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This workshop was given during SRI Congress 2023. https://sricongress.org/ Data Management Plan Yoga: learning techniques and developing your practice Like yoga, developing and using a Data Management Plan (also known as a Data and Digital Output Management Plan) should become an integral part of your daily practice. Commonly a Data Management Plan (DMP or DDOMP) only includes the requirements of the funder which are focused on final products and long-term preservation. What is left out are the steps necessary to plan and manage your digital objects (e.g., datasets, software, images) throughout your research process. Research is a dynamic process and the DMP needs to be flexible over time. In this workshop we will discuss the elements to include in a DMP, and the leading practices on how to determine the best approach for your team. At the end of the session, you will have a DMP template, how to complete your own DMP, and how to help your team meet the expectations set in the DDOMP. The session agenda: Welcome and Introduction Overview of a Data Management Plan, with Q&A Detailed review of how to approach each DMP element with interactive discussion Break Operationalizing the DMP with your Team Exercise: Setting up tools to help your Team Wrap up Your instructors are Belmont Forum awardees who developed a proven method for all researchers to meet the demanding requirements established for the DDOMP, or any funder DMP. Robust data and software management supports FAIR and Open Science., This presentation is a contribution from the PARSEC team. PARSEC is a project sponsored by the Belmont Forum as part of its Collaborative Research Action (CRA) on Science-Driven e-Infrastructures Innovation (SEI), with funding from FAPESP, the ANR, JST and the NSF, with collaborators from Australia, and support from the synthesis centre CESAB of the French Foundation for Research on Biodiversity.

The PARSEC team, organised by Yasuhiro Murayama, contributed a session to the Japanese Open Science Summit, 2023, in which common challenges were discussed for effective and creditable sharing, publication, and re-use of data and code with the community and to catalyse pathways to their solution. This session is part of the Belmont Forum-funded project, PARSEC, which has been extended by one year (to 2024). The PARSEC team from five geographically-dispersed countries has collaborated for the last four years on the collation and harmonisation of data and the development of new methods for sharing data and code as we investigate the socio-economic effects of nature conservation initiatives. The session language will be English in principle. The speakers were: Alison Specht (The University of Queensland) Yasuhisa Kondo (Research Institute for Humanity and Nature) Shelley Stall(American Geophysical Union) Nobuko Miyairi (National Institute of Information and Communications Technology) * Kazuhiro Hayashi (National Institute of Science and Technology Policy) Yasuhiro Murayama (National Institute of Information and Communications Technology) * this presentation is in this associated document: Miyairi, Nobuko. (2023, June 20). RDA national PID strategies WG: final outputs and the way forward. Zenodo. https://doi.org/10.5281/zenodo.8058059, This presentation is a contribution from the PARSEC team. PARSEC is a project sponsored by the Belmont Forum as part of its Collaborative Research Action (CRA) on Science-Driven e-Infrastructures Innovation (SEI), with funding from FAPESP, the ANR, JST and the NSF, with collaborators from Australia, and support from the synthesis centre CESAB of the French Foundation for Research on Biodiversity.

Ensure your team has access to common resources and guidelines that support collaboration, transparency, and openness. This work is part of theBuilding New Tools for Data Sharing and Re-use through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas (PARSEC)project with fundingprovided by the Belmont Forum through the National Science Foundation, NSF, Grant1929464, (US), Agence Nationale de la Recherche,ANR (France), Fundação de Amparo à Pesquisa do Estado de São Paulo,FAPESP (Brazil), and Japan Science and Technology Agency,JST (Japan). Version 2 includes minor text updates adding clarification, updated links, and funder logos.&nbsp

Ensure all digital objects created or used by the team are fully documented, preserved for the long-term, and made openly accessible to the team. This work is part of theBuilding New Tools for Data Sharing and Re-use through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas (PARSEC)project with fundingprovided by the Belmont Forum through the National Science Foundation, NSF, Grant1929464, (US), Agence Nationale de la Recherche,ANR (France), Fundação de Amparo à Pesquisa do Estado de São Paulo,FAPESP (Brazil), and Japan Science and Technology Agency,JST (Japan). Version 2 includes minor text updates adding clarification, updated links, and funder logos.&nbsp

Asegúrese de que su equipo tiene acceso a recursos y guías comunes que favorezcan la colaboración, la transparencia y la apertura. Este trabajo es parte del proyectoCreación de nuevas herramientas para compartir y reutilizar datos a través de una investigación transnacional de los impactos socioeconómicos de las áreas protegidas (PARSEC)financiado por Belmont Forum a través de National Science Foundation, NSF, Grant 1929464, (EE.UU), Agence Nationale de la Recherche, ANR (Francia), Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (Brasil) y Japan Science and Technology Agency, JST (Japón).

Garantizar que todos los objetos digitales creados o utilizados por el equipo estén plenamente documentados, se preserven a largo plazo y el equipo pueda acceder a ellos abiertamente. Este trabajo es parte del proyectoCreación de nuevas herramientas para compartir y reutilizar datos a través de una investigación transnacional de los impactos socioeconómicos de las áreas protegidas (PARSEC)financiado por Belmont Forum a través de National Science Foundation, NSF, Grant 1929464, (EE.UU), Agence Nationale de la Recherche, ANR (Francia), Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (Brasil) y Japan Science and Technology Agency, JST (Japón).

Desenvolva e utilize práticas de Ciência Aberta para seu laboratório ou grupo de pesquisa. Melhore as práticas de gerenciamento de dados e software da equipe. Codifique estas práticas no Código de Prática de Ciência Aberta de seu grupo. Este trabalho é parte do projetoDesenvolvimento de Novas Ferramentas para o Compartilhamento e Reuso de Dados através de Pesquisa Transnacional sobre o Impacto Socioeconômico das Unidades de Conservação (PARSEC)financiado pelo Belmont Forum por meio da National Science Foundation, NSF, Grant 1929464, (EUA), Agence Nationale de la Recherche, ANR (França), Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (Brasil) e Japan Science and Technology Agency, JST (Japão).

