protection.csvResource Description: Listing of technologies for increased crop productivity, economically and environmentally sustainable methods of crop production, and protection from plant diseases and pests. This CSV file provides the title, technology type, docket number, contact, description, and category for each item. Machine-readable content extracted from corresponding slides accompanying this dataset.

About this dataset

Context

The dataset tabulates the Standard population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Standard. The dataset can be utilized to understand the population distribution of Standard by age. For example, using this dataset, we can identify the largest age group in Standard.

Key observations

The largest age group in Standard, IL was for the group of age 85+ years with a population of 27 (9.68%), according to the 2021 American Community Survey. At the same time, the smallest age group in Standard, IL was the 70-74 years with a population of 1 (0.36%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Standard is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Standard total population. 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 Standard Population by Age. You can refer the same here

Note: this dataset is replication data for the paper: Kampen, A., Pearson, M., & Smit, M. (2016). Digital Tools and Techniques in Scholarship and Pedagogy in the Social Sciences and Humanities. Technical Report, Dalhousie University. This data is about the adoption, diffusion and use of digital tools and techniques within the social sciences and humanities research communities (Kampen, Pearson & Smit, 2016). The dataset includes a weighted random sampling of 1001 articles (500 from the Social Sciences subject area; 501 from the Arts and Humanities subject area) from original research articles published in academic journals in 2014. These articles were assessed individually for the presence, and nature, of digital tools and techniques used in the research process, particularly collection, analysis, and/or visualization. Each article was also assessed to determine if it belonged to one of the three focus areas identified by the funding agency: Diversity/Inequality/Differences, Environmental studies, and Resilient and innovative societies. Tabular data was recorded based on these assessments; a complete key is included in the Readme file. The tabular data and the bibliography for the sampled articles are included. Data consists of: 1) One tabular data file (CSV) containing, for each citation: - Numeric citation identifier - Metadata (e.g., title, subject areas, granting information) - Digital tools analysis (e.g., Type of digital tool used, name of digital tool, source of digital tool) - Research notes 2) One BibTeX file containing citations for the 1001 articles analyzed (citation identifier matched to CSV id) 3) One PDF file containing the output of the BibTeX file 4) One Readme file containing data description, cleaning techniques, and known remaining issues

The Social Media Profile Description Tools market has evolved significantly in recent years, emerging as an essential component for businesses and individuals striving to enhance their online presence. These tools enable users to craft impactful, concise, and optimized profiles that effectively convey their brand me

