What This Output IsThis is a CSV file containing text and metadata of 3,440 Tweets publicly published from the Twitter account @realdonaldtrump between 25/02/2016 and 21/01/2017.Column D indicates GMT and column E Eastern Time (Washington, DC).Columns in this spreadsheet areid_strfrom_user text created_at time geo_coordinates user_langfrom_user_id_str source profile_image_url user_followers_count user_friends_count status_url entities_strData fromin_reply_to_user_id_str in_reply_to_screen_name in_reply_to_status_id_strhas not been included.Methodology and LimitationsThe Tweets contained in this file were collected by Ernesto Priego using a Python script. The data collection search query was from:realdonaldtrump. The original data harvesting was refined to delete duplications and the data was re-ordered in chronological order.Retweets have been included (Retweets count as Tweets), so Tweet text duplication is normal.status_url shows the original URL for each Tweet as it was originally posted, however it is possible those links redirect elsewhere or appear as deleted. Both research and experience show that the Twitter search API is not 100% reliable. Large Tweet volumes affect the search collection process. The API might "over-represent the more central users", not offering "an accurate picture of peripheral activity" (Gonzalez-Bailon, Sandra, et al. 2012).Apart from the filters and limitations already declared, it cannot be guaranteed that this file contains each and every Tweet posted by the account realdonaldtrump during the indicated period. This file dataset is shared for archival, comparative and indicative educational research purposes only. The content included is from a public Twitter account and was obtained from the Twitter Search API. The shared data is also publicly available to all Twitter users via the Twitter Search API and available to anyone with an Internet connection via the Twitter and Twitter Search web client and mobile apps without the need of a Twitter account.Each Tweet and its contents were published openly on the Web from the queried public account and are responsibility of the original authors. Original Tweets are likely to be copyright their individual authors but please check individually.No private personal information is shared in this dataset. The collection and sharing of this dataset is enabled and allowed by Twitter's Privacy Policy. The sharing of this dataset complies with Twitter's Developer Rules of the Road.This dataset is shared to archive, document and encourage open educational research into political activity on Twitter.Other ConsiderationsAll Twitter usersagree to Twitter's Privacy and data sharing policies. Different scholarly professional associations like the Modern Language Association recognise Tweets as citeable scholarly outputs.Twitter's search API has well-known temporal limitations for retrospective historical search and collection. Archiving Tweets of public interest due to their historic significance is a means to preserve this form of rapid online communication that otherwise can very likely become unretrievable as time passes. To date, collecting in real time is the only relatively accurate method to archive tweets at a small scale. Archived Tweets can provide interesting insights for the contemporary history of media, politics, diplomacy, etc.Though these datasets have limitations and are not thoroughly systematic, it is hoped they can contribute to developing new insights into the discipline's presence on Twitter over time.The CC-0 license has been applied to the output in the repository as a curated dataset. Authorial/curatorial/collection work has been performed on the file in order to make it openly available as part of the scholarly record.The data contained in the deposited file is otherwise freely available elsewhere through different methods.

