Update built resources

content/contributor-analysis/index.md

@@ -9,10 +9,10 @@ output:
 type: contributors_analysis
 ---
 
-As of 12 February 2026, FORRT has a total of 98 completed or ongoing
-projects and support teams, with a total of 627 contributors. There is
-an average (mean) of 2.4 contributions per person across all FORRT
-projects, and the average number of contributors per project is 15. You
+As of 10 June 2026, FORRT has a total of 109 completed or ongoing
+projects and support teams, with a total of 647 contributors. There is
+an average (mean) of 2.44 contributions per person across all FORRT
+projects, and the average number of contributors per project is 14. You
 can see the full list of FORRT contributors and their individual
 contributions [here](https://forrt.org/contributors/).
 

content/curated_resources/1-500-scientists-lift-the-lid-on-reprodu.md

@@ -0,0 +1,41 @@
+{
+    "timestamp": "2020-05-28T13:13:41.155Z",
+    "title": "1,500 scientists lift the lid on reproducibility",
+    "link_to_resource": "https://doi.org/10.1038/533452a",
+    "creators": [
+        "Monya Baker"
+    ],
+    "material_type": [
+        "Primary Source",
+        "Reading",
+        "Paper"
+    ],
+    "education_level": [
+        "College / Upper Division (Undergraduates)"
+    ],
+    "abstract": "Survey sheds light on the \u2018crisis\u2019 rocking research.",
+    "language": [
+        "English"
+    ],
+    "conditions_of_use": "I don't see any of these",
+    "primary_user": [
+        "Student"
+    ],
+    "subject_areas": [
+        "Applied Science",
+        "Social Science"
+    ],
+    "FORRT_clusters": [
+        "Reproducibility and Replicability Knowledge"
+    ],
+    "tags": [
+        "Reproducibility Crisis and Credibility Revolution",
+        "Open Science"
+    ],
+    "forrt_clusters_new": "Replication Crisis and Credibility Revolution",
+    "forrt_sub_clusters": "History of the replication crisis & credibility revolution",
+    "doi": "10.1038/533452a",
+    "in-clusters": true,
+    "pdf name": "",
+    "apa reference": ""
+}

content/curated_resources/10-things-for-curating-reproducible-and.md

@@ -0,0 +1,44 @@
+{
+    "timestamp": "6/27/2023 14:45:33",
+    "title": "10 Things for Curating Reproducible and FAIR Research",
+    "link_to_resource": "https://doi.org/10.15497/RDA00074",
+    "creators": [
+        "RDA CURE-FAIR WG (Consortium)"
+    ],
+    "material_type": [
+        "Primary Source",
+        "Teaching/Learning Strategy"
+    ],
+    "education_level": [
+        "Graduate / Professional",
+        "Career /Technical"
+    ],
+    "abstract": "10 Things for Curating Reproducible and FAIR Research (CURE-FAIR) provides standards-based guidelines to follow CURE-FAIR best practices with regards to publishing and archiving computationally reproducible studies, including the associated computational methods and materials. The 10 Things are modular (users can select which Thing is relevant to their needs as well as the level of guidance they prefer: 'Get started', 'Learn more', and 'Go deeper') and customizable. While focusing on social science research that relies on quantitative data, it can be adapted for broader use in life sciences and other long tail small scale research relying on data analysis. See also https://curating4reproducibility.org/10things/ ",
+    "language": [
+        "English"
+    ],
+    "conditions_of_use": "CC BY",
+    "primary_user": [
+        "Librarian"
+    ],
+    "subject_areas": [
+        "Social Science"
+    ],
+    "FORRT_clusters": [
+        "Reproducibility and Replicability Knowledge",
+        "Reproducible Analyses",
+        "Open Data and Materials"
+    ],
+    "tags": [
+        "Reproducibility",
+        "Curation",
+        "Open Data",
+        "Open Science"
+    ],
+    "forrt_clusters_new": "Transparency and reproducibility in computation and analysis, FAIR data and materials",
+    "forrt_sub_clusters": "Computational reproducibility, Research data management, Metadata standards",
+    "doi": "10.15497/RDA00074",
+    "in-clusters": true,
+    "pdf name": "",
+    "apa reference": ""
+}

