33 datasets found
  1. H

    GIS Data Layers

    • dataverse.harvard.edu
    • search.dataone.org
    Updated Sep 27, 2016
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    Harvard Planning & Project Management (HPPM) (2016). GIS Data Layers [Dataset]. http://doi.org/10.7910/DVN/CKYCHU
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 27, 2016
    Dataset provided by
    Harvard Dataverse
    Authors
    Harvard Planning & Project Management (HPPM)
    License

    https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/CKYCHUhttps://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/CKYCHU

    Description

    The GIS data maintained by HPPM includes information on buildings and grounds related to Harvard University. Our "standard" base layers are available to Harvard affiliates and their service providers (for example, architects) working on Harvard projects in AutoCAD DWG, ESRI SHP or File Geodatabase format. Additional datasets are sometimes available by special arrangement. http://home.hppm.harvard.edu/pages/gis-data-layers

  2. d

    Online Appendix to Securities Auctions with Pre-project Information...

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    • dataverse.harvard.edu
    Updated Nov 8, 2023
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    Wong, Tak-Yuen; Wong, Crystal Ho-Po (2023). Online Appendix to Securities Auctions with Pre-project Information Management [Dataset]. https://search.dataone.org/view/sha256%3A142f977e3166319bab12fb9606c48ef5eab49f4ea1079d59dd73656ccc84bfc6
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    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Wong, Tak-Yuen; Wong, Crystal Ho-Po
    Description

    The file contains a characterization of the welfare-maximizing mechanism and a simplified model with an ex-ante private signal for the paper "Securities Auctions with Pre-project Information Management".

  3. H

    WxEM Wave 1

    • dataverse.harvard.edu
    • dataone.org
    • +1more
    Updated Mar 17, 2025
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    Anna Wanless; Sam Stormer; Andrew Fox; Joseph Ripberger; Makenzie Krocak; Abby Bitterman; Carol Silva; Hank Jenkins-Smith (2025). WxEM Wave 1 [Dataset]. http://doi.org/10.7910/DVN/8VUUDA
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 17, 2025
    Dataset provided by
    Harvard Dataverse
    Authors
    Anna Wanless; Sam Stormer; Andrew Fox; Joseph Ripberger; Makenzie Krocak; Abby Bitterman; Carol Silva; Hank Jenkins-Smith
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This report describes the results of a regularly distributed survey of nationwide Emergency Managers as part of The Extreme Weather and Emergency Management Survey (WxEM) series. This project aims to send surveys to Emergency Managers across the United States three to four times a year, although that frequency may change based on Emergency Manager and research needs. The Extreme Weather and Emergency Management Survey, Wave 1 (WxEM Wave 1) was designed and administered by the Institute for Public Policy Research and Analysis (IPPRA) at the University of Oklahoma. It is the first survey in the series (Stormer et al. 2023 and Wanless et al. 2023). WxEM Wave 1 opened on August 4, 2022, using an online questionnaire that as of this writing has been completed by 720 Emergency Management personnel that were contacted from an IPPRA built database of Emergency Managers from across the country (see Stormer et al. 2023 for more information on the database). WxEM Wave 1 was designed to recruit participants for the WxEM project. This survey gathers demographic information on enrollees, including location, jurisdiction type, and experience. This report presents an overview of the methodology of the survey data collection, and a reproduction of the survey instrument with frequencies for the questions that elicited numeric responses. Because this survey acts as an enrollment tool, it will remain open, and results may vary as participants are added and removed. Future reference reports will update the number of participants as the project continues.

  4. H

    Epistemic Market Object-JM

    • dataverse.harvard.edu
    Updated Mar 14, 2024
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    Zeynep Arsel (2024). Epistemic Market Object-JM [Dataset]. http://doi.org/10.7910/DVN/8VXSYT
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 14, 2024
    Dataset provided by
    Harvard Dataverse
    Authors
    Zeynep Arsel
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This database includes all secondary data used in the project "Epistemic Market Objects: How Sponsored Content Disrupted Marketing." It includes data from Adweek, podcast data, and third-party platform reviews. Unfortunately, we are not allowed to share primary interviews as we signed a confidentiality form with participants. This form is also protected by the Canadian Tri-Council for Research.

  5. T

    Replication Data for: Review of Classification of Key Competencies for CPM...