Garanta que sua equipe tenha acesso aos recursos e às diretrizes que dão suporte à colaboração, transparência e abertura. Este trabalho é parte do projeto Desenvolvimento de Novas Ferramentas para o Compartilhamento e Reuso de Dados através de Pesquisa Transnacional sobre o Impacto Socioeconômico das Unidades de Conservação (PARSEC) financiado pelo Belmont Forum por meio da National Science Foundation, NSF, Grant 1929464, (EUA), Agence Nationale de la Recherche, ANR (França), Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (Brasil) e Japan Science and Technology Agency, JST (Japão).

Certifique-se de que todos os objetos digitais criados ou usados pela equipe sejam plenamente documentados, preservados para o longo prazo e disponibilizados abertamente para o grupo. Este trabalho é parte do projetoDesenvolvimento de Novas Ferramentas para o Compartilhamento e Reuso de Dados através de Pesquisa Transnacional sobre o Impacto Socioeconômico das Unidades de Conservação (PARSEC)financiado pelo Belmont Forum por meio da National Science Foundation, NSF, Grant 1929464, (EUA), Agence Nationale de la Recherche, ANR (França), Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (Brasil) e Japan Science and Technology Agency, JST (Japão).

Développez et utilisez des pratiques de “Science Ouverte” pour votre laboratoire/équipe de recherche. Utilisez ces conseils pour améliorer les pratiques de gestion des données et des logiciels de votre équipe afin de favoriser la science ouverte. Inscrivez-les dans la charte de pratiques “Science Ouverte” de votre équipe. Ce travail a été réalisé dans le cadre du projetPARSEC (Building New Tools for Data Sharing and Re-use through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas)financé par le Forum Belmont via la National Science Foundation, NSF, Grant 1929464, (USA), l'Agence Nationale de la Recherche, ANR (France), la Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (Brésil), et la Japan Science and Technology Agency, JST (Japon).

Veillez à bien documenter, archiver à long terme et à rendre librement accessiblestous les objets numériques créés ou utilisés par l'équipe. Ce travail a été réalisé dans le cadre du projetPARSEC (Building New Tools for Data Sharing and Re-use through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas)financé par le Forum Belmont via la National Science Foundation, NSF, Grant 1929464, (USA), l'Agence Nationale de la Recherche, ANR (France), la Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (Brésil), et la Japan Science and Technology Agency, JST (Japon).

あなたのソフトウェアをきちんと計画し、管理、共有しましょう。このチェックリストを活用して、ソフトウェアの管理と共有の方法を改善しましょう。 この研究は、「自然保護区が社会経済に及ぼす影響の多国融合研究を通じた新たなデータ共有・再利用手法の構築(PARSEC)」プロジェクトの一部であり、ベルモント・フォーラムより、全米科学財団(NSF、Grant 1929464)、フランス国立研究機構(ANR)、ブラジルサンパウロ州研究財団(FAPESP)、日本科学技術振興機構(JST)を通じた資金提供を受けています。 The Japanese characters on the attached PDFare not displaying in the Zenodopreview window correctly. Please download the PDF to view the checklist. Zenodo上で日本語フォントが表示されない場合がありますが、ダウンロードしたPDFでは正常に表示されます。

研究データを計画し、管理し、共有しましょう。このチェックリストを活用して、データ管理と共有方法を改善しましょう。 この研究は、「自然保護区が社会経済に及ぼす影響の多国融合研究を通じた新たなデータ共有・再利用手法の構築(PARSEC)」プロジェクトの一部であり、ベルモント・フォーラムより、全米科学財団(NSF、Grant 1929464)、フランス国立研究機構(ANR)、ブラジルサンパウロ州研究財団(FAPESP)、日本科学技術振興機構(JST)を通じた資金提供を受けています。 Important Note: The Japanese characters on the attached PDFare not displaying in the Zenodopreview window correctly. Please download the PDF to view the checklist. Zenodo上で日本語フォントが表示されない場合がありますが、ダウンロードしたPDFでは正常に表示されます。

このチェックリストを活用して、あなたの研究をデータやソフトウェア、機関などに関連付けていけば、あなたの「デジタルプレゼンス」はより高まり、将来の共同研究者やパートナーとなるかもしれない人々への認知度が向上します。また、あなたの研究が利用された場合には学術貢献としての評価にもつながります。 この研究は、「自然保護区が社会経済に及ぼす影響の多国融合研究を通じた新たなデータ共有・再利用手法の構築(PARSEC)」プロジェクトの一部であり、ベルモント・フォーラムより、全米科学財団(NSF、Grant 1929464)、フランス国立研究機構 (ANR)、ブラジル サンパウロ州研究財団(FAPESP)、日本科学技術振興機構(JST)を通じた資金提供を受けています。 Important Note: The Japanese characters on the attached PDFare not displaying in the Zenodopreview window correctly. Please download the PDF to view the checklist. Zenodo上で日本語フォントが表示されない場合がありますが、ダウンロードしたPDFでは正常に表示されます。 Version 2: There were no changes to the text. The PDF was recreated in order to resolve the issue with the preview window not displaying most of the Japanese characters.