The WIC Infant and Toddler Feeding Practices Study–2 (WIC ITFPS-2) (also known as the “Feeding My Baby Study”) is a national, longitudinal study that captures data on caregivers and their children who participated in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) around the time of the child’s birth. The study addresses a series of research questions regarding feeding practices, the effect of WIC services on those practices, and the health and nutrition outcomes of children on WIC. Additionally, the study assesses changes in behaviors and trends that may have occurred over the past 20 years by comparing findings to the WIC Infant Feeding Practices Study–1 (WIC IFPS-1), the last major study of the diets of infants on WIC. This longitudinal cohort study has generated a series of reports. These datasets include data from caregivers and their children during the prenatal period and during the children’s first five years of life (child ages 1 to 60 months). A full description of the study design and data collection methods can be found in Chapter 1 of the Second Year Report (https://www.fns.usda.gov/wic/wic-infant-and-toddler-feeding-practices-st...). A full description of the sampling and weighting procedures can be found in Appendix B-1 of the Fourth Year Report (https://fns-prod.azureedge.net/sites/default/files/resource-files/WIC-IT...). Processing methods and equipment used Data in this dataset were primarily collected via telephone interview with caregivers. Children’s length/height and weight data were objectively collected while at the WIC clinic or during visits with healthcare providers. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. Study date(s) and duration Data collection occurred between 2013 and 2019. Study spatial scale (size of replicates and spatial scale of study area) Respondents were primarily the caregivers of children who received WIC services around the time of the child’s birth. Data were collected from 80 WIC sites across 27 State agencies. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) This dataset includes sampling weights that can be applied to produce national estimates. A full description of the sampling and weighting procedures can be found in Appendix B-1 of the Fourth Year Report (https://fns-prod.azureedge.net/sites/default/files/resource-files/WIC-IT...). Level of subsampling (number and repeat or within-replicate sampling) A full description of the sampling and weighting procedures can be found in Appendix B-1 of the Fourth Year Report (https://fns-prod.azureedge.net/sites/default/files/resource-files/WIC-IT...). Study design (before–after, control–impacts, time series, before–after-control–impacts) Longitudinal cohort study. Description of any data manipulation, modeling, or statistical analysis undertaken Each entry in the dataset contains caregiver-level responses to telephone interviews. Also available in the dataset are children’s length/height and weight data, which were objectively collected while at the WIC clinic or during visits with healthcare providers. In addition, the file contains derived variables used for analytic purposes. The file also includes weights created to produce national estimates. The dataset does not include any personally-identifiable information for the study children and/or for individuals who completed the telephone interviews. Description of any gaps in the data or other limiting factors Please refer to the series of annual WIC ITFPS-2 reports (https://www.fns.usda.gov/wic/infant-and-toddler-feeding-practices-study-2-fourth-year-report) for detailed explanations of the study’s limitations. Outcome measurement methods and equipment used The majority of outcomes were measured via telephone interviews with children’s caregivers. Dietary intake was assessed using the USDA Automated Multiple Pass Method (https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-h...). Children’s length/height and weight data were objectively collected while at the WIC clinic or during visits with healthcare providers. Resources in this dataset:Resource Title: ITFP2 Year 5 Enroll to 60 Months Public Use Data CSV. File Name: itfps2_enrollto60m_publicuse.csvResource Description: ITFP2 Year 5 Enroll to 60 Months Public Use Data CSVResource Title: ITFP2 Year 5 Enroll to 60 Months Public Use Data Codebook. File Name: ITFPS2_EnrollTo60m_PUF_Codebook.pdfResource Description: ITFP2 Year 5 Enroll to 60 Months Public Use Data CodebookResource Title: ITFP2 Year 5 Enroll to 60 Months Public Use Data SAS SPSS STATA R Data. File Name: ITFP@_Year5_Enroll60_SAS_SPSS_STATA_R.zipResource Description: ITFP2 Year 5 Enroll to 60 Months Public Use Data SAS SPSS STATA R DataResource Title: ITFP2 Year 5 Ana to 60 Months Public Use Data CSV. File Name: ampm_1to60_ana_publicuse.csvResource Description: ITFP2 Year 5 Ana to 60 Months Public Use Data CSVResource Title: ITFP2 Year 5 Tot to 60 Months Public Use Data Codebook. File Name: AMPM_1to60_Tot Codebook.pdfResource Description: ITFP2 Year 5 Tot to 60 Months Public Use Data CodebookResource Title: ITFP2 Year 5 Ana to 60 Months Public Use Data Codebook. File Name: AMPM_1to60_Ana Codebook.pdfResource Description: ITFP2 Year 5 Ana to 60 Months Public Use Data CodebookResource Title: ITFP2 Year 5 Ana to 60 Months Public Use Data SAS SPSS STATA R Data. File Name: ITFP@_Year5_Ana_60_SAS_SPSS_STATA_R.zipResource Description: ITFP2 Year 5 Ana to 60 Months Public Use Data SAS SPSS STATA R DataResource Title: ITFP2 Year 5 Tot to 60 Months Public Use Data CSV. File Name: ampm_1to60_tot_publicuse.csvResource Description: ITFP2 Year 5 Tot to 60 Months Public Use Data CSVResource Title: ITFP2 Year 5 Tot to 60 Months Public Use SAS SPSS STATA R Data. File Name: ITFP@_Year5_Tot_60_SAS_SPSS_STATA_R.zipResource Description: ITFP2 Year 5 Tot to 60 Months Public Use SAS SPSS STATA R DataResource Title: ITFP2 Year 5 Food Group to 60 Months Public Use Data CSV. File Name: ampm_foodgroup_1to60m_publicuse.csvResource Description: ITFP2 Year 5 Food Group to 60 Months Public Use Data CSVResource Title: ITFP2 Year 5 Food Group to 60 Months Public Use Data Codebook. File Name: AMPM_FoodGroup_1to60m_Codebook.pdfResource Description: ITFP2 Year 5 Food Group to 60 Months Public Use Data CodebookResource Title: ITFP2 Year 5 Food Group to 60 Months Public Use SAS SPSS STATA R Data. File Name: ITFP@_Year5_Foodgroup_60_SAS_SPSS_STATA_R.zipResource Title: WIC Infant and Toddler Feeding Practices Study-2 Data File Training Manual. File Name: WIC_ITFPS-2_DataFileTrainingManual.pdf