    • dataverse.tdl.org
    pdf
    Updated Oct 18, 2020
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    Fatemeh Pariafsai; Fatemeh Pariafsai (2020). Replication Data for: Review of Classification of Key Competencies for CPM (ASCE 1988-2019) October 2020 [Dataset]. http://doi.org/10.18738/T8/KBYA5W
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    pdf(2318594)Available download formats
    Dataset updated
    Oct 18, 2020
    Dataset provided by
    Texas Data Repository
    Authors
    Fatemeh Pariafsai; Fatemeh Pariafsai
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This technical report aims to provide detailed information on the results of Stage I of the methodology used to find references that are potentially relevant to the topic “Classification of Key Competencies for Construction Project Management.” In Stage I, potentially relevant references were searched using the American Society of Civil Engineers (ASCE) Library. In the ASCE Library, “advanced search” was used to find applicable references via specific search terms, topics and publication dates. For topics, the term “construction” was used. The option “title” was checked to specify where to look for search terms. The search terms used included competencies, competence, skill, capability, knowledge, project manager, project management, construction management, and engineering management. For more representative results, the search was restricted to references inclusively published from 1988 to 2019. When more than one chapter of a book was found, instead of counting all the chapters found, the book was counted as one single reference. In such cases, the book title might exclude all the search terms used. If the same reference was found under different search terms, it was numbered only one time when counting the total number of references initially found. This process resulted in 2,102 references retrieved from the ASCE Library (Table 1 to Table 16). In the following Tables, “Selected: Yes” indicates that the initially-retrieved reference was ultimately selected for content analysis, and “Selected: No” means that the reference was not selected for content analysis.

  6. T

    Replication Data for: Comparing Teamwork & Collaboration Competencies...

    • dataverse.tdl.org
    pdf +2
    Updated Nov 6, 2020
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    Martin Wallace; Martin Wallace; Ryan Hulla; Ryan Hulla (2020). Replication Data for: Comparing Teamwork & Collaboration Competencies between a Technology in Art Education course and an Engineering Project Management Course [Dataset]. http://doi.org/10.18738/T8/NTHYEE
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    pdf(599974), txt(5599), text/comma-separated-values(14987)Available download formats
    Dataset updated
    Nov 6, 2020
    Dataset provided by
    Texas Data Repository
    Authors
    Martin Wallace; Martin Wallace; Ryan Hulla; Ryan Hulla
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This data was collected over two academic years, 2018/19 and 2019/20 from students enrolled in two courses at UTA: ART 4365 Technology in Art Education and IE 4340 Engineering Project Management. The data collection instruments were pre- and post-self assessment surveys, distributed at the beginning and end of the semester. The data includes student-self reported competencies for Maker Competencies 9 and 10, "Assembles Effective Teams" and "Collaborates Effectively" on a range of 1 (low) to 5 (high).

  7. d

    Kickstarter Structured Relational Database

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    • dataverse.harvard.edu
    • +1more
    Updated Nov 22, 2023
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    LI, GUAN-CHENG (2023). Kickstarter Structured Relational Database [Dataset]. http://doi.org/10.7910/DVN/EOYBXM
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    Dataset updated
    Nov 22, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    LI, GUAN-CHENG
    Description

    Relational SQLite Database Tables of Kickstarter, including projects, creators, funders, comments, geography, pledge and funding, etc.

  8. D

    ABC metadata project

    • dataverse.nl
    pdf
    Updated May 1, 2023
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    Joost Daams; Evelien van der Schaaf - de Wolf; Joost Daams; Evelien van der Schaaf - de Wolf (2023). ABC metadata project [Dataset]. http://doi.org/10.34894/T80BZM
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    pdf(473728)Available download formats
    Dataset updated
    May 1, 2023
    Dataset provided by
    DataverseNL
    Authors
    Joost Daams; Evelien van der Schaaf - de Wolf; Joost Daams; Evelien van der Schaaf - de Wolf
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    In the ABC metadata project, a metadata model with mappings to international standards and a translation to JSON is developed. The ABC metadata model (Amsterdam UMC Biomedical Concise metadata model) is a model that provides guidance for minimal metadata implementation in the field of biomedicine. Being a model implies that it isn’t a ready-made solution, but it offers guidance to what metadata is needed for FAIR data management. It is deliberately kept minimal, thus facilitating data exchange between persons and systems, reducing workload for researchers and support alike. It could be considered to be a checklist at the utmost minimum of items that should be reported for FAIR data management. It aims to bridge the gap between generic metadata standards and detailed metadata generated by man and machine in the field of biomedicine, covering all disciplines in health and life sciences. The ambition of the development team is that the ABC metadata model eventually will grow to become a standard by wide adaptation in FAIR RDM practice, collective collaboration in improvement of the model and community endorsement.

