Dynamic Documents with R and knitr

The analysis of public policies, even when performed by the best non-partisanagencies, often lacks credibility (Manski, 2013). This allows policy makers to cherrypick between reports, or within a specific report, to select estimates that better match their beliefs. For example, in 2014 the Congressional Budget Office (CBO) produced a report on the effects of raising the minimum wage that was cited both by opponents and supporters of the policy, with each side accepting as credible only partial elements of the report. Lack of transparency and reproducibility (TR) in a policy report implies that its credibility relies on the reputation of the authors, and their organizations, instead of on a critical appraisal of the analysis.This dissertation translates to policy analysis solutions developed to address thelack of credibility in a different setting: the reproducibility crisis in science. I adapt the Transparency and Openness Promotion (TOP) guidelines (Nosek et al, 2015) to the policy analysis setting. The highest standards from the adapted guidelines involve the use of two key tools: dynamic documents that combine all elements of an analysis in one place, and open source version control (git). I then implement these high standards in a case study of the CBO report mentioned above, and present the complete analysis in the form of an open-source dynamic document. In addition to increasing the credibility of the case study analysis, this methodology brings attention to several components of the policy analysis that have been traditionally overlooked in academic research, for example the distribution of the losses used to pay for the increase in wages. Increasing our knowledge in these overlooked areas may prove most valuable to an evidence-based policy debate.

Download Full-text

Multi-Step Iterative Algorithm for Feature Selection on Dynamic Documents

International Journal of Information Retrieval Research ◽

10.4018/ijirr.2016040102 ◽

2016 ◽

Vol 6 (2) ◽

pp. 24-40 ◽

Cited By ~ 2

Author(s):

Prafulla Bharat Bafna ◽

Shailaja Shirwaikar ◽

Dhanya Pramod

Keyword(s):

Feature Selection ◽

Iterative Algorithm ◽

Research Papers ◽

Semantic Group ◽

Term Frequency ◽

Dynamic Documents ◽

Parent Term ◽

Artificial Datasets

The authors propose clustering based multistep iterative algorithm. The important step is where terms are grouped by synonyms. It takes advantage of semantic relativity measure between the terms. Term frequency is computed of the group of synonyms by considering the relativity measure of the terms appearing in the document from the parent term in the group. This increases the importance of terms which though individually appear less frequently but together show their strong presence. The authors tried experiments on different real and artificial datasets such as NEWS 20, Reuters, emails, research papers on different topics. Resulted entropy shows that their algorithm gives improved result on certain set of documents which are well-articulated, such as research papers. The results are marginal on documents where the message is emphasized by repetitions of terms specifically the documents that are rapidly generated such as emails. The authors also observed that newly arrived documents get appropriately mapped based on proximity to the semantic group.

Download Full-text

Ensuring Scientific Reproducibility within the Earth Observation Community: Standardized Algorithm Documentation for Improved Scientific Data Understanding

10.5194/egusphere-egu2020-3663 ◽

2020 ◽

Author(s):

Aaron Kaulfus ◽

Kaylin Bugbee ◽

Alyssa Harris ◽

Rahul Ramachandran ◽

Sean Harkins ◽

...

Keyword(s):

Earth Science ◽

Scientific Data ◽

Earth Observation ◽

Data Reuse ◽

Observation Data ◽

Science Data ◽

Dynamic Documents ◽

Common Information ◽

Earth Observation Data ◽

Earth Science Data

<p>Algorithm Theoretical Basis Documents (ATBDs) accompany Earth observation data generated from algorithms. ATBDs describe the physical theory, mathematical procedures and assumptions made for the algorithms that convert radiances received by remote sensing instruments into geophysical quantities. While ATBDs are critical to scientific reproducibility and data reuse, there have been technical, social and informational issues surrounding the creation and maintenance of these key documents. A standard ATBD structure has been lacking, resulting in inconsistent documents of varying levels of detail. Due to the lack of a minimum set of requirements, there has been very little formal guidance on the ATBD publication process.&#160; Additionally, ATBDs have typically been provided as static documents that are not machine readable, making search and discovery of the documents and the content within the documents difficult for users. To address the challenges surrounding ATBDs, NASA has prototyped the Algorithm Publication Tool (APT), a centralized cloud-based publication tool that standardizes the ATBD content model and streamlines the ATBD authoring process. This presentation will describe our approach in developing a common information model for ATBDs and our efforts to provide ATBDs as dynamic documents that are available for both human and machine utilization. We will also include our vision for APT within the broader NASA Earth science data system and how this tool may assist in standardizes and easing the ATBD creation and maintenance process.</p>

Download Full-text