This presentation was given at a session organised by the ESIP/RDA Earth, Space, Environmental Sciences Interest Group for the RDA Plenary 20 in Gothenburg, Sweden on the 23th March 2023 entitled 'Continuing the Circle of Life of Earth and Environmental Science Data Infrastructure Projects: Maturing PARSEC and ENVRI-FAIR, Beginning New Adventures…'. In this presentation, we cover the broad achievements of our Belmont-funded project, PARSEC (Building new tools for data sharing and re-use through a transnational investigation of the socio-economic impacts of protected areas), discuss some challenges we have encountered and how we have overcome them, and our next steps., This research is product of the PARSEC group funded by the Belmont Forum as part of its Collaborative Research Action (CRA) on Science-Driven e-Infrastructures Innovation (SEI), with funding from FAPESP, JST, ANR, and NSF and support from the synthesis centre CESAB of the French Foundation for Research on Biodiversity and our many partners. The link to the talk within the session is here: https://youtu.be/I3YBPEYFXYg?t=2148

This presentation is the PARSEC-based contribution to a Birds of a Feather session at RDA P20 in March 2023. This session aimed to: discuss research software visibility highlight to the audience existing mechanisms to make code visible, and collect community input about other mechanisms and future coordinated actions. Our presentation focussed on the challenges related to achieving reproducibility and replicability of Deep Learning (DL) experiments, drawing on the developed protocol for checking the veracity of a DL paper published by Machicao et al. (2022) doi:10.1029/2022EA002379, This research is product of the PARSEC group funded by the Belmont Forum as part of its Collaborative Research Action (CRA) on Science-Driven e-Infrastructures Innovation (SEI), + synthesis centre CESAB of the French Foundation for Research on Biodiversity. The link to the session page is here: https://www.rd-alliance.org/coordinating-mechanisms-making-code-visible

Planifiez, gérez et partagez vos données. Utilisez cette check-liste pour améliorer vos pratiques de gestion et de partage des données. Ce travail a été réalisé dans le cadre du projetPARSEC (Building New Tools for Data Sharing and Re-use through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas)financé par le Forum Belmont via la National Science Foundation, NSF, Grant 1929464, (USA), l'Agence Nationale de la Recherche, ANR (France), la Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (Brésil), et la Japan Science and Technology Agency, JST (Japon).

Connectez votre recherche à vos données, logiciels, institutions et plus encore. Utilisez cette check-liste pour optimiser votre présence numérique, accroître la découverte de votre travail par des collaborateur.rices et partenaires potentiels, et être reconnu.e comme auteur.e lorsque d'autres utilisent votre travail. Ce travail a été réalisé dans le cadre du projet PARSEC (Building New Tools for Data Sharing and Re-use through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas) financé par le Forum Belmont via la National Science Foundation, NSF, Grant 1929464, (USA), l'Agence Nationale de la Recherche, ANR (France), la Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (Brésil), et la Japan Science and Technology Agency, JST (Japon).

Conecte sua pesquisa aos seus dados, software, instituição e mais.Use este checklist para otimizar sua presença digital, melhorara descoberta do seu trabalho por potenciais colaboradores e parceiros para receber crédito quando outros usarem seu trabalho. Este trabalho é parte do projeto Desenvolvimento de Novas Ferramentas para o Compartilhamento e Reuso de Dados através de Pesquisa Transnacional sobre o Impacto Socioeconômico das Unidades de Conservação (PARSEC) financiado pelo Belmont Forum por meio da National Science Foundation, NSF, Grant 1929464, (EUA), Agence Nationale de la Recherche, ANR (França), Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (Brasil) e Japan Science and Technology Agency, JST (Japão).

Planeje, gerencie e compartilhe seus dados.Use este checklist para otimizar sua gestão de dados e práticas de compartilhamento. Este trabalho é parte do projeto Desenvolvimento de Novas Ferramentas para o Compartilhamento e Reuso de Dados através de Pesquisa Transnacional sobre o Impacto Socioeconômico das Unidades de Conservação (PARSEC) financiado pelo Belmont Forum por meio da National Science Foundation, NSF, Grant 1929464, (EUA), Agence Nationale de la Recherche, ANR (França), Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (Brasil) e Japan Science and Technology Agency, JST (Japão).

Planifique, administre y comparta su software. Utilice esta lista de verificación para optimizar sus prácticas de administración y uso compartido de software. Este trabajo es parte del proyectoCreación de nuevas herramientas para compartir y reutilizar datos a través de una investigación transnacional de los impactos socioeconómicos de las áreas protegidas (PARSEC)financiado por Belmont Forum a través de National Science Foundation, NSF, Grant 1929464, (EE.UU), Agence Nationale de la Recherche, ANR (Francia), Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (Brasil) y Japan Science and Technology Agency, JST (Japón).

Conecte su investigación a sus datos, software, institución y más.Use esta lista de verificación para optimizar su presencia digital, aumentar la visibilidad de su trabajo para posibles colaboradores y socios, y para recibir crédito cuando otros usen su trabajo. Este trabajo es parte del proyecto Creación de nuevas herramientas para compartir y reutilizar datos a través de una investigación transnacional de los impactos socioeconómicos de las áreas protegidas (PARSEC) financiado por Belmont Forum a través de National Science Foundation, NSF, Grant 1929464, (EE.UU), Agence Nationale de la Recherche, ANR (Francia), Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (Brasil) y Japan Science and Technology Agency, JST (Japón).

Planeje, gerencie e compartilhe seu software. Use este checklist para otimizar a gestão de seu software e práticas de compartilhamento. Este trabalho é parte do projetoDesenvolvimento de Novas Ferramentas para o Compartilhamento e Reuso de Dados através de Pesquisa Transnacional sobre o Impacto Socioeconômico das Unidades de Conservação (PARSEC)financiado pelo Belmont Forum por meio da National Science Foundation, NSF, Grant 1929464, (EUA), Agence Nationale de la Recherche, ANR (França), Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (Brasil) e Japan Science and Technology Agency, JST (Japão).

Planifique, gestione y comparta sus datos.Utilice esta lista de verificación para optimizar sus prácticas de gestión e intercambio de datos. Este trabajo es parte del proyectoCreación de nuevas herramientas para compartir y reutilizar datos a través de una investigación transnacional de los impactos socioeconómicos de las áreas protegidas (PARSEC)financiado por Belmont Forum a través de National Science Foundation, NSF, Grant 1929464, (EE.UU), Agence Nationale de la Recherche, ANR (Francia), Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (Brasil) y Japan Science and Technology Agency, JST (Japón).