  9. d

    Data from: The Startup Cartography Project

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    • dataverse.harvard.edu
    Updated Nov 22, 2023
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    Andrews, RJ; Fazio, Catherine; Guzman, Jorge; Liu, Yupeng; Stern, Scott (2023). The Startup Cartography Project [Dataset]. http://doi.org/10.7910/DVN/BMRPVH
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    Dataset updated
    Nov 22, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Andrews, RJ; Fazio, Catherine; Guzman, Jorge; Liu, Yupeng; Stern, Scott
    Time period covered
    Jan 1, 1988 - Dec 31, 2016
    Description

    Startup Cartography Project (http://www.startupcartography.com)

  10. H

    ILSSI/IFPRI study on irrigation, gender, and nutrition

    • dataverse.harvard.edu
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    Updated Oct 15, 2015
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    International Food Policy Research Intitute (IFPRI) (2015). ILSSI/IFPRI study on irrigation, gender, and nutrition [Dataset]. http://doi.org/10.7910/DVN/DH1O3J
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 15, 2015
    Dataset provided by
    Harvard Dataverse
    Authors
    International Food Policy Research Intitute (IFPRI)
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Dataset funded by
    United States Agency for International Development (USAID)
    Description

    The Feed the Future Innovation Lab on Small-Scale Irrigation (FTF-ILSSI) is a cooperative agreement funded by USAID under the Feed the Future program to undertake research aimed to increase food production, improve nutrition, accelerate economic development and contribute to the protection of the environment. The project seeks these objectives through identifying, testing and demonstrating technological options in small-scale irrigation and irrigated fodder, supported by a continual dialogue approach with stakeholders and capacity development toward sustained use of research approaches and evidence. Collaborators on this project include Texas A&M University, the International Water Management Institute (IWMI), the International Food Policy Research Institute (IFPRI), the International Livestock Research Institute (ILRI), North Carolina A&T State University (NCAT) and Texas A&M AgriLife Research (TAMUS). As part of this project, IFPRI is undertaking a study of irrigating and non-irrigating households in Tanzania, Ethiopia, and Ghana to investigate the connections between irrigation, gender, nutrition and health. The survey explores these linkages through an in-depth household questionnaire with questions on agricultural production, nutrition and health, a WEAI module and a community questionnaire. This work forms part of the CGIAR Research Program on Water, Land and Ecosystems (WLE).

  11. H

    Providing collateral and improving product market access for smallholder...

    • dataverse.harvard.edu
    Updated Jun 23, 2017
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    Prof Tavneet Suri (2017). Providing collateral and improving product market access for smallholder farmers: a randomized evaluation of inventory credit in Sierra Leone [Dataset]. http://doi.org/10.7910/DVN/2RIDLK
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 23, 2017
    Dataset provided by
    Harvard Dataverse
    Authors
    Prof Tavneet Suri
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Sierra Leone
    Description

    The title of the project was: "Providing collateral and improving product market access for smallholder farmers: a Randomized evaluation of inventory credit in Sierra Leone."

  12. d

    CFC data from WECT Project (Cohen et al. 2014, JPSP)

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    • dataverse.harvard.edu
    Updated Nov 22, 2023
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    Cohen, Taya (2023). CFC data from WECT Project (Cohen et al. 2014, JPSP) [Dataset]. https://search.dataone.org/view/sha256%3Ae7253dce6a795ccd66f40ffef6b35afc3234a63914b63b3c98499caa25ea69cb
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    Dataset updated
    Nov 22, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Cohen, Taya
    Description