Data Description: This study is based on a dataset of 143 peer-reviewed articles published between 2012 and 2025, focusing on the relationship between carbon disclosure and corporate value. The bibliometric data were retrieved from Web of Science Core Collection, using a comprehensive search strategy that incorporated relevant keywords such as “carbon disclosure”, “carbon reporting”, “corporate value”, “firm performance”, and “sustainability”. Data analysis was conducted using CiteSpace, a widely used bibliometric visualization tool. Through co-citation, keyword co-occurrence, and clustering analyses, the study reveals the knowledge structure and emerging trends in the literature. The dataset includes information on: Publication year (2012–2025) Authors and affiliations Journals and citations Keywords and research themes Collaboration networks (institutional and regional) The final dataset was cleaned and validated to remove duplicates and ensure the accuracy of bibliometric indicators.

Data Set from the Online Survey of the Research Data Alliance's Discipline-Specific Guidance for Data Management Plans Working Group.

The data was collected from November 8, 2021 to January 14, 2022.

The survey was divided into the following areas after a brief introduction on "Purpose of this survey" and "Use of the information you provide."

• Demographics
• Data Description and Collection
• Data Documentation & Quality
• Data Archiving, Publishing & Sharing After the Project
• Guidelines, Principles & Best Practices
• Follow-up interviews and group discussions

The analysis of the online survey was focused on the four areas: Natural Sciences, Life Sciences, Humanities & Social Sciences, and Engineering. The results of the evaluation will be presented in a separate publication.

In addition to the data, the variables and values are also published here.

The online survey questions can be accessed here: https://doi.org/10.5281/zenodo.7443373

The data were evaluated using R. The code is published under the following DOI: 10.5281/zenodo.7443467

A more detailed analysis and description can be found in the paper "Discipline-specific Aspects in Data Management Planning" submitted to Data Science Journal (2022-12-15).

About this dataset

Context

The dataset tabulates the Red Cross population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Red Cross. The dataset can be utilized to understand the population distribution of Red Cross by age. For example, using this dataset, we can identify the largest age group in Red Cross.

Key observations

The largest age group in Red Cross, NC was for the group of age 70-74 years with a population of 72 (10.50%), according to the 2021 American Community Survey. At the same time, the smallest age group in Red Cross, NC was the 5-9 years with a population of 10 (1.46%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Red Cross is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Red Cross total population. 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 Red Cross Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the New Market population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for New Market. The dataset can be utilized to understand the population distribution of New Market by age. For example, using this dataset, we can identify the largest age group in New Market.

Key observations

The largest age group in New Market, IN was for the group of age 70 to 74 years years with a population of 80 (14.71%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in New Market, IN was the Under 5 years years with a population of 7 (1.29%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the New Market is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of New Market total population. 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 New Market Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Mill Creek population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Mill Creek. The dataset can be utilized to understand the population distribution of Mill Creek by age. For example, using this dataset, we can identify the largest age group in Mill Creek.

Key observations

The largest age group in Mill Creek, PA was for the group of age 60 to 64 years years with a population of 32 (11.03%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Mill Creek, PA was the 75 to 79 years years with a population of 5 (1.72%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Mill Creek is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Mill Creek total population. 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 Mill Creek Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Big Spring population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Big Spring. The dataset can be utilized to understand the population distribution of Big Spring by age. For example, using this dataset, we can identify the largest age group in Big Spring.

Key observations

The largest age group in Big Spring, TX was for the group of age 35-39 years with a population of 2,222 (8.48%), according to the 2021 American Community Survey. At the same time, the smallest age group in Big Spring, TX was the 80-84 years with a population of 286 (1.09%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Big Spring is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Big Spring total population. 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 Big Spring Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Butler population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Butler. The dataset can be utilized to understand the population distribution of Butler by age. For example, using this dataset, we can identify the largest age group in Butler.

Key observations

The largest age group in Butler, IN was for the group of age 5-9 years with a population of 282 (9.78%), according to the 2021 American Community Survey. At the same time, the smallest age group in Butler, IN was the 85+ years with a population of 53 (1.84%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Butler is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Butler total population. 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 Butler Population by Age. You can refer the same here