On the occasion of the tenth anniversary of the RDA, and as we approach the end of our Belmont-funded project, PARSEC (www.parsecproject.org), we felt it was time to discuss the practicalities of collaboration, data and code sharing for publication and re-use across the globe. The PARSEC team–from five geographically-dispersed countries–has collaborated for four years on the collation and harmonisation of data and the development of new methods for sharing data and code as we investigate the socio-economic effects of nature conservation initiatives. We have had very profitable partnerships in this endeavour with several leading data infrastructure and research tool providers, including ORCID, DataCite, the RDA itself, and the World Data System. In this session representatives of the key data science infrastructures (ORCID, Scholix, Crossref, DataCite, the WDS, the Environmental Data Initiative) and users (representing the voice of marine conservation, machine learning, data for artificial intelligence, health and life sciences, social inequalities in health, the geoscience community and domain variations in open science and open data) discuss the challenges they face and their vision of the optimum path to the future., This project was conducted as part of the Belmont Forum PARSEC project, funded under the Collaborative Research Action (CRA) on Science-Driven e-Infrastructures Innovation (SEI), with funding from FAPESP, the ANR, JST and the NSF, with collaborators from Australia, and support from the synthesis centre CESAB of the French Foundation for Research on Biodiversity.

En los últimos años, se han acelerado los esfuerzos para desarrollar buenas prácticas de investigación, y las cuestiones de Reproducibilidad y Replicabilidad (R & R) en los experimentos de ciencias de la computación han ganado atención. Debido a la complejidad de los enfoques utilizados, la reproducibilidad de los experimentos de aprendizaje profundo (Deep Learning, DL) es un reto particular. Sin embargo, sin el uso de la tecnología de DL, sería imposible resolver problemas que requieren la adquisición y el análisis de enormes cantidades de datos en el espacio y el tiempo. Los futuros usuarios podrían beneficiarse en gran medida de la experiencia de crear y ejecutar tales experimentos, y habría que superar los retos de reproducibilidad de los experimentos de DL. Para investigar la naturaleza de los retos de reproducibilidad de la DL en detalle y encontrar soluciones que puedan mejorar su reproducibilidad, evaluamos tres estudios de DL que analizaron elementos visuales de imágenes de satélite y de calles para estimar los niveles de pobreza en los asentamientos humanos. Detallamos sistemáticamente las dificultades con los conjuntos de datos, los métodos y los flujos de trabajo de cada experimento e identificamos acciones para combatir los problemas comunes. Nuestro público objetivo incluye a los investigadores que quieren replicar un experimento, a los autores que quieren informar sobre un experimento y a los revisores que quieren evaluar los experimentos de DL. Como resultado de esta evaluación, hemos desarrollado dos enfoques para mejorar la reproducibilidad y replicabilidad de los experimentos de DL: (i) una lista de comprobación de la calidad que incorpora, entre otros, los principios FAIR pertinentes, y (ii) directrices para reducir la probabilidad de que se desperdicien esfuerzos a la hora de decidir si un experimento puede reproducirse de forma viable., Acknowledgments: The PARSEC project is funded by the Belmont Forum, Collaborative Research Action on Science-Driven e-Infrastructures Innovation. J.M. is grateful for the support from FAPESP (grant 2020/03514–9).

Develop and use Open Science practices for your lab or research team. Improve your team’s data and software management practices. Codify these practices in your team’s Code of Open Science Practice. This guideline in: English, French, Japanese, Portuguese,Spanish[links to be added] Your Journey to Open Science in: English, French,Japanese, Portuguese,Spanish This work is part of theBuilding New Tools for Data Sharing and Re-use through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas (PARSEC)project with fundingprovided by the Belmont Forum through the National Science Foundation, NSF, Grant1929464, (US), Agence Nationale de la Recherche,ANR (France), Fundação de Amparo à Pesquisa do Estado de São Paulo,FAPESP (Brazil), and Japan Science and Technology Agency,JST (Japan). Version 2 includes minor text updates adding clarification, updated links, and funder logos.&nbsp

This talk was part of theNovember Narratives Twin-Talks - Reproducibility of ML Experiments and National Research Graph on 17 Nov 2022. It provides some insights from the paper ‘Mitigation Strategies to Improve Reproducibility of Poverty Estimations from Remote Sensing Images Using Deep Learning’ Machicao, J., Ben Abbes, A., Meneguzzi, L., Corrêa, P.L.P., Specht, A., David, R., Subsol, G., Vellenich, D., Devillers, R., Stall, S., Mouquet, N., Chaumont, M., Berti‐Equille, L., Mouillot, D., 2022. Mitigation Strategies to Improve Reproducibility of Poverty Estimations From Remote Sensing Images Using Deep Learning. Earth and Space Science 9.https://doi.org/10.1029/2022EA002379 You can watch the talk on: https://www.youtube.com/watch?v=gAmL40vtcTg This research is product of the PARSEC group funded by the Belmont Forum as part of its Collaborative Research Action (CRA) on Science-Driven e-Infrastructures Innovation (SEI) with support from FAPESP, JST, ANR and NSF, and thesynthesis centre CESAB of the French Foundation for Research on Biodiversity.&nbsp

In recent years efforts to develop good research practices have accelerated, and issues of Reproducibility and Replicability (R & R) in computer science experiments have gained attention. Due to the complexity of the approaches used, the reproducibility of deep learning (DL) experiments is a particular challenge. Without the use of DL technology, however, it would be impossible to solve problems which require the acquisition and analysis of enormous amounts of data across space and time. Future users could greatly benefit from the experience of creating and running such experiments, and the challenges to reproducibility of DL experiments should be overcome. To investigate the nature of DL reproducibility challenges in detail and to find solutions that might improve their reproducibility, we assessed three DL studies which analysed visual elements from satellite and street imagery to estimate poverty levels in human settlements. We systematically detailed difficulties with the datasets, methods, and workflows for each experiment and identified actions to militate common problems. Our target audience includes researchers who want to replicate an experiment, authors who want to report on an experiment, and reviewers who want to evaluate DL experiments. As a result of this assessment, we have developed two approaches to improve the reproducibility and replicability of DL experiments: (i) a quality checklist incorporating the relevant FAIR principles inter alia, and (ii) guidelines to reduce the likelihood of wasted effort when deciding whether an experiment is able to be reproduced in a feasible manner.