This is a CSV file containing Tweet IDs of 3,805 Tweets from user ID 25073877 posted publicly between Thursday February 25 2016 16:35:12 +0000 to Monday April 03 2017 12:51:01 +0000.This file does not include Tweets' texts nor URLs. Columns in the file areid_strfrom_user_id_str created_at time source user_followers_count user_friends_count Motivations to Share this DataArchived Tweets can provide interesting insights for the study of contemporary history of media, politics, diplomacy, etc. The queried account is a public account widely agreed to be of exceptional national and international public interest. Though they provide public access to tweeted content in real time, Twitter Web and mobile clients are not suited for appropriate Tweet corpus analysis. For anyone researching social media, access to the data is absolutely essential in order to perform, review and reproduce studies. Archiving Tweets of public interest due to their historic significance is a means to both preserve and enable reproducible study of this form of rapid online communication that otherwise can very likely become unretrievable as time passes. Due to Twitter's current business model and API limits, to date collecting in real time is the only relatively reliable method to archive Tweets at a small scale. Methodology and LimitationsThe Tweets contained in this file were collected by Ernesto Priego using a Python script. The data collection search query was from:realdonaldtrump. A trigger was scheduled to collect atuomatically every hour. The original data harvesting was refined to delete duplications, to subscribe to Twitter's Terms and Conditions and so that the data was sorted in chronological order.Duplication of data due to the automated collection is possible so further data refining might be required. The file may not contain data from Tweets deleted by the queried user account immediately after original publication. Both research and experience show that the Twitter search API is not 100% reliable. (Gonzalez-Bailon, Sandra, et al. 2012).Apart from the filters and limitations already declared, it cannot be guaranteed that this file contains each and every Tweet posted by the queried account during the indicated period. This file dataset is shared for archival, comparative and indicative educational research purposes only. The content included is from a public Twitter account and was obtained from the Twitter Search API. The shared data is also publicly available to all Twitter users via the Twitter Search API and available to anyone with an Internet connection via the Twitter and Twitter Search web client and mobile apps without the need of a Twitter account.The original Tweets, their contents and associated metadata were published openly on the Web from the queried public account and are responsibility of the original authors. Original Tweets are likely to be copyright their individual authors but please check individually.No private personal information is shared in this dataset. As indicated above this dataset does not contain the text of the Tweets. The collection and sharing of this dataset is enabled and allowed by Twitter's Privacy Policy. The sharing of this dataset complies with Twitter's Developer Rules of the Road.This dataset is shared to archive, document and encourage open educational research into political activity on Twitter.Other ConsiderationsAll Twitter users agree to Twitter's Privacy and data sharing policies. Social media research remains in its infancy and though work has been done to develop best practices there is yet no agreement on a series of grey areas relating to reseach methodologies including ad hoc social media specific research ethics guidelines for reproducible research. Though these datasets have limitations and are not thoroughly systematic, it is hoped they can contribute to developing new insights into the discipline's presence on Twitter over time. Reproducibility is considered here a key value for robust and trustworthy research. Different scholarly professional associations like the Modern Language Association recognise Tweets, datasets and other online and digital resources as citeable scholarly outputs.The data contained in the deposited file is otherwise available elsewhere through different methods.

Slice created from parent data set: Full Virginia Girls' Reformatory Admissions Database 1910-1938

Data set of 2,370 individual female reformatory inmates admitted to the Virginia Home and Industrial School for Girls at Bon Air and the Industrial Home School for Colored Girls at Peake’s Turnout between 1910 and 1938. Created out of the unpublished and archived admissions books of these institutions. Due to Virginia’s 75-year privacy restriction, I stopped collecting at December 1938 for each institution. Data from the Home at Bon Air runs 1910 to 1938; from the Home at Peake’s Turnout from 1915 to 1938. Each reformatory kept separate books, which were archived into separate collections. There was enough similarity between the two books to transcribe the data into one large data set.

Reformatory administrators hand wrote basic administrative information about each girl into bound books. For incoming delinquent girls, they recorded: student number, name, birthdate or age at admittance, date of admittance, and committing jurisdiction (by county or city jurisdiction.) The books also recorded the individual’s parole history, including first parole (and up to her third parole on an individual’s performance) and any return dates; administrators recorded the destination of the first parole, but this was inconsistently recorded. The books note when and to whom inmates were married, usually after their official dismissal. Because transferring an inmate officially removed them from the responsibility of the reformatory, administrators recorded transfer information, including where they went and when. Lastly, the books recorded the official dismissal date and reason. Bon Air’s books were more consistent with recording dismissals and neither institution used consistent definitions of “transfer” versus “dismissal.” I manually transcribed these books verbatim into a database. From this core data, I added categories of information to aid my analysis. These include: race, gender, and reformatory; calculations of either age or birthdate (Peake’s recorded birthdates, Bon Air only ages); parole year taken from the first parole date; parole type determined by me based on the parole destination, if recorded. To help me analyze transfer and dismissal information, I determined the “type” and “category” of each transfer or dismissal and added new categories. These allowed me to “rollup” the varieties of recorded data into fewer descriptive types and categories. Because administrators recorded only the institution name or location when girls were transferred and dismissed elsewhere, this allowed me to organize this info into 18 “types”: asylum, colony, court, death, department of public welfare, escape, family, honorable discharge, illegal commitment, maternity, orphanage, other, penal, private, reorganization (only used for Bon Air in 1914), sanitorium/hospital, venereal disease, and wages. These 18 “types” were then further distilled into 9 “categories” to capture the broadest possible categorization of the reasons why girls were transferred or dismissed: administrative, death, escape, mental, penal, physical, private, unknown, and work.

I have removed the names of the individual inmates upon publication. Researchers interested in using this data in their own work can contact me at erin@erinbush.org to request the versions that include full name fields. The Data Dictionary is also available upon request.

The contents of these data sets, as government records, I believe fall under fair use.

