This CSV Template for Altmetric Customers - Publications is part of the CSV Primer Toolkit - a resource to help you decide whether to use CSV files as an integration method of your data within the Altmetric Explorer. Please do download to access the data dictionary as well.

The Zip file contains the following CSV files:

• Visitors per year_1.csv
• Visitors per month — individual period_1.csv
• Visitors per month_1.csv
• Visitors per hour_1.csv
• Visitors per day_1.csv
• Visitors_1.csv
• Browser main versions by operating system_1.csv
• Browser by operating system subversions_1.csv
• Browser subversions_1.csv
• Entry page_1.csv
• Overview of origin_1.csv
• Usage per domain_1.csv
• Page impressions per page_1.csv
• Search word statistics phrases by search engine_1.csv
• Search word statistics Phrasen_1.csv
• Search word statistics words_1.csv

Dataset: AE2207 Unprocessed CTD

PLEASE NOTE: This record has been retired. It is superseded by 'Shoreline Management Plan Mapping': https://environment.data.gov.uk/dataset/8e383070-d465-11e4-b752-f0def148f590

Shoreline Management Plan Explorer (SMP Explorer) is an online digital and map based system to hold the details of shoreline management plans around the coast of England. SMP-Explorer was developed by the Environment Agency on behalf of Coastal Groups. Data is published as linked data but data on action plans within each Shoreline Management Plan (SMP) is available to download in CSV format.

SMP Explorer contains hyperlinks to websites operated by other parties. We do not control such websites and we take no responsibility for, and will not incur any liability in respect of, their content. Our inclusion of hyperlinks to such websites does not imply any endorsement of views, statements or information contained in such websites.

RP-commenting-practices-multiple-sources Replication package for the paper "What do Developers Discuss about Code Comments?" ## Structure Appendix.pdf Tags-topics.md Stack-exchange-query.md RQ1/ LDA_input/ combined-so-quora-mallet-metadata.csv topic-input.mallet LDA_output/ Mallet/ output_csv/ docs-in-topics.csv topic-words.csv topics-in-docs.csv topics-metadata.csv output_html/ all_topics.html Docs/ Topics/ RQ2/ datasource_rawdata/ quora.csv stackoverflow.csv manual_analysis_output/ stackoverflow_quora_taxonomy.xlsx ## Contents of the Replication Package --- - Appendix.pdf- Appendix of the paper containing supplement tables - Tags-topics.md tags selected from Stack overflow and topics selected from Quora for the study (RQ1 & RQ2) - Stack-exchange-query.md the query interface used to extract the posts from stack exchnage explorer. - RQ1/ - contains the data used to answer RQ1 - LDA_input/ - input data used for LDA analysis - combined-so-quora-mallet-metadata.csv - Stack overflow and Quora questions used to perform LDA analysis - topic-input.mallet - input file to the mallet tool - LDA_output/ - Mallet/ - contains the LDA output generated by MALLET tool - output_csv/ - docs-in-topics.csv - documents per topic - topic-words.csv - most relevant topic words - topics-in-docs.csv - topic probability per document - topics-metadata.csv - metadata per document and topic probability - output_html/ - Browsable results of mallet output - all_topics.html - Docs/ - Topics/ - RQ2/ - contains the data used to answer RQ2 - datasource_rawdata/ - contains the raw data for each source - quora.csv - contains the processed dataset (like removing html tags). To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using Makar tool. - stackoverflow.csv - contains the processed stackoverflow dataset. To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using Makar tool. - manual_analysis_output/ - stackoverflow_quora_taxonomy.xlsx - contains the classified dataset of stackoverflow and quora and description of taxonomy. - Taxonomy - contains the description of the first dimension and second dimension categories. Second dimension categories are further divided into levels, separated by | symbol. - stackoverflow-posts - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories. - quota-posts - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories. ---

Analysis of ‘Access statistics from moers.de for May 2015 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/8175bf5f-0be1-488b-8d1a-d602b075694d on 14 January 2022.

--- Dataset description provided by original source is as follows ---

The Zip file contains the following CSV files:

• Visitors per year_1.csv
• Visitors per month — individual period_1.csv
• Visitors per month_1.csv
• Visitors per hour_1.csv
• Visitors per day_1.csv
• Visitors_1.csv
• Browser main versions by operating system_1.csv
• Browser by operating system subversions_1.csv
• Browser subversions_1.csv
• Access pages_1.csv
• Statement of origin_1.csv
• Use per domain_1.csv
• PAGE Impression per page_1.csv
• Search word statistics Phrasen by search engine_1.csv
• Search word statistics Phrasen_1.csv
• Search word statistics words_1.csv

--- Original source retains full ownership of the source dataset ---

The Pacific Community Results Report highlights the results achieved by SPC with our 26 Member countries and territories, and development partners. This dataset provides the data used in the Results Report provided in Excel and CSV formats.

This data has been visualised in the Results Explorer Dashboard: https://pacificdata.org/results-explorer

This database consists of two CSV files resulting from an iterative collection of data from 80 channels on the Odysee platform. Created on September 28, 2020, by American libertarian Jeremy Kauffman, the platform defines itself on its website as a repository for a wide range of content, although it is "mainly recognized for its video hosting capabilities." Unlike YouTube, Odysee uses a peer-to-peer data exchange (a client-server architecture), which allows it to avoid national jurisdictions imposed by the geographically restricted use of centralized servers. This configuration allows the platform to escape any form of moderation, making it a privileged digital space for communities with the most extreme positions.

