While social media has been proved as an exceptionally useful tool to interact with other people and massively and quickly spread helpful information, its great potential has been ill-intentionally leveraged as well to distort political elections and manipulate constituents. In the paper at hand, we analyzed the presence and behavior of social bots on Twitter in the context of the November 2019 Spanish general election.

Instructions: 

Data have been exported in three formats to provide the maximum flexibility:

  • MongoDB Dump BSONs
    • To import these data, please refer to the official MongoDB documentation.
  • JSON Exports
    • Both the users and the tweets collections have been exported as canonical JSON files. 
  • CSV Exports (only tweets)
    • The tweet collection has been exported as plain CSV file with comma separators.
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739 Views

Socio-economic, demogaphic and protest data of South Africa's 345 Local Municipalties.

Instructions: 

 

Columns

 

Muni - - Name of South African municipality in 2011

 

Change in Population - - % change in population between 2001 and 2011

 

% Unemployed  - - Level of unemployment using official definition (2011)

 

% Poor - - Poverty percentage at household level (2011)

 

Voter Turnout - -% of eligible voters voting in the 2009 National elections (2011)

 

Mean Age - - Average age of people living in municipality (2011)

 

Small Households - - Percentage of households with 1 or 2 people (2011)

 

Large Households - - Percentage of households with 4 or more people (2011)

 

Percentage Youth - - Percentage of population between 16 and 35 (inclusive) (2011)

 

Dep Ratio - - Non working population (by ages, not by employment status) as percentage of total population. Here, "non working" = below 18 or over 60. (2011)

 

% White - - Percentage of population self-identifying as white (2011)

 

% HWW  - - Percentage of households without (reticulated/municipally piped) water (2011)

 

R-P Ratio - - Ratio of richest people to poorest people  (2011)

 

Gini - - Municipal Gini coefficient (2011)

 

% PWM - - Percentage of population with a "Matric" (Grade 12) certificate (2011)

 

% NSL - - Percentage of the population self-reporting their home language to be other than one of South Africa's 11 National Languages  (2011)

 

% Tribal - - Percentage of households living in areas classified as "tribal" by official "geographic type"  (2011)

 

% Male - - Percentage of the population that self-identifies as male (2011)

 

% Informal - - Percentage of households that are classified as informal (structure types) (2011)

 

% Rural - - Percentage of households that are classified as rural (2011)

 

Urban =1 - - One-hot/dummy variable - 1 = municipality is classfied as either "Metropolitan" or "Secondary City" (2011)

 

Urban - - Municipal classification

 

COGTA Score - - 4-level score of municipal efficiency (running good to bad) (2011)

 

Percentage_Poor - - Individual level poverty (best avoided)

 

AG Rating - - 5-level score of municipal governance by the South African Auditor-General (running good to bad) (2011)

 

Prov - - 9 provinces, numeric

 

Prov2 - - 9 provinces, names

 

Pop - - municipal population (2011)

 

Target 1 - -  Count of protests 1997-2013

 

TargetLog - - Log10 of Target1

 

TagetNatLog - - LogE of Target1

 

X Values - - Safely ignore

 

Y Values - - Safely Ignore

 

Target 2 - - Number of protests per capita (2009-2013)

 

2NatLog - - LogE of Target 2

 

NatLog7 - - LogE of count of protests*crowd-size of protests / capita (2009-2013)

 

Target10 - -  Turmoil of protests (0 - 1, where 1 = 100% violent) (2009-2013)

 

13NatLog - - LogE of count of protests*crowd-size of protests / capita (2009-2013) only considering community protests

 

15NatLog - - LogE of count of protests*crowd-size of protests / capita (2009-2013) only considering Labour-related protests

 

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70 Views

This simulated combat reports dataset combines fictional headings, reporting units, and attack times with real data from 551 records of terrorist attacks in Afghanistan (2009–2010) [1]. The dataset combines selected attributes from the DA Form 1594 [2] and U.S. Army Spot Report [3]. The dataset also includes additional attributes for tactical context.

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234 Views

Along with the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on the automatic understanding of UAV videos is of paramount importance.

Instructions: 

=================  Authors  ===========================

Lichao Mou,lichao.mou@dlr.de

Yuansheng Hua, yuansheng.hua@dlr.de

Pu Jin, pu.jin@tum.de

Xiao Xiang Zhu, xiaoxiang.zhu@dlr.de

 

=================  Citation  ===========================

If you use this dataset for your work, please use the following citation:

@article{eradataset,

  title= {{ERA: A dataset and deep learning benchmark for event recognition in aerial videos}},

  author= {Mou, L. and Hua, Y. and Jin, P. and Zhu, X. X.},

  journal= {IEEE Geoscience and Remote Sensing Magazine},

  year= {in press}

}

 

==================  Notice!  ===========================

This dataset is ONLY released for academic uses. Please do not further distribute the dataset on other public websites.

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822 Views

Behavioral traits for 115 employees in public buildings, namely sensitivity to personal comfort loss, desire for conformance to social norms, desire for teaming, desire for rewards. Data has been collected in the pilot studies of H2020 ChArGED for gamified energy conservation in public buildings (grant agreement no. 696170). 

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145 Views

The dataset comprises raw data to validate methods for reliable data collection. We proposed the data collection methods in a path to assess digital healthcare apps. To validate the methods, we conducted experiments in Amazon Mechanical Turk (MTurk), and then we showed that the methods have a significant meaning based on statistical tests.

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107 Views

This dataset was collected for research conducted within the project AN.ON-Next funded by the German Federal Ministry of Education and Research (BMBF) with grant number: 16KIS0371.

Instructions: 

The data are primarily gathered with 7-point Likert scales (exceptions are shown in the documentation). Thus, the analysis requires statistical approaches which are applicable to ordinal data. Examples of how the dataset was used in prior research can be found in the documentation.

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226 Views

This dataset was collected for research conducted within the project AN.ON-Next funded by the German Federal Ministry of Education and Research (BMBF) with grant number: 16KIS0371.

Instructions: 

The data are primarily gathered with 7-point Likert scales (exceptions are shown in the documentation). Thus, the analysis requires statistical approaches which are applicable to ordinal data. Examples of how the dataset was used in prior research can be found in the documentation.

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318 Views

Dataset consists of various open GIS data from the Netherlands as Population Cores, Neighbhourhoods, Land Use, Neighbourhoods, Energy Atlas, OpenStreetMaps, openchargemap and charging stations. The data was transformed for buffers with 350m around each charging stations. The response variable is binary popularity of a charging pool.

Instructions: 

Use the first n_RFID variable as a response, the rest as predictors.

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476 Views

Blended Learning has been widely used in current basic education as a new teaching model, and how to improve the acceptance of students in Blended Learning is a hot issue that needs to be solved in the practice of teaching. 

Instructions: 

357 questionnaire responses are collected through Blended Learning student acceptance questionnaire survey.

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240 Views

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