PARSEC Scientific results: Methodology and Case Studies. This session at the University of Sao Paulo's 6th Data Science Symposium was a presentation of PARSEC results to date. The organisers are grateful for funding from IUGS and UNESCO to support the attendance of early career researchers. • PARSEC execution. Introduction to the application of the PARSEC synthesis model with case studies from East Africa. Ali Ben Abbes (Foundation for Research on Biodiversity-CESAB) Research foci • Estimation of wealth index in Brazil: Pedro Luiz Pizzigatti Corrêa (Escola Politécnica da USP), Jeaneth Machicao and team • Socio-economic assessment and PAs in Australia: Alison Specht (University of Queensland, TERN), Germàn Gonzalez (AURIN), Michael Rigby (AURIN) • Mapping the socio-economic consequences of PAs in west coast USA: Jamie Trammell (Southern Oregon University) and Jeffrey Evans (The Nature Conservancy) • Pandemic effects on tourism: Yasuhisa Kondo (Research Institute for Humanity and Nature, RIHN)&nbsp

Plan, manage and share your data.Use this checklist to improve your data management and sharing practices. This work is part of theBuilding New Tools for Data Sharing and Re-use through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas (PARSEC)project with fundingprovided by the Belmont Forum through the National Science Foundation, NSF, Grant1929464, (US), Agence Nationale de la Recherche,ANR (France), Fundação de Amparo à Pesquisa do Estado de São Paulo,FAPESP (Brazil), and Japan Science and Technology Agency,JST (Japan). Version 2 includes an updated citation to the document.

Plan, manage and share your software.Use this checklist to improve your software management and sharing practices. This work is part of theBuilding New Tools for Data Sharing and Re-use through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas (PARSEC)project with fundingprovided by the Belmont Forum through the National Science Foundation, NSF, Grant1929464, (US), Agence Nationale de la Recherche,ANR (France), Fundação de Amparo à Pesquisa do Estado de São Paulo,FAPESP (Brazil), and Japan Science and Technology Agency,JST (Japan). Version 2 includes an updated citation to the document.

Species know no boundaries, and to solve thorny environmental problems you need a great variety of expertise and skill sets that do not exist in one person or even one place. As researchers, we build teams that include multiple disciplines, multiple languages, (potentially) multiple funders, and definitely different points of view. Building the team can seem challenging, but in truth the actual challenge is making sure everyone feels welcome and able to contribute. Teams that are good at communication and collaboration have developed practices that support an open science environment. These practices need to be put into place as a foundation when the team is forming, setting common expectations and tools. Teams with members from different countries and continents have additional considerations. These include the need to manage time zones, languages, and access to common tools. In this workshop we will use a workflow that can be applied to most research scenarios to enable an international, interdisciplinary team to establish the practices and tools needed for optimal opportunity to collaborate and contribute to the scientific question at hand. In short, this is a “do-it-yourself” (DIY) collaboration primer., Acknowledgments: The PARSEC project is funded by the Belmont Forum, Collaborative Research Action on Science-Driven e-Infrastructures Innovation. J.M. is grateful for the support from FAPESP (grant 2020/03514–9).

This presentation was part of a session at the SRI conference in Pretoria, June 2022. The session was entitled 'Transnational data interoperability, open workflows and reproducible science in sustainability and environmental change research' organised by the Belmont Forum CRA for Science-driven e-infrastructures innovation (SEI) for the enhancement of transnational, interdisciplinary and transdisciplinary data use in environmental change research. This presentation discusses the progress to date of the PARSEC project, the challenges, pitfalls and successes as they relate to data standards, transnational data interoperability that feed into automated workflows, data persistence, and how this data work leads to open workflows and reproducible science., PARSEC is a project sponsored by the Belmont Forum as part of its Collaborative Research Action (CRA) on Science-Driven e-Infrastructures Innovation (SEI), with funding from FAPESP, the ANR, JST and the NSF, with collaborators from Australia, and support from the synthesis centre CESAB of the French Foundation for Research on Biodiversity.

At a minimum, standardised, community accepted vocabularies are necessary for effective data discovery. Further, they can assist with large scale and reproducible analyses. There are some attempts to define the characteristics of a vocabulary which (a) make it most useful to a particular scientific domain, and (b) what characteristics will make it FAIR, i.e., that the vocabulary can be located ('Findability'), 'Accessibility, 'Interoperable' (or Interpretable), and 'Reusable' (e.g. Laporte et al., 2021, David et al., 2021). What do we need to do in order to make these vocabularies appealing to scientists, i.e. to the creators of research data? What do we (semantic repository curators, data managers, developers and so on) need to do to improve the level of data FAIRness, and ensure that datasets are tagged with appropriate, sufficient and community-approved controlled vocabularies? What mechanisms and tools best capture essential metadata in the physical or digital lab before they reach the data repositories? This session at SciDataCon2022 organised by the PARSEC team of Specht, David, O'Brien and Stall, focussed on how the environmental and earth science researcher might better be able to apply vocabularies that are controlled, standardised, and persistent (sustained for at least 5 years into the future) to help maintain the integrity of their datasets. Our keynote speaker, Dr Gwenaëlle Moncoiffé introduced the challenges and potential solutions to making vocabularies more FAIR and also more accessible to researchers, followed by contributions from members of the wider community (Brownlee, Horsburgh and Sparkes), solicited specifically to help us explore the options available. References David et al. (2021) https://doi.org/10.5281/zenodo.5749695 Laporte et al. (2021) https://doi.org/10.5281/zenodo.5594693, PARSEC is a project sponsored by the Belmont Forum as part of its Collaborative Research Action (CRA) on Science-Driven e-Infrastructures Innovation (SEI), with funding from FAPESP, the ANR, JST and the NSF, with collaborators from Australia, and support from the synthesis centre CESAB of the French Foundation for Research on Biodiversity

This presentation was given during the Japan Open Science Summit during Session E3: Open science, its discussions and practices in research projects., This presentation is a contribution from the PARSEC team. PARSEC is a project sponsored by the Belmont Forum as part of its Collaborative Research Action (CRA) on Science-Driven e-Infrastructures Innovation (SEI), with funding from FAPESP, the ANR, JST and the NSF, with collaborators from Australia, and support from the synthesis centre CESAB of the French Foundation for Research on Biodiversity.