Full Collection

Full Virginia Girls' Reformatory Admissions Database 1910-1938: https://zenodo.org/records/3872019

Full Virginia Girls' Reformatory Transfer and Dismissal Data 1910-1938: https://zenodo.org/records/3872110

Full Virginia Girls' Reformatory Parole Data 1910-1938: https://zenodo.org/records/3872100

This study is a qualitative critical case study examining the role and nature of student participation in decision-making process of two higher education institutes in India. The study examines how even though there are legal frameworks created for student participation at the institutional level, students still feel dissatisfied and alienated within these models of university governance. Therefore, interrogating both formal and informal ways of participation where the former is about leadership, and representation at the institutional and departmental levels, and the latter caters to activism and resistance. The study also investigates this process of student alienation from the institutional decision-making frameworks. Student activism as ways and means to achieve student agency and agents of change are the prime focus of the study.The study adopted Foucault’s discourse analysis of power and notion of governmentality as the theoretical lens to explore students’ participation as stakeholders and neoliberal clients where the students are failing to utilize their agency in the process of increasing bureaucratic structuring, authoritarianism, and the entry of market forces. The study utilised document analysis, observation of participants, in-depth interviews in the form of semi-structured and focus group interviews to collect the data for the case study. Thus, the study examined students’ perceptions and voices in negotiating and contesting power in university governance in India. The data collection techniques involve documents, archival sources, observations, and in-depth interviews.1. Documentary and archival sources included things like the university newspaper, university committee manuals, and reports from the academic and executive councils, notices of the administrative boards, circulars, along with the data obtained from university documents official reports, and documents issued by government and Ministry of Higher Education is also studied for understanding the recent amendments and changes in the governance structures and originations. The pamphlets prepared by various student organizations on campus, election manifesto, leaflets, posters, and propagational speeches, are closely analyzed to understand student concerns and how they are reflected in administrative decisions and the recent amendments of policy documents and framework. The recent newspaper articles, monthly magazines, and online forums are reviewed to understand mass opinion and the reported environment of universities in India. I have reviewed the documents, organizational charts, artifacts, and the official websites of the universities that were relevant to my research, which helped provide contextual data supporting the interviews.2. Interviews: I shared emails to contact students before seeking the face to face interviews and develop my personal network with them. I've reviewed the consent script for the potential participants and then invite them for the in-depth interview by prior informed oral consent. I collected audio recordings of interviews when given explicit consent to do so by the participants, otherwise, I had written notes during the interview. The primary data is largely collected using a semi-structured interview schedule composed mostly of open-ended questions. I have share the interview transcript or the interview notes with the interviewee to ensure transcript accuracy. Every interview is audio recording and or transcript/notes file will be named according to the interview date, interviewee’s pseudonym, and main topic in order to facilitate data and generate codes. Audio recordings and transcript files are organized in folders respectively for different interviewee groups. Data files are password protected, encrypted, and stored in the following secured locations: My personal laptop, external hard drive, and Google Drive. Audio recordings will be initially made on an iPhone during data collection after this recording is transferred to the storage locations and it will be deleted from the phone. while I will avoid asking for personal direct identifiers, if any interviewee uses such personal information (for example full name or caste) during the interviews, I have masked this information in my transcription and/or notes.3. Observation: I obtained informed consent from event participants and organizers. The concerned participants is then notified about the nature of the study and ask for consent, those refusing to participate will have their information excluded. Direct personal identifiers will be masked to protect participant confidentiality. My field notes are recorded as MS Word documents and named by activity date and topic and saved in a folder specifically for observation notes.4. Personal reflections: I recorded my personal reflections on fieldwork experiences in MS Word files and label them as “personal reflection" along with the date recorded.5. Documents and Archival materials: I collected documents and archival materials from my selected field site after receiving the required permission from the relevant stakeholders, including government reports and manuals, records and files compiled by the administrative staff. These archival materials will be stored as scanned PDF documents, image files, or MS Word files depending on the original format.6. All data collected, and files generated will be password protected, encrypted, and stored in the following secured locations my personal laptop, external hard drive, and Google Drive.

