2020 Massachusetts Motor Vehicle Citations Data.

This dashboard allows an interactive experience. Users will be able to filter out only relevant information, such as county, watershed, watershed type, survey's conducted, and EPA watershed violations...this is a work in progress...CAFO information needs to be added to this dashboard.

Blockchain data dashboard: SB quote program

Use the calling bull course to introduce students to data, ethics, visualization, and R.

In this module the students will learn some basic concepts in statistical thinking about data, with emphasis on exploratory data analysis.

These datasets and R script are to generate the visualizations used in "Search Engine Manipulation: SEO to Spread Kremlin-Aligned Disinformation". Data were collected from the Ahrefs (ahrefs.com) dashboard on Sep 22, 2022. Any re-use of this data must cite Ahrefs as the source. Data originally posted to Kilthub: https://figshare.com/articles/dataset/Search_Engine_Manipulation_to_Spread_Pro-Kremlin_Propaganda/21936030

This repository contains data on 17,419 DOIs cited in the IPCC Working Group 2 contribution to the Sixth Assessment Report, and the code to link them to the dataset built at the Curtin Open Knowledge Initiative (COKI).

References were extracted from the report's PDFs (downloaded 2022-03-01) via Scholarcy and exported as RIS and BibTeX files. DOI strings were identified from RIS files by pattern matching and saved as CSV file. The list of DOIs for each chapter and cross chapter paper was processed using a custom Python script to generate a pandas DataFrame which was saved as CSV file and uploaded to Google Big Query.

We used the main object table of the Academic Observatory, which combines information from Crossref, Unpaywall, Microsoft Academic, Open Citations, the Research Organization Registry and Geonames to enrich the DOIs with bibliographic information, affiliations, and open access status. A custom query was used to join and format the data and the resulting table was visualised in a Google DataStudio dashboard.

This version of the repository also includes the set of DOIs from references in the IPCC Working Group 1 contribution to the Sixth Assessment Report as extracted by Alexis-Michel Mugabushaka and shared on Zenodo: https://doi.org/10.5281/zenodo.5475442 (CC-BY)

A brief descriptive analysis was provided as a blogpost on the COKI website.

The repository contains the following content:

Data:

data/scholarcy/RIS/ - extracted references as RIS files

data/scholarcy/BibTeX/ - extracted references as BibTeX files

IPCC_AR6_WGII_dois.csv - list of DOIs

data/10.5281_zenodo.5475442/ - references from IPCC AR6 WG1 report

Processing:

preprocessing.R - preprocessing steps for identifying and cleaning DOIs

process.py - Python script for transforming data and linking to COKI data through Google Big Query

Outcomes:

Dataset on BigQuery - requires a google account for access and bigquery account for querying

Data Studio Dashboard - interactive analysis of the generated data

Zotero library of references extracted via Scholarcy

PDF version of blogpost

Note on licenses: Data are made available under CC0 (with the exception of WG1 reference data, which have been shared under CC-BY 4.0) Code is made available under Apache License 2.0

What's in this dataset

We hope that this dataset will prove useful to answer questions pertaining to "What do we know about non-pharmaceutical interventions?". This is a machine-readable dataset related to socioeconomic factors that may affect the spread and/or consequences of epidemiological outbreaks of the novel coronavirus (COVID-19). This is combined with timeseries of the infections and deaths from 1/22 to now and the foot traffic at points of interest of different types, aggregated at the county level. By combining these, we want to measure whether NPIs work differently in different counties, and whether their effects can be predicted by county-specific traits. This dataset is envisioned to serve the data science, machine learning, and epidemiological modeling communities.

We collected the data set from a variety of sources. In the interest of not cluttering this dataset, we only included the data after it has been processed into a machine readable format. Please see the raw data with the full acknowledgements to our sources at our Github page. https://github.com/JieYingWu/COVID-19_US_County-level_Summaries

ArXiv report on dataset: http://arxiv.org/abs/2004.00756

Acknowledgements

Thank you to all our sources, especially the JHU CSSE COVID-19 Dashboard for making their data public and SafeGraph, for providing researchers their data for COVID-19 related work.