content/curated_resources/1economics-270d-research-transparency-me.md

@@ -0,0 +1,43 @@
+{
+    "timestamp": "10/14/2021 13:51:02",
+    "title": "1Economics 270D: Research Transparency Methods in the Social Sciences",
+    "link_to_resource": "https://osf.io/bt6j2/",
+    "creators": [
+        "Edward Miguel"
+    ],
+    "material_type": [
+        "Syllabus"
+    ],
+    "education_level": [
+        "Graduate / Professional"
+    ],
+    "abstract": "This course coversa range of approachesthat aim to enhance the transparency and reproducibility of social science research. It is appropriate for Ph.D. students in social science disciplines and related fields.",
+    "language": [
+        "English"
+    ],
+    "conditions_of_use": "I don't see any of these",
+    "primary_user": [
+        "Teacher"
+    ],
+    "subject_areas": [
+        "Social Science"
+    ],
+    "FORRT_clusters": [
+        "Reproducibility and Replicability Knowledge",
+        "Conceptual and Statistical Knowledge",
+        "Reproducible Analyses",
+        "Preregistration",
+        "Open Data and Materials",
+        "Replication Research"
+    ],
+    "tags": [
+        "Reproducibility Crisis and Credibility Revolution",
+        "Open Science"
+    ],
+    "forrt_clusters_new": "Replication Crisis and Credibility Revolution",
+    "forrt_sub_clusters": "Proposed science improvement initiatives on statistics, measurement, teaching, data sharing, code sharing, pre-registration, & replication",
+    "doi": "",
+    "in-clusters": false,
+    "pdf name": "",
+    "apa reference": ""
+}

content/curated_resources/66th-helmholtz-open-science-online-semin.md

@@ -0,0 +1,45 @@
+{
+    "timestamp": "2/27/2025 3:53:23",
+    "title": "66th Helmholtz Open Science Online Seminar",
+    "link_to_resource": "https://os.helmholtz.de/en/events/online-seminars/66th-online-seminar/",
+    "creators": [
+        "Helmholz Open Science"
+    ],
+    "material_type": [
+        "Reading",
+        "Lecture Slides",
+        "Seminar Recording"
+    ],
+    "education_level": [
+        "College / Upper Division (Undergraduates)",
+        "Graduate / Professional"
+    ],
+    "abstract": "In September 2022 the Helmholtz Association adopted a new Open Science Policy. The policy stipulates that scholarly publications, research data, and research software are published openly in Helmholtz, Germany's largest research performing organization. In doing so, the policy takes up the UNESCO Recommendation on Open Science and relates to the EU Commission's open science funding policy in the current Horizon Europe research framework program.\n\nCERN, the European Organization for Nuclear Research, also approved a new policy for open science in September 2022. This policy aims to make all CERN research fully accessible, inclusive, democratic and transparent, both to other researchers and to wider society, and thus also refers to UNESCO\u2019s Recommendation on Open Science.\n\nThe 66th Helmholtz Open Science Online Seminar was dedicated to the topic of the creation, design, and implementation of Open Science Policies with contributions from CERN and the Helmholtz Open Science Office, and was opened with a welcome address by Lutz M\u00f6ller, Deputy Secretary-General of the German Commission for UNESCO.",
+    "language": [
+        "English"
+    ],
+    "conditions_of_use": "CC BY",
+    "primary_user": [
+        "Student",
+        "Teacher"
+    ],
+    "subject_areas": [
+        "Physical Science"
+    ],
+    "FORRT_clusters": [
+        "Reproducibility and Replicability Knowledge",
+        "Open Data and Materials"
+    ],
+    "tags": [
+        "Helmholz Open Science",
+        "Open Data",
+        "Open Software",
+        "Open Access"
+    ],
+    "forrt_clusters_new": "FAIR data and materials",
+    "forrt_sub_clusters": "Repositories, Research data management",
+    "doi": "",
+    "in-clusters": false,
+    "pdf name": "",
+    "apa reference": ""
+}