Environmental science teams are characteristically multi-disciplinary, multi-national, and multi-organisational. The environmental and ecological challenges that face us make these characteristics unavoidable if the best open science is to be achieved. The importance of ensuring the terms that describe shared data, and indeed information, are well-understood and properly defined cannot be under-estimated, not just within the teams, but also when sharing the results with the world. Fundamental to the achievement of open science is sharing the data on which you base your work for others to use. For this you need to have your data, at the very least, understandable by others. Three approaches are proposed to improve the use of controlled, accepted, preferably multi-lingual, vocabularies, and these are discussed in the presentation, to provide exemplars, to encourage early alignment with an intended repository (TRUSTed of course), and to provide educational packages. It is recommended that these are all valuable, while taking care of any confounding factors, such as multi-linguality and researcher's ability to efficiently discover suitable vocabularies for them to use or contribute to., This presentation is a contribution from the PARSEC team. PARSEC is a project sponsored by the Belmont Forum as part of its Collaborative Research Action (CRA) on Science-Driven e-Infrastructures Innovation (SEI), with funding from FAPESP, the ANR, JST and the NSF, with collaborators from Australia, and support from the synthesis centre CESAB of the French Foundation for Research on Biodiversity.

The challenges of Reproducibility and Replicability (R&R) have become a focus of attention in order to promote open and accessible research. Therefore, efforts have been made to develop good practices for R&R in the area of computer science. Nevertheless, Deep Learning (DL) based experiments remain difficult to reproduce by others due to the complexity of these techniques. In addition, several challenges concern the use of massive and heterogeneous data that contribute to the complexity of this R&R. Firstly, we compiled three different aspects to help researchers to improve R&R. This compilation was based on machine learning checklists, guidelines, and principles from FAIR. Therefore, this compilation is useful for a (1) researcher seeking to reproduce a paper, (2) an author reporting on an experiment, and (3) a reviewer seeking to qualify the scientific contributions of the work. Secondly, we illustrate the compilation of three recent DL experiments for socio-economic estimation using remotely sensed data. Poster to be presented during RDA 19th Plenary Meeting, Part Of International Data Week, 20–23 June 2022, Seoul, South Korea, Acknowledgments: The PARSEC project is funded by the Belmont Forum, Collaborative Research Action on Science-Driven e-Infrastructures Innovation. J.M. is grateful for the support from FAPESP (grant 2020/03514–9).

Environmental science teams are characteristically multi-disciplinary, multi-national, and multi-organisational. The environmental and ecological challenges that face us make these characteristics unavoidable if the best open science is to be achieved. The importance of ensuring the terms that describe shared data, and indeed information, are well-understood and properly defined cannot be under-estimated, not just within the teams themselves, but also when sharing the results with the world. In a session run at SciDataCon in late 20211, several data-science specialists were asked to talk about their experience of how scientists engage with earth and environmental vocabularies and ontologies. This session highlighted the question of whether scientists truly appreciate the reason for using standardised vocabularies, and whether there were ways to improve their acceptance and hence practice. This would require several approaches. One might be to demonstrate the effectiveness of using a community-recognised, controlled vocabulary in enabling work to be discovered. Another might be to get researchers to identify and engage with a CoreTrustSeal2 accredited repository for their project data at the start of the project. Another is to create a range of educational packages, such as those introducing them to established vocabularies and ontologies. Overlay these challenges with language differences, we need to be more and more inventive. We must improve the situation for researchers and demonstrate to them that there is value in using standardised and accepted vocabularies and that this is an achievable part of their work, we miss fully benefitting from the wealth of expertise available to solve global problems and learn from each other. In this talk we shall expand on these options, showing where they might be most effectively introduced. 1Laporte, M.A., Guru, S., Archambeau, et al., 2021. Earth and Environmental vocabularies and ontologies today: how are they managed? How are they used by scientists? https://doi.org/10.5281/zenodo.5594693 2Lin, D., Crabtree, J., Dillo, et al. 2020. The TRUST Principles for digital repositories. Scientific Data 7, 1–5. https://doi.org/10.1038/s41597-020-0486-7,  This research is product of the PARSEC group funded by the Belmont Forum as part of its Collaborative Research Action (CRA) on Science-Driven e-Infrastructures Innovation (SEI) through research agencies, ANR (France), JST (Japan), FAPESP (Brazil) and NSF (USA) with the support of the synthesis centre CESAB of the French Foundation for Research on Biodiversity.

This document, prepared by the Data Strand of the PARSEC project funded by the Belmont Forum,provides guidelines for researchers about the importance and benefits of data repositories, the risks if you choose the wrong repository for your data, suggestions for selection of the right, TRUSTed repository and where to find them.

Although protected areas (PAs) are often considered the backbone of biodiversity conservation, their existence is tenuous, and the physical space they occupy is often highly contested. Both local and broader community needs must be supported to ensure the sustainability of PAs. Some might even argue that PAs should not only support, but enhance, the well-being of the communities that live around them. With renewed interest in protecting even more lands (i.e. 30 by 30 executive order), we need to better understand the socioeconomic context of existing, and future PAs. We present here some initial work that begins to address this need through a multi-national project called “Building New Tools for Data Sharing and Re-use through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas” (abbreviated to PARSEC). Hypothesizing that the relationship between PAs and local communities varies by ecoregion and regional economics, we highlight some of the geospatial approaches and constraints encountered to socio-ecologically characterize PAs in the context of local communities in order to statistically explore these important relationships., This research is product of the PARSEC group funded by the Belmont Forum as part of its Collaborative Research Action (CRA) on Science-Driven e-Infrastructures Innovation (SEI) with the National Science Foundation, inter alia, and supported by the synthesis centre CESAB of the French Foundation for Research on Biodiversity.