This dataset contains information on Government of Canada tender information published according to the Financial Administration Act. It includes data for all Schedule I, Schedule II and Schedule III departments, agencies, Crown corporations, and other entities (unless specifically exempt) who must comply with the Government of Canada trade agreement obligations. CanadaBuys is the authoritative source of this information. Visit the How procurement works page on the CanadaBuys website to learn more. All data files in this collection share a common column structure, and the procurement category field (labelled as “procurementCategory-categorieApprovisionnement”) can be used to filter by the following four major categories of tenders: Tenders for construction, which will have a value of “CNST” Tenders for goods, which will have a value of “GD” Tenders for services, which will have a value of “SRV” Tenders for services related to goods, which will have a value of “SRVTGD” A tender may be associated with one or more of the above procurement categories. Note: Some records contain long tender description values that may cause issues when viewed in certain spreadsheet programs, such as Microsoft Excel. When the information doesn’t fit within the cell’s character limit, the program will insert extra rows that don’t conform to the expected column formatting. (Though, all other records will still be displayed properly, in their own rows.) To quickly remove the “spill-over data” caused by this display error in Excel, select the publication date field (labelled as “publicationDate-datePublication”), then click the Filter button on the Data menu ribbon. You can then use the filter pull-down list to remove any blank or non-date values from this field, which will hide the rows that only contain “spill-over” description information. The following list describes the resources associated with this CanadaBuys tender notices dataset. Additional information on Government of Canada tenders can also be found on the Tender notices tab of the CanadaBuys tender opportunities page. NOTE: While the CanadaBuys online portal includes tender opportunities from across multiple levels of government, the data files in this related dataset only include notices from federal government organizations. (1) CanadaBuys data dictionary: This XML file offers descriptions of each data field in the tender notices files linked below, as well as other procurement-related datasets CanadaBuys produces. Use this as a guide for understanding the data elements in these files. This dictionary is updated as needed to reflect changes to the data elements. (2) New tender notices: This file contains up to date information on all new tender notices that are published to CanadaBuys throughout a given day. The file is updated every two hours, from 6:15 am until 10:15 pm (UTC-0500) to include new tenders as they are published. All tenders in this file will have a publication date matching the current day (displayed in the field labelled “publicationDate-datePublication”), or the day prior for systems that feed into this file on a nightly basis. (3) Open tender notices: This file contains up to date information on all tender notices that are open for bidding on CanadaBuys, including any amendments made to these tender notices during their lifecycles. The file is refreshed each morning, between 7:00 am and 8:30 am (UTC-0500) to include newly published open tenders. All tenders in this file will have a status of open (displayed in the field labelled “tenderStatus-tenderStatut-eng”). (4) All CanadaBuys tender notices, 2022-08-08 onwards: This file contains up to date information on all tender notices published through CanadaBuys. This includes any tender notices that were open for bids on or after August 8, 2022, when CanadaBuys launched as the system of record for all Tender Notices for the Government of Canada. This file includes any amendments made to these tender notices during their lifecycles. It is refreshed each morning, between 7:00 am and 8:30 am (UTC-0500) to include any updates or amendments, as needed. Tender notices in this file can have any publication date on or after August 8, 2022 (displayed in the field labelled “publicationDate-datePublication”), and can have a status of open, cancelled or expired (displayed in the field labelled “tenderStatus-tenderStatut-eng”). (5) Legacy tender notices, 2009 to 2022-08 (prior to CanadaBuys): This file contains details of the tender notices that were launched prior to the implementation of CanadaBuys, which became the system of record for all tender notices for the Government of Canada on August 8, 2022. This datafile is refreshed monthly. The over 70,000 tenders in this file have publication dates from August 5, 2022 and before (displayed in the field labelled “publicationDate-datePublication”) and have a status of cancelled or expired (displayed in the field labelled “tenderStatus-tenderStatut-eng”). Note: Procurement data was structured differently in the legacy applications previously used to administer Government of Canada tender notices. Efforts have been made to manipulate these historical records into the structure used by the CanadaBuys data files, to make them easier to analyse and compare with new records. This process is not perfect since simple one-to-one mappings can’t be made in many cases. You can access these historical records in their original format as part of the archived copy of the original tender notices dataset. You can also refer to the supporting documentation for understanding the new CanadaBuys tender and award notices datasets. (6) Tender notices, YYYY-YYYY: These files contain information on all tender notices published in the specified fiscal year that are no longer open to bidding. The current fiscal year's file is refreshed each morning, between 7:00 am and 8:30 am (UTC-0500) to include any updates or amendments, as needed. The files associated with past fiscal years are refreshed monthly. Tender notices in these files can have any publication date between April 1 of a given year and March 31 of the subsequent year (displayed in the field labelled “publicationDate-datePublication”) and can have a status of cancelled or expired (displayed in the field labelled “tenderStatus-tenderStatut-eng”). New records are added to these files once related tenders reach their close date, or are cancelled. Note: New tender notice data files will be added on April 1 for each fiscal year.