content/curated_resources/7-easy-steps-to-open-science-an-annotate.md

@@ -0,0 +1,55 @@
+{
+    "timestamp": "6/27/2023 14:45:33",
+    "title": "7 Easy Steps to Open Science: An Annotated Reading List",
+    "link_to_resource": "https://psyarxiv.com/cfzyx/",
+    "creators": [
+        "Alexander Etz",
+        "Amy Orben",
+        "Hannah Moshontz",
+        "Jesse Niebaum",
+        "Johnny van Doorn",
+        "Matthew Makel",
+        "Michael Schulte-Mecklenbeck",
+        "Sam Parsons",
+        "Sophia Cr\u00fcwell"
+    ],
+    "material_type": [
+        "Reading"
+    ],
+    "education_level": [
+        "Graduate / Professional"
+    ],
+    "abstract": "The Open Science movement is rapidly changing the scientific landscape. Because exact definitions are often lacking and reforms are constantly evolving, accessible guides to open science are needed. This paper provides an introduction to open science and related reforms in the form of an annotated reading list of seven peer-reviewed articles, following the format of Etz et al. (2018). Written for researchers and students - particularly in psychological science - it highlights and introduces seven topics: understanding open science; open access; open data, materials, and code; reproducible analyses; preregistration and registered reports; replication research; and teaching open science. For each topic, we provide a detailed summary of one particularly informative and actionable article and suggest several further resources. Supporting a broader understanding of open science issues, this overview should enable researchers to engage with, improve, and implement current open, transparent, reproducible, replicable, and cumulative scientific practices.",
+    "language": [
+        "English"
+    ],
+    "conditions_of_use": "cc-by",
+    "primary_user": [
+        "student",
+        "teacher"
+    ],
+    "subject_areas": [
+        "Applied Science",
+        "Life Science",
+        "Physical Science",
+        "Social Science"
+    ],
+    "FORRT_clusters": [
+        ""
+    ],
+    "tags": [
+        "Data",
+        "Materials",
+        "Open Scholarship Guidelines",
+        "Policy",
+        "Publishing",
+        "Reproducibility",
+        "Researchers"
+    ],
+    "forrt_clusters_new": "Replication Crisis and Credibility Revolution",
+    "forrt_sub_clusters": "Proposed science improvement initiatives on statistics, measurement, teaching, data sharing, code sharing, pre-registration, & replication",
+    "doi": "",
+    "in-clusters": false,
+    "pdf name": "",
+    "apa reference": ""
+}

content/curated_resources/a-21-word-solution.md

@@ -0,0 +1,47 @@
+{
+    "timestamp": "2020-05-27T16:12:42.502Z",
+    "title": "A 21 word solution.",
+    "link_to_resource": "http://dx.doi.org/10.2139/ssrn.2160588",
+    "creators": [
+        "Simmons",
+        "Joseph P. and Nelson",
+        "Leif D. and Simonsohn",
+        "Uri",
+        "A"
+    ],
+    "material_type": [
+        "Primary Source",
+        "Reading",
+        "Paper"
+    ],
+    "education_level": [
+        "College / Upper Division (Undergraduates)"
+    ],
+    "abstract": "One year after publishing \"False-Positive Psychology,\" we propose a simple implementation of disclosure that requires but 21 words to achieve full transparency. This article is written in a casual tone. It includes phone-taken pictures of milk-jars and references to ice-cream and sardines.",
+    "language": [
+        "English"
+    ],
+    "conditions_of_use": "I don't see any of these",
+    "primary_user": [
+        "Student"
+    ],
+    "subject_areas": [
+        "Applied Science",
+        "Social Science"
+    ],
+    "FORRT_clusters": [
+        "Reproducibility and Replicability Knowledge",
+        "Reproducible Analyses",
+        "Replication Research"
+    ],
+    "tags": [
+        "Reproducibility Crisis and Credibility Revolution",
+        "Open Science"
+    ],
+    "forrt_clusters_new": "Replication Crisis and Credibility Revolution",
+    "forrt_sub_clusters": "Proposed science improvement initiatives on statistics, measurement, teaching, data sharing, code sharing, pre-registration, & replication",
+    "doi": "10.2139/ssrn.2160588",
+    "in-clusters": true,
+    "pdf name": "",
+    "apa reference": ""
+}