The CSV files were obtained using the Odysee platform API to retrieve data and metadata related to the creation of channels and the publication of listed users. An initial list of 80 channels allowed the identification of a new grouping of channels, whose data and metadata were also collected. Odysee indeed allows video sharing, creating a relationship between a source channel and a recipient channel, similar to shares on Twitter. This process is repeated automatically five times, across different levels of depth, to collect each identified grouping and expand the total number of channels through the effect of shared videos. Thus, the fifth depth level allows for an increase from 80 channels and 38,906 videos to a total sample of 13,436 channels, 4,937,385 videos and documents, and 516,810 shares.

A first CSV file of 6.9 MB lists the data and metadata related to the channels strictly speaking, across different categories: channel ID, creation date, number of subscribers, description (if present), a link to the profile picture, a link to the cover image, and the channel's nickname. A second CSV file of 6.5 GB concerns the data and metadata of published content, including the canonical URL, the type of content (videos or others, such as PDF files), the video identification code (claim_id), the creation date chosen by the user, the title, the number of views, the channel ID and name, the video duration, the language chosen by the user, tags, likes, dislikes, the actual date (retrieved by scraping the publication dates on the site https://explorer.lbry.com), and the date formatted without the hours.

The data is multilingual, while the initial list of channels included 80 conspiracy channels, affiliated with the QAnon movement, in French, German, and Italian. Gradually, over the course of iterative collection, Spanish, Russian, and other channels and content also appeared.

This database, the first ever dedicated to Odysee, aims to enable researchers to explore the new possibilities offered by this platform, as the APIs of other traditional platforms are no longer accessible. The possibilities are numerous for studying social networks, politicized or extreme communities, online interactions between users, or audiovisual corpora.

Raw results from the regulome explorer analysis for LSD1 and DNMT1. (CSV 36Â kb)

NTIA has made public use datasets available for every CPS Computer and Internet Use Supplement, as well as the Analyze Table of summary statistics used in the Data Explorer. All datasets are available in Stata, CSV, and raw/fixed formats, and are accompanied by official documentation produced by the Census Bureau (with the exception of the Analyze Table, with was created by NTIA and accompanied by documentation written by NTIA).

Solar Wind Omni and SAMPEX ( Solar Anomalous and Magnetospheric Particle Explorer) datasets used in examples for SEAnorm, a time normalized superposed epoch analysis package in python.

Both data sets are stored as either a HDF5 or a compressed csv file (csv.bz2) which contain a Pandas DataFrame of either the Solar Wind Omni and SAMPEX data sets. The data sets where written with pandas.DataFrame.to_hdf() and pandas.DataFrame.to_csv() using a compression level of 9. The DataFrames can be read using pandas.DataFrame.read_hdf( ) or pandas.DataFrame.read_csv( ) depending on the file format.

The Solar Wind Omni data sets contains solar wind velocity (V) and dynamic pressure (P), the southward interplanetary magnetic field in Geocentric Solar Ecliptic System (GSE) coordinates (B_Z_GSE), the auroral electrojet index (AE), and the Sym-H index all at 1 minute cadence.

The SAMPEX data set contains electron flux from the Proton/Electron Telescope (PET) at two energy channels 1.5-6.0 MeV (ELO) and 2.5-14 MeV (EHI) at an approximate 6 second cadence.

Static timetables, stop locations, and route shape information in General Transit Feed Specification (GTFS) format for all operators, including regional, trackwork and transport routes not available in realtime feeds. Returns ZIP file containing CSV files Please note: due to the large file size, the API explorer will not work for this resource, ie. 'EXPLORE API' function. To use this dataset please download the zip file using the 'DOWNLOAD' button below or use cURL to get directly. TfNSW GTFS Pathways extension as part of the GTFS Timetables Complete bundle released 2 June 2023.

Die Zip-Datei enthält nachfolgende CSV-Dateien: Besucher pro Jahr_1.csv Besucher pro Monat - individueller Zeitraum_1.csv Besucher pro Monat_1.csv Besucher pro Stunde_1.csv Besucher pro Tag_1.csv Besucher_1.csv Browser Hauptversionen nach Betriebssystem_1.csv Browser nach Betriebssystem Unterversionen_1.csv Browser Unterversionen_1.csv

Die Zip-Datei enthält nachfolgende CSV-Dateien: Besucher pro Jahr_1.csv Besucher pro Monat - individueller Zeitraum_1.csv Besucher pro Monat_1.csv Besucher pro Stunde_1.csv Besucher pro Tag_1.csv Besucher_1.csv Browser Hauptversionen nach Betriebssystem_1.csv Browser nach Betriebssystem Unterversionen_1.csv Browser Unterversionen_1.csv

Die Zip-Datei enthält nachfolgende CSV-Dateien: Besucher pro Jahr_1.csv Besucher pro Monat - individueller Zeitraum_1.csv Besucher pro Monat_1.csv Besucher pro Stunde_1.csv Besucher pro Tag_1.csv Besucher_1.csv Browser Hauptversionen nach Betriebssystem_1.csv Browser nach Betriebssystem Unterversionen_1.csv Browser Unterversionen_1.csv

Die Zip-Datei enthält nachfolgende CSV-Dateien: Besucher pro Jahr_1.csv Besucher pro Monat - individueller Zeitraum_1.csv Besucher pro Monat_1.csv Besucher pro Stunde_1.csv Besucher pro Tag_1.csv Besucher_1.csv Browser Hauptversionen nach Betriebssystem_1.csv Browser nach Betriebssystem Unterversionen_1.csv Browser Unterversionen_1.csv