Report Contributor Page - a list of Customer Service Report Contributors.

CRediT (Contributor Roles Taxonomy) is high-level taxonomy, including 14 roles, that can be used to represent the roles typically played by contributors to scientific scholarly output. The roles describe each contributor’s specific contribution to the scholarly output.

About this notebookThis notebook was created using a helper script: Base.ipynb. This script has some helper functions that push output directly to Datawrapper to generate the graphs that have been included in the opnion piece. To run without the helper functions and bigquery alone use!pip install google-cloud-bigquerythen add:from google.cloud.bigquery import magicsproject_id = "your_project" # update as neededmagics.context.project = project_idbq_params = {}client = bigquery.Client(project=project_id)%load_ext google.cloud.bigqueryfinally, comment out the make_chart lines.### About dimensions-ai-integrity.ds_dp_pipeline_ripeta_staging.trust_markers_rawdimensions-ai-integrity.ds_dp_pipeline_ripeta_staging.trust_markers_raw is an internal table that is the result of runing a process over the text of publications in order to identify trustmarker segments including authors contributions.The process works as follows:The process aims to automatically segment research papers into their constituent sections. It operates by identifying headings within the text based on a pre-defined set of patterns and a rule-based system. The system first cleans and normalizes the input text. It then employs regular expressions to detect potential section headings. These potential headings are validated against a set of rules that consider factors such as capitalization, the context of surrounding words, and the typical order of sections within a research paper (e.g., certain sections not appearing after "References" or before "Abstract"). Specific rules also handle exceptions for particular heading types like "Keywords" or "Appendices." Once valid headings are identified, the system extracts the corresponding textual content for each section. The output is a structured representation of the paper, categorizing text segments under their respective heading types. Any text that doesn't fall under a recognized heading is also identified as unlabeled content. The overall process aims to provide a structured understanding of the document's organization for subsequent analysis.Author Contributions segments are identified using the following regex:"author_contributions": ["((credit|descript(ion(?:s)?|ive)| )*author(s|'s|ship|s')?( |contribution(?:s)?|statement(?:s)?|role(?:s)?){2,})","contribution(?:s)"]Access to dimensions-ai-integrity.ds_dp_pipeline_ripeta_staging.trust_markers_raw is available to peer reveiwers of the opinion piece.Datasets that allow external validation of the credit ontology process identification process have also been produced.

ESS-DIVE’s (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) dataset metadata reporting format is intended to compile information about a dataset (e.g., title, description, funding sources) that can enable reuse of data submitted to the ESS-DIVE data repository. The files contained in this dataset include instructions (dataset_metadata_guide.md and README.md) that can be used to understand the types of metadata ESS-DIVE collects. The data dictionary (dd.csv) follows ESS-DIVE’s file-level metadata reporting format and includes brief descriptions about each element of the dataset metadata reporting format. This dataset also includes a terminology crosswalk (dataset_metadata_crosswalk.csv) that shows how ESS-DIVE’s metadata reporting format maps onto other existing metadata standards and reporting formats. Data contributors to ESS-DIVE can provide this metadata by manual entry using a web form or programmatically via ESS-DIVE’s API (Application Programming Interface). A metadata template (dataset_metadata_template.docx or dataset_metadata_template.pdf) can be used to collaboratively compile metadata before providing it to ESS-DIVE. Since being incorporated into ESS-DIVE’s data submission user interface, ESS-DIVE’s dataset metadata reporting format, has enabled features like automated metadata quality checks, and dissemination of ESS-DIVE datasets onto other data platforms including Google Dataset Search and DataCite.

Contributors (updated description 2/17/2023)

Pub Date: Date of publication of paper DOI: Digital object identifier of paper Journal: Journal name CRediT: Contribution made on paper Author Name: Name of the author

MDOD Contributors (description updated 2/17/2023)

Distribution of data-contributors’ characteristics and influence on compliance for the NL and NAM cohorts.

Dataset

This dataset was created by Arya Setia Pratama

Released under Other (specified in description)

Contents

Under San Francisco Campaign & Governmental Conduct Code Section 3.216(d) elected officers are required to file the Gifts of Travel form before accepting a gift of transportation, lodging, or subsistence for any out-of-state travel that is paid for in part by an individual or entity other than the City and County of San Francisco, another governmental body, or a bona-fide educational institution as defined in section 203 of the Revenue and Taxation Code, or that is paid for by the City in whole or in part with funds donated by any of those individuals or entities. An elected officer who reimburses an individual or entity for a gift of transportation, lodging or subsistence related to out-of-state travel must also file this form within 30 days of the reimbursement. For more information visit www.sfethics.org. There are two sub-tables of information on the Gifts of Travel form: Accompanying persons and Contributors. The parent Gifts of Travel form dataset ( https://data.sfgov.org/City-Management-and-Ethics/Gifts-Of-Travel/snqf-y894 ) must be joined with this Contributors dataset to view the contributors who contributed more than $500 to the entity funding the trip and whose contributions were used in whole or in part to fund the trip. Join the this dataset with the parent dataset using the DocuSignID.

论文中包含的原始数据

Connection to contributors in the WOUDC Data Registry Search Index.

Characteristics of contributors and non contributors of social security

MIA contributors to the customer service annual report.

A listing of campaign contributions for candidates for City office during the 2013 election cycle

About the Dataset

A dataset containing the List of top Organizations on Github and their contributors.

Tasks

Primarily Intended for Social Network analysis. Check out the Starter Notebook for ref.

Comparison of metadata schema for ORCID, DataCite, Dublin Core, CASRAI, MODS and DDI regarding contributors, organizations and artefacts.

Brazil Social Security: Revenue: Individual Contributor data was reported at 821,691,176.000 BRL in Jun 2018. This records a decrease from the previous number of 842,305,623.000 BRL for May 2018. Brazil Social Security: Revenue: Individual Contributor data is updated monthly, averaging 787,256,993.500 BRL from Jan 2008 (Median) to Jun 2018, with 126 observations. The data reached an all-time high of 1,163,675,301.000 BRL in Dec 2014 and a record low of 410,011,519.000 BRL in Jan 2008. Brazil Social Security: Revenue: Individual Contributor data remains active status in CEIC and is reported by Ministry of Labor and Social Security. The data is categorized under Global Database’s Brazil – Table BR.GBE001: Social Security: Summary.

The data have been produced as part of the HAL/LO research project on the contributors of the HAL repository. (1) Background: The 2002 Budapest Open Access Initiative recommended on self-archiving of scientific articles in open repositories as the “green road” to open access. Twenty years later, only one part of the researchers deposits their publications in open repositories; moreover, one part of the repositories’ content is not based on self-archived deposits but on mediated nonfaculty contributions. The purpose of the research is to provide more empirical evidence on this situation and to assess the impact on the future of the green road. (2) Methods: We analyzed the contributions on the French national HAL repository from more than 1,000 laboratories affiliated to the ten most important French research universities, with a focus on 2020, representing 14,023 contributor accounts and 166,939 deposits. (3) Results: We identified seven different types of contributor accounts, including deposits from nonfaculty staff and import flows from other platforms. Mediated nonfaculty contribution accounts for at least 48% of the deposits. We also identified difference between institutions and disciplines. (4) Conclusions: Our empirical results reveal a transformation of open repositories from self-archiving and direct scientific communication towards research information management. Repositories like HAL are somewhere in the middle of the process.

Maryland Dept. of Labor Contributors (description updated 3/10/2023)