Inspiration

Using this dataset, we hope to promote better understanding of how diseases spread differently in different communities, as well as how policies to limit a disease's spread will impact different communities. We hope that this can inform policy makers to enact interventions that are effective for each county.

Citation

If you find this dataset useful, please consider citing our paper: latex @article{killeenCountylevelDatasetInforming2020, title = {A {{County}}-Level {{Dataset}} for {{Informing}} the {{United States}}' {{Response}} to {{COVID}}-19}, author = {Killeen, Benjamin D. and Wu, Jie Ying and Shah, Kinjal and Zapaishchykova, Anna and Nikutta, Philipp and Tamhane, Aniruddha and Chakraborty, Shreya and Wei, Jinchi and Gao, Tiger and Thies, Mareike and Unberath, Mathias}, year = {2020}, month = apr, }

This is a series of research modules designed to both teach beginning R users how to work with data and lead undergraduates through research by constructing social-ecological-agricultural case studies of individual counties.

BridgeDb ID mapping database for metabolites, using HMDB 3.6 (26 August 2017), ChEBI 154, and Wikidata (26 August 2017) as data sources. Two significant changes: Mappings to the EPA CompTox Dashboard have been added (about 36 thousand) and it is using a newer HMDB 3.6 version with many more compounds. If you experience problems, please report on the project page. See the attached QC for more details on the changes.If you use this data in your research, please cite that data set, and the BridgeDb, ChEBI, and HMDB articles.

DAO-Analyzer is a web dashboard that shows the state and evolution of DAOs.

This is the dataset used by that dashboard.

⚠️ Please see the "Citing" section below if you want to use this dataset

Which DAOs does DAO-Analyzer monitor?

DAO-analyzer monitors, so far, the DAOs from the following platforms: DAOhaus, Aragon and Daostack. These platforms facilitate the deployment of a DAO in a blockchain and the interaction of the DAO members with the DAO.

While each DAO platform provides different ruling mechanisms for DAOs, they all essentially provide mechanisms for voting and for the allocation of cryptofunds.

The DAOs that we monitor are running on public blockchains. Mainly, in the Ethereum mainnet, that is, the primary public Ethereum blockchain network. However, in recent times, DAO platforms make it possible to deploy and operate a DAO in other chains, such as xDai or Polygon, that are designed to address Ethereum mainnet issues like slow transactions, high fees and throughput problems. DAO-Analyzer also monitors the DAOs in such networks.

How does DAO-Analyzer get the data?

DAO-Analyzer retrieves the data from the different blockchains using The Graph, an indexing protocol for querying decentralized networks such as Ethereum, xDai, Polygon, etc. Using this protocol, we get the public data stored on the blockchain about each DAO.

In the blockchain, there is a record of every action made by the DAO software (remember that a blockchain can be viewed as a decentralized database). Thus, we use the The Graph protocol to query the blockchain and retrieve information about the DAO: membership, assets, voting, etc.

Acknowledgments

We are researchers of the GRASIA research group of Universidad Complutense de Madrid.

In particular, DAO-Analyzer is created under the umbrella of multiple research projects: - Chain Community, funded by the Spanish Ministry of Science and Innovation (RTI2018‐096820‐A‐I00) and led by Javier Arroyo and Samer Hassan - P2P Models, funded by the European Research Council (ERC-2017-STG 625 grant no.: 75920), led by Samer Hassan. - DAOapplications, funded by the Spanish Ministry of Science and Innovation (PID2021-127956OB-I00) and led by Javier Arroyo, Samer Hassan and maria Cruz Valiente

The programmers of this project were formerly Youssef El Faqir El Rhazoui and currently David Davó Laviña, Elena Martínez Vicente is in charge of the UI/UX and Javier Arroyo leads the development of the product.

DAO-Analyzer is free open source software and we develop it in the open. You can have a look at the code on Github

Cite as

Arroyo, Javier, Davó, David, & Faqir-Rhazoui, Youssef. (2023). DAO Analyzer dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7669709