Addressing today���s complex, global research challenges, such as the threats posed by climate change and other environmental hazards associated with breaching our planetary health boundaries, has never been as urgent. Much of the data and information to tackle such problems is at our fingertips, the question is how to acquire, assemble and analyse diverse and complex information as expeditiously as possible to arrive at new understanding and recommendations for action or policies. Such cross-domain synthesis involves achieving four key components: (i) successful collaboration between experts and stakeholders across disciplinary, skillset, organisational and national boundaries, (ii) the availability of relevant, trusted, good quality data and code fit for use, (iii) access to the best infrastructure and tools available, wherever they may be, and (iv) openly-available results for critical review and future use. The presentation draws on examples from around the world to illustrate important key elements that can support effective collaboration, both at an individual and a team level. The means by which the researcher might identify good quality data and code for use is covered together with examples of the use of infrastructures of various sorts that may assist the team achieve their goal. The talk is given against the background of open science (UNESCO, 2021) and how the work of an individual and a project can be best presented within the academic community., this talk was given in the Wichita State University Disaster Resilience Analytics Center Seminar Series

Steady progress is being made across the Earth and space science disciplines to move towards more common sharing of the data and software that supports our research. As each discipline determines what is best for them, the experience gained by synthesis center communities is valuable to support the collaboration needed for cross-discipline teams and international efforts. In our work on the PARSEC project, the Data Leadership team at AGU is applying what we learn to the guidance and support we provide to our researchers. Here we will share this learning and our effort to prepare for a more transparent and reproducible future. Conference American Geophysical Union, Fall Meeting 2021 Session:IN52B: Connecting Disciplines and Data in Earth and Environmental Synthesis Research: Enabling International and Interdisciplinary Data Discovery, Integration, and Reuse II eLightning This work is part of theBuilding New Tools for Data Sharing and Re-use through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas (PARSEC)project with fundingprovided by the Belmont Forum through the National Science Foundation, Grant1929464.

Notre société est aujourd'hui confrontée à de profonds changements (biodiversité, climat, pandémie, etc.). Les impacts humains et leur atténuation dépendent de notre capacité à mobiliser la recherche au niveau mondial. Le développement durable de la société dépendra en grande partie du développement sur le long terme d’une science globale et d’outils de recherche scientifique, de leurs résultats et des écosystèmes de recherche partagés. Cette globalisation de la recherche nécessite d'inter-opérer nos systèmes d'observation et d'expérimentation pour mieux comprendre ces changements, pour mieux simuler leurs effets. La pandémie de Covid-19 fait désormais rage dans le monde. La reproductibilité de la recherche et des résultats à travers les régions dans des contextes différents devrait accélérer les réponses sociétales. Le partage des données et le développement de la recherche de synthèse avec agrégation de données à large échelle sont essentiels pour permettre de tels processus. L'utilisation partagée de connaissances formalisées, de vocabulaires,de normes et de procédures communes à grande échelle est nécessaire. Ce poster présente une méthodologie commune, un “livre de cuisine” de dictionnaire de données, qui propose une feuille de route pour construire des dictionnaires de données à grande échelle. L'objectif est de présenter les défis relevés lors de la construction de dictionnaires de données dans trois projets à l’échelle globale liés à la recherche sur la biodiversité et/ou les maladies infectieuses: PARSEC, Kakila, ERINHA-Advance. La version en anglais est ici:https://doi.org/10.5281/zenodo.4683066

Notre soci��t�� est aujourd'hui confront��e �� de profonds changements (biodiversit��, climat, pand��mie, etc.). Les impacts humains et leur att��nuation d��pendent de notre capacit�� �� mobiliser la recherche au niveau mondial. Le d��veloppement durable de la soci��t�� d��pendra en grande partie du d��veloppement sur le long terme d���une science globale et d���outils de recherche scientifique, de leurs r��sultats et des ��cosyst��mes de recherche partag��s. Cette globalisation de la recherche n��cessite d'inter-op��rer nos syst��mes d'observation et d'exp��rimentation pour mieux comprendre ces changements, pour mieux simuler leurs effets. La pand��mie de Covid-19 fait d��sormais rage dans le monde. La reproductibilit�� de la recherche et des r��sultats �� travers les r��gions dans des contextes diff��rents devrait acc��l��rer les r��ponses soci��tales. Le partage des donn��es et le d��veloppement de la recherche de synth��se avec agr��gation de donn��es �� large ��chelle sont essentiels pour permettre de tels processus. L'utilisation partag��e de connaissances formalis��es, de vocabulaires, de normes et de proc��dures communes �� grande ��chelle est n��cessaire. Ce poster pr��sente une m��thodologie commune, un ���livre de cuisine��� de dictionnaire de donn��es, qui propose une feuille de route pour construire des dictionnaires de donn��es �� grande ��chelle. L'objectif est de pr��senter les d��fis relev��s lors de la construction de dictionnaires de donn��es dans trois projets �� l�����chelle globale li��s �� la recherche sur la biodiversit�� et/ou les maladies infectieuses: PARSEC, Kakila, ERINHA-Advance. La version en anglais est ici: https://doi.org/10.5281/zenodo.4683066

