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Country area DEM provided as 1201x1201 pixel tiles, based on GADM v2.0 country boundaries (http://www.gadm.org/) with some island ccid, internal country boundary, and enclave amendments from gpwv4 (http://www.ciesin.columbia.edu/data/gpw-v4) and GADM v2.8, and Viewfinder Panorama SRTM based 3" topography tiles (http://viewfinderpanoramas.org/). Calculated using Earth surface area grid.
Data Types:
  • File Set
Contains LIWC feature tables for all ~27,000 documents used in this study, R and Python code used to generate statistical results, and all supporting tables.
Data Types:
  • File Set
Replication Data for: Active magnetic levitation and 3-D position measurement for a ball viscometer
Data Types:
  • File Set
Normalized Difference Vegetation Index (NDVI) raster layer captured for an extension of 200 hectares of neotropical forested landscapte in Puerto Viejo de Sarapiquí, Costa Rica at approximately 16 cm pixel resolution. Data is presented in WGS84 geographic coordinate system (EPSG:4326)
Data Types:
  • File Set
Digital Surface Model (DSM) raster layer captured for an extension of 200 hectares of neotropical forested landscapte in Puerto Viejo de Sarapiquí, Costa Rica at approximately 16 cm pixel resolution. Data is presented in WGS84 geographic coordinate system (EPSG:4326) (2015-10-26)
Data Types:
  • File Set
Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, creating a global gridscape at the confluence between geography and agricultural production systems. Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts.
Data Types:
  • Document
  • File Set
The large majority of inferences drawn in empirical political research follow from model-based associations (e.g. regression). Here, we articulate the benefits of pre- dictive modeling as a complement to this approach. Predictive models aim to specify a probabilistic model that provides a good fit to testing data that were not used to estimate the model’s parameters. Our goals are threefold. First, we review the central benefits of this under-utilized approach from a perspective uncommon in the existing literature: we focus on how predictive modeling can be used to complement and aug- ment standard associational analyses. Second, we advance the state of the literature by laying out a simple set of benchmark predictive criteria. Third, we illustrate our approach through a detailed application to the prediction of interstate conflict.
Data Types:
  • Text
  • File Set
How does individuals’ influence in a large social network change? Social scientists have difficulty answering this question because measuring influence requires frequent observations of a population of individuals’ connections to each other, while sampling that social network removes information in a way that can bias inferences. This paper introduces a method to measure influence over time accurately from sampled network data. Ranking individuals by the sum of their connections’ connections — neighbor cumulative indegree centrality — preserves the rank influence ordering that would be achieved in the presence of complete network data, lowering the barrier to measuring influence accurately. The paper then shows how to measure that variable changes each day, making it possible to analyze when and why an individual’s influence in a network changes. This method is demonstrated and validated on 21 Twitter accounts in Bahrain and Egypt from early 2011. The paper then discusses how to use the method in domains such as voter mobilization and marketing.
Data Types:
  • Software/Code
  • Text
  • File Set
Review of Economics and Statistics: Forthcoming
Data Types:
  • File Set
This dataset contains data and software for the following paper: Hill, Benjamin Mako and Shaw, Aaron. (2015) “Page Protection: Another Missing Dimension of Wikipedia Research.” In Proceedings of the 11th International Symposium on Open Collaboration (OpenSym 2015). ACM Press. doi: 10.1145/2788993.2789846 This is an archival version of the data and software released with the paper. All of these data were (and, at the time of writing, continue to be) hosted at: https://communitydata.cc/wiki-proetection/ Page protection is a feature of MediaWiki software that allows administrators to restrict contributions to particular pages. For example, a page can be “protected” so that only administrators or logged-in editors with a history of good editing can edit, move, or create it. Protection might involve “full protection” where a page can only be edited by administrators (i.e., “sysops”) or “semi-protection” where a page can only be edited by accounts with a history of good edits (i.e., “autoconfirmed” users). Although largely hidden, page protection profoundly shapes activity on the site. For example, page protection is an important tool used to manage access and participation in situations where vandalism or interpersonal conflict can threaten to undermine content quality. While protection affects only a small portion of pages in English Wikipedia, many of the most highly viewed pages are protected. For example, the “Main Page” in English Wikipedia has been protected since February, 2006 and all Featured Articles are protected at the time they appear on the site’s main page. Millions of viewers may never edit Wikipedia because they never see an edit button. Despite it's widespread and influential nature, very little quantitative research on Wikipedia has taken page protection into account systematically. This page contains software and data to help Wikipedia researchers do exactly this in their work. Because a page's protection status can change over time, the snapshots of page protection data stored by Wikimedia and published by Wikimedia Foundation in as dumps is incomplete. As a result, taking protection into account involves looking at several different sources of data. Much more detail can be found in our paper Page Protection: Another Missing Dimension of Wikipedia Research. If you use this software or these data, we would appreciate if you cite the paper.
Data Types:
  • Software/Code
  • File Set
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