CAPITAL PUNISHMENT IN THE UNITED STATES, 1973-2018 provides annual data on prisoners under a sentence of death, as well as those who had their sentences commuted or vacated and prisoners who were executed. This study examines basic sociodemographic classifications including age, sex, race and ethnicity, marital status at time of imprisonment, level of education, and state and region of incarceration. Criminal history information includes prior felony convictions and prior convictions for criminal homicide and the legal status at the time of the capital offense. Additional information is provided on those inmates removed from death row by yearend 2018. The dataset consists of one part which contains 9,583 cases. The file provides information on inmates whose death sentences were removed in addition to information on those inmates who were executed. The file also gives information about inmates who received a second death sentence by yearend 2018 as well as inmates who were already on death row.

The Scottish Household Survey (SHS) is a continuous survey based on a sample of the general population in private residences in Scotland. It is financed by the Scottish Government (previously the Scottish Executive). The survey started in 1999 and up to 2011 followed a fairly consistent survey design. From 2012 onwards, the survey was substantially redesigned to include elements of the Scottish House Condition Survey (SHCS) (also available from the UK Data Service), including the physical survey. The SHS is run through a consortium led by Ipsos MORI. The survey is designed to provide reliable and up-to-date information on the composition, characteristics, attitudes and behaviour of private households and individuals, both nationally and at a sub-national level and to examine the physical condition of Scotland's homes. It covers a wide range of topics to allow links to be made between different policy areas.

Further information about the survey series, and links to publications, can be found on the Scottish Government's Scottish Household Survey webpages.

COVID-19 restrictions

Due to COVID-19 restrictions, the SHS was conducted by telephone or via MS Teams in 2020 and 2021 (SNs 9186 and 9187). Face-to-face interviewing resumed for SHS 2022 (SN 9294) when restrictions had been lifted.

Scottish Household Survey Lite
The SHS Lite dataset is a simplified version of the full Scottish Household Survey. To stimulate the use of SHS data, particularly amongst local authorities, voluntary organisations and academia, the Scottish Executive decided to commission a simplified data file, which would allow users to undertake most forms of analysis using a substantially smaller data file. The resulting dataset has had 1,300 variables removed. The full SHS dataset is both larger and more complex, containing around 30,000 cases for each two-year sweep of the survey and approximately 2,000 variables. The full 1999-2000 dataset is held at the UK Data Archive (UKDA) under SN 4351.

The Lite dataset is different to the full SHS in that the number of variables has been reduced from 1,825 to 573, complex data loops have been removed and the original variables have been summarised in new variables. The variables have also been organised into 'sets' of related variables. These can be used to further simplify accessing variables through SPSS dialog boxes.

Some aspects of the data have not changed. For example, the number of cases remains over 30,000. With fewer variables however, running analysis will be faster on most computers. The structure of the data continues to include questions that relate to both sections (household and random adult) of the questionnaire. The data still need to be weighted before the results can be considered representative of the household or adult populations. The variable names are still linked to the Computer Aided Personal Interviewing (CAPI) script used to collect the data, and the questionnaire will remain an important reference source for identifying and understanding the variables in the data.

The documentation for the SHS Lite dataset includes a Microsoft Access variable database, and an index of variable names. As it includes search functionality, this is available for download with the SHS Lite dataset, rather than via the 'Online Documentation' table below.

For the second edition of the dataset, a new version of the data file was supplied, and the documentation updated accordingly.

HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The datasets cover the UK at 1 km x 1 km resolution. These 1 km x 1 km data have been used to provide a range of other resolutions and across countries, administrative regions and river basins to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2022, but the start time is dependent on climate variable and temporal resolution.

The gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.

This data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).

The changes for v1.2.0.ceda HadUK-Grid datasets are as follows:

• Added data for calendar year 2022

• Added newly digitised data for monthly sunshine 1910-1918

  • Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242

  • Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files

  • Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs

  • Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.

  • Updated ordering of regions within regional values files. Alphabetical ordering.

  • Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression

• Net changes to the input station data used to generate this dataset:

• Total of 125601744 observations

• 122621050 (97.6%) unchanged

• 26700 (0.02%) modified for this version

• 2953994 (2.35%) added in this version

• 16315 (0.01%) deleted from this version

• Changes to monthly rainfall 1836-1960

• Total of 4823973 observations

• 3315657 (68.7%) unchanged

• 21029 (0.4%) modified for this version

• 1487287 (30.8%) added in this version

• 11155 (0.2%) deleted from this version

The primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project "Analysis of historic drought and water scarcity in the UK"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.