The Environmental and Earth sciences are faced with complex and dynamic challenges: they cannot be solved in isolation from each other or from other sciences, such as the social and health sciences. Researchers who tackle real world challenges must have data that are easy to use and understandable in a variety of contexts. An important element of understanding these data is the use of well-organised, community-accepted terminology for data. With the increasing uptake of the semantic web and the demand for open data, data descriptors of varying quality and technical maturity are proliferating (e.g. ESIP/RDA Earth, Space and Environmental Sciences Interest Group (ESES-IG), Semantic Resource Catalogue, 2021). These range from simple lists, through thesauri and taxonomies, to formalised ontologies. This proliferation can be extremely confusing for the practitioner (data creator or re-user), as there are no agreed criteria to support a selection decision. In addition, the variety of descriptors in the community stunts machine discovery. Elements such as community-endorsement and sustainability of terminology are essential to enable open scientific practice. Session 320 at the Virtual SciDataCon2021 presented seven experts with a range of approaches to effectively using heterogeneous environmental and earth science terms. The speakers discussed several approaches to the use of heterogeneous environmental and earth science terms, and they shared their learned experience of vocabulary implementation, the challenges they have encountered in real-world use, and their various solutions. Seven key points were highlighted in a final wrap-up of the session. After an introduction by Alison Specht (TERN, University of Queensland) on behalf of the organising team of Romain David, Margaret O'Brien, Shelley Stall and Lesley Wyborn), the speakers and their subjects were as follows: Marie Angélique Laporte (Alliance of Biodiversity International and CIAT, Switzerland), Vocabularies: How to link them and what they are-ontology flavour. Siddeswara Guru (TERN, Australia), Semantic challenges in Environmental and Earth vocabularies in Australia for an infrastructure serving a heterogeneous community. Anne-Sophie Archambeau (IRD/UMS PatriNat/ GBIF France), The long history of DarwinCore. Yvan le Bras (PNDB, MNHN, France), Vocabularies for a national biodiversity e-infrastructure. Wim Hugo (SAEON, South Africa, now at the Dutch national centre of expertise and repository for research data, DANS), Challenges and Opportunities - Semantic Interoperability and Developing Countries. Yann Le Franc (FAIRsFAIR), An overview of the FAIRsFAIR project and our work on FAIR Semantics. Barbara Magagna (Environment Agency Austria), Interoperability via the application of the I-ADOPT Framework. Shelley Stall (AGU), provided the session wrap-up., https://www.scidatacon.org/virtual-2021/sessions/320/

Connect your research to your data, software and institution. Use this checklist to optimize your digital presence, increase discovery of your work to potential collaborators and partners, and receive credit when others use your work. This work is part of the Building New Tools for Data Sharing and Re-use through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas (PARSEC) project with funding provided by the Belmont Forum through the National Science Foundation, Grant 1929464 as well as the Accelerating Open and FAIR Data Practices Across the Earth, Space, and Environmental Sciences: A Pilot with the NSF to Support Public Access to Research Data project funded by the National Science Foundation, Grant 2025364. Version 2 includes minor text updates adding clarification, updated links, and funder logos. Version 3 includes updated template and links to new data and software checklists. Version 4 includes an updated citation to the document., {"references":["Edmunds, Rorie, Specht, Alison, Stall, Shelley, David, Romain, Mabile, Laurence, O'Brien, Margaret, Murayama, Yasuhiro, Correa, Pedro, Machicao, Jeaneth, & Miyairi, Nobuko. (2022). Repository Guidelines. Zenodo. https://doi.org/10.5281/zenodo.6542494","Katz DS, Chue Hong NP, Clark T et al. Recognizing the value of software: a software citation guide [version 2; peer review: 2 approved]. F1000Research 2021, 9:1257 (https://doi.org/10.12688/f1000research.26932.2)"]}

This presentation was the core for a session held on 30July 2021 AESTin theFSCI2021 course T20: Case studies in the Earth Sciences: current approaches to publishing, data and computation. FSCI2021 washeld from 26 July to 5 August2021. It is important that the data used in a research project is as properly managed as the research. Most funders now require that a data management plan (DMP) is created and maintained in each project that describes how the data will be treated throughout the research life cycle. Journals are increasingly requiring that the data supporting a project are published alongside the publication, and that the published data are Finable, Accessible, Interoperable and Reusable (the FAIR principles). These requirements mean researchers need to have appropriate knowledge and skills to ensure their data comply with these demands. The session is in three parts: an introduction to the Data and Digital Objects Management Plan showing linkage with the research life cycle. We link to examples of DMPs from the earth and environmental sciences. In the second part of the session, we breakdown the dynamic DMP and how you can manage that through a checklist of steps and actions. In the last component of the session we show how FAIR principles can be incorporated. In all instances we providelinks to standards and tools available to assist the user to complete and maintain good data management from the start of the research (or project) lifecycle to publication.&nbsp

A presentation at the Japan Open Science Summit 2021 about the challenges to data challenges that have emerged in the Belmont Forum funded PARSEC project "Building new tools for data sharing and re-use through a transnational investigation of the socio-economic impacts of protected areas (PARSEC)". The two aspects covered in the talk are(1) challenges to working together across large geographic boundaries and some of the practical responses to those, and emerging challenges related to (2) Blending data of different scales.

Instruments for the conservation of biodiversity are frequently seen as counter productive for human life and prosperity. This session will provide a basis for lively discussion and knowledge-sharing using evidence-based science, the incorporation of indigenous values, and insights from practice to achieve good biodiversity conservation outcomes within global Sustainable Development Goals. Our five speakers offer different perspectives and solutions around the socio-economic factors influencing practical community and government attitudes and actions, the value of indigenous involvement, and the opportunities arising from technological innovation and knowledge-sharing. All speakers have been directly involved with members of their community affected by conservation actions. We ask the following questions to focus our discussion: ��� does the human community ���own��� biodiversity conservation enough to make it sustainable in the long-term? What can we learn from each other? ��� Is the cost of conservation (perceived as) too high and how can we change this? ��� Are we able to really measure anything effectively (i.e. are the costs and benefits real or just believed) to know how we are going? ��� How can we better engage the community to invest in conservation action and benefit from it, not just talk about it? ��� what innovative solutions and new tools are most promising? ��� can open science and open data initiatives help? A selection of key challenges and solutions will be presented by researchers and practitioners. The speakers offer different perspectives, from that of globally-scoping research scientists and nature conservation agency staff, to an expert in citizen science and data management. The session is facilitated by members of PARSEC (Building New Tools for Data Sharing and Reuse through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas), a Belmont Forum- funded international project with partners from Brazil, France, Japan, the United States of America, and collaborators in the UK and Australia., link to session: https://attend.sri2021.org/meetings/virtual/g8ReYPgH4heqiRcSy