replicability-presentation-2022
Presentation on Lessons Learned after 1000 articles
https://github.com/labordynamicsinstitute/replicability-presentation-2022
Science Score: 52.0%
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✓CITATION.cff file
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✓codemeta.json file
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○DOI references
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Organization labordynamicsinstitute has institutional domain (www.ilr.cornell.edu) -
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○Scientific vocabulary similarity
Low similarity (2.6%) to scientific vocabulary
Repository
Presentation on Lessons Learned after 1000 articles
Basic Info
- Host: GitHub
- Owner: labordynamicsinstitute
- License: bsd-3-clause
- Default Branch: main
- Size: 52.8 MB
Statistics
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 0
- Releases: 6
Metadata Files
README.md
Presentation on Lessons Learned after 1000 articles
The American Economic Association's Data Editor has reviewed more than 1,000 empirical articles since July 2019, and worked with authors to improve the reproducibility of their research compendia (replication packages). Some lessons emerge from this work. In this presentation, I will focus on lessons for young scholars (students and young researchers), on possible lessons even for more seasoned researchers. Students and researchers are embedded within institutions, and I will discuss the kind of support that institutions (universities, data providers, compute services) should be providing to students, faculty, and researchers, for a robust, reproducible, and transparent science enterprise.
This particular presentation has been shortened for presentation at the MEA-SOLE meetings in 2023.
Owner
- Name: Labor Dynamics Institute
- Login: labordynamicsinstitute
- Kind: organization
- Email: ldi@cornell.edu
- Location: Ithaca, NY, USA
- Website: http://www.ilr.cornell.edu/ldi/
- Repositories: 138
- Profile: https://github.com/labordynamicsinstitute
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
Transparency and Reproducibility in Economics:
Context and Lessons learned from 1,000 papers
message: Use this citation when referencing the presentation
type: dataset
authors:
- given-names: Lars
family-names: Vilhuber
email: lars.vilhuber@cornell.edu
affiliation: Cornell University
orcid: 'https://orcid.org/0000-0001-5733-8932'
identifiers:
- type: doi
value: 10.5281/zenodo.6787998
description: DOI for all versions
repository-code: >-
https://github.com/labordynamicsinstitute/replicability-presentation-2022/
abstract: >-
The American Economic Association's Data Editor has
reviewed more than 1,000 empirical articles since
July 2019, and worked with authors to improve the
reproducibility of their research compendia
(replication packages). Some lessons emerge from
this work. In this presentation, I will focus on
lessons for young scholars (students and young
researchers), on possible lessons even for more
seasoned researchers. Students and researchers are
embedded within institutions, and I will discuss
the kind of support that institutions
(universities, data providers, compute services)
should be providing to students, faculty, and
researchers, for a robust, reproducible, and
transparent science enterprise. This particular presentation has
been shortened for presentation at the MEA-SOLE meetings in 2023.
license: CC-BY-4.0
date-released: '2023-03-31'