content/curated_resources/a-bayesian-perspective-on-the-reproducib.md

@@ -0,0 +1,41 @@
+{
+    "timestamp": "2020-05-31T18:51:25.106Z",
+    "title": "A Bayesian Perspective on the Reproducibility Project: Psychology",
+    "link_to_resource": "https://doi.org/10.1371/journal.pone.0149794",
+    "creators": [
+        "Alexander Etz and Joachim Vandekerckhove"
+    ],
+    "material_type": [
+        "Primary Source",
+        "Reading",
+        "Paper"
+    ],
+    "education_level": [
+        "College / Upper Division (Undergraduates)"
+    ],
+    "abstract": "We revisit the results of the recent Reproducibility Project: Psychology by the Open Science Collaboration. We compute Bayes factors\u2014a quantity that can be used to express comparative evidence for an hypothesis but also for the null hypothesis\u2014for a large subset (N = 72) of the original papers and their corresponding replication attempts. In our computation, we take into account the likely scenario that publication bias had distorted the originally published results. Overall, 75% of studies gave qualitatively similar results in terms of the amount of evidence provided. However, the evidence was often weak (i.e., Bayes factor < 10). The majority of the studies (64%) did not provide strong evidence for either the null or the alternative hypothesis in either the original or the replication, and no replication attempts provided strong evidence in favor of the null. In all cases where the original paper provided strong evidence but the replication did not (15%), the sample size in the replication was smaller than the original. Where the replication provided strong evidence but the original did not (10%), the replication sample size was larger. We conclude that the apparent failure of the Reproducibility Project to replicate many target effects can be adequately explained by overestimation of effect sizes (or overestimation of evidence against the null hypothesis) due to small sample sizes and publication bias in the psychological literature. We further conclude that traditional sample sizes are insufficient and that a more widespread adoption of Bayesian methods is desirable.",
+    "language": [
+        "English"
+    ],
+    "conditions_of_use": "I don't see any of these",
+    "primary_user": [
+        "Student"
+    ],
+    "subject_areas": [
+        "Social Science"
+    ],
+    "FORRT_clusters": [
+        "Reproducibility and Replicability Knowledge",
+        "Replication Research"
+    ],
+    "tags": [
+        "Reproducibility Crisis and Credibility Revolution",
+        "Open Science"
+    ],
+    "forrt_clusters_new": "Replication and meta-research, Replication Crisis and Credibility Revolution",
+    "forrt_sub_clusters": "Collection of large scale replications, Meta-analyses",
+    "doi": "10.1371/journal.pone.0149794",
+    "in-clusters": true,
+    "pdf name": "",
+    "apa reference": ""
+}

content/curated_resources/a-call-for-open-science-in-forensics.md

@@ -0,0 +1,44 @@
+{
+    "timestamp": "12/19/2024 9:27:04",
+    "title": "A call for open science in forensics",
+    "link_to_resource": "https://doi.org/10.1073/pnas.2321809121",
+    "creators": [
+        "Thomas D. Albright and Nicholas Scurich"
+    ],
+    "material_type": [
+        "Reading"
+    ],
+    "education_level": [
+        "College / Upper Division (Undergraduates)",
+        "Graduate / Professional"
+    ],
+    "abstract": "The modern canon of open science consists of five \u201cschools of thought\u201d that justify unfettered access to the fruits of scientific research: i) public engagement, ii) democratic right of access, iii) efficiency of knowledge gain, iv) shared technology, and v) better assessment of impact. Here, we introduce a sixth school: due process. Due process under the law includes a right to \u201cdiscovery\u201d by a defendant of potentially exculpatory evidence held by the prosecution. When such evidence is scientific, due process becomes a Constitutional mandate for open science. To illustrate the significance of this new school, we present a case study from forensics, which centers on a federally funded investigation that reports summary statistics indicating that identification decisions made by forensic firearms examiners are highly accurate. Because of growing concern about validity of forensic methods, the larger scientific community called for public release of the complete analyzable dataset for independent audit and verification. Those in possession of the data opposed release for three years while summary statistics were used by prosecutors to gain admissibility of evidence in criminal trials. Those statistics paint an incomplete picture and hint at flaws in experimental design and analysis. Under the circumstances, withholding the underlying data in a criminal proceeding violates due process. Following the successful open-science model of drug validity testing through \u201cclinical trials,\u201d which place strict requirements on experimental design and timing of data release, we argue for registered and open \u201cforensic trials\u201d to ensure transparency and accountability.",
+    "language": [
+        "English"
+    ],
+    "conditions_of_use": "CC BY",
+    "primary_user": [
+        "Student",
+        "Teacher"
+    ],
+    "subject_areas": [
+        "Applied Science",
+        "Law",
+        "Physical Science"
+    ],
+    "FORRT_clusters": [
+        "Reproducibility and Replicability Knowledge",
+        "Open Data and Materials"
+    ],
+    "tags": [
+        "Forensics",
+        "Due process",
+        "Evidence"
+    ],
+    "forrt_clusters_new": "FAIR data and materials",
+    "forrt_sub_clusters": "Reasons to share data and materials",
+    "doi": "10.1073/pnas.2321809121",
+    "in-clusters": true,
+    "pdf name": "",
+    "apa reference": ""
+}