    This SPSS file contains data from the 12-item Consideration of Future Consequences scale (Strathman, Gleicher, Boninger, & Edwards, 1994, JPSP), along with demographic information, from the WECT Project: Cohen, T. R. & Panter, A. T. (2011-2012). The WECT Project: Workplace experiences and character traits [project information]. Information available at: http://www.wectproject.org/ and on the Open Science Framework https://osf.io/w3hgr/. These data were first used in: Cohen, T. R., Panter, A. T., Turan, N., Morse, L. A., & Kim, Y. (2014). Moral character in the workplace. Journal of Personality and Social Psychology, 107(5), 943-963. doi: http://dx.doi.org/10.1037/a0037245

  13. d

    China's P2P project Data

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    Updated Nov 14, 2023
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    Ma, Zhengwei (2023). China's P2P project Data [Dataset]. http://doi.org/10.7910/DVN/RJTXC7
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    Dataset updated
    Nov 14, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Ma, Zhengwei
    Description

    China's P2P project Data.The project data has been privacy protected.

  14. d

    Experimental Data and Program Code for the picker routing problem in...

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    • dataverse.harvard.edu
    Updated Nov 12, 2023
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    Cano, Jose Alejandro (2023). Experimental Data and Program Code for the picker routing problem in multi-block high-level storage systems [Dataset]. https://search.dataone.org/view/sha256%3Af988917b27f7f1a8d921f363a765d4185aaa749f7904246d6ffd3226d01ae94a
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    Dataset updated
    Nov 12, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Cano, Jose Alejandro
    Description

    Experimental Data and Program Code in VBA for the picker routing problem in multi-block high-level storage systems using genetic algorithms and ACO

  15. H

    Skills needed for using social science research tools in natural resource...

    • dataverse.harvard.edu
    Updated Mar 22, 2017
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    Y.K. Nchanji; P. Levang; R. Jalonen (2017). Skills needed for using social science research tools in natural resource management: Personal experience from gender research in the “Beyond timber project” in Cameroon [Dataset]. http://doi.org/10.7910/DVN/SUEWWX
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 22, 2017
    Dataset provided by
    Harvard Dataverse
    Authors
    Y.K. Nchanji; P. Levang; R. Jalonen
    License

    https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/SUEWWXhttps://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/SUEWWX

    Time period covered
    Mar 2013 - Oct 2014
    Area covered
    Cameroon
    Description

    This dataset contains charts of different participatory research tools drawn from gender and age differentiated groups as well as results of focus group discussion carried out separately with men and women of different age groups. The study was conducted in five villages, three in the East Region and two in the South. Villages were selected based on their location (proximity to logging areas, easy access by road and to markets) and composition (similar size of village, different ethnic groups). Participatory research tools such as Seasonal Activity Calendar (SAC) and Access and Control Matrix (ACM) in addition to Focus Group Discussions (FGD)– were selected for comparison with household surveys used in the other components of the Beyond timber project to represent both quantitative and qualitative, and conventional and participatory research methods. Data collection with the use of each tool was done in gender and age segregated groups in each of the sampled communities. These participatory methods and tools were used to: - Examine participants’ knowledge differentiated by gender and age on the collection / gathering / harvesting, processing and management of forest resources and their uses (medicinal, cultural, domestic and social); - To bring together the knowledge of women and men from different ages to inform the broader project about how communities use, manage and benefit from forest resources. FGD segregated by gender and age were held in each community. A number of 5 -10 participants took part in the different group discussions. The participants were split into four groups: (i) younger women (15 - 35 years of age), (ii) older women (> 35 years of age), (iii) younger men (15 - 35 years of age) and older men (> 35 years of age). Gender and age were selected as analytical variables to ensure a wider range of experiences with respect to forest resources

  16. d

    Replication Data for: How and When Democratic Values Matter: Challenging the...

    • dataone.org
    • dataverse.harvard.edu
    Updated Nov 8, 2023
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    Liu, Yixin; Lee, Heewon; Berry, Frances (2023). Replication Data for: How and When Democratic Values Matter: Challenging the Effectiveness Centric Framework in Program Evaluation [Dataset]. http://doi.org/10.7910/DVN/FJ82VU
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    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Liu, Yixin; Lee, Heewon; Berry, Frances
    Description

    Performance information is overwhelmingly used in program evaluation by both public managers and external stakeholders. In the market-based New Public Management movement, effectiveness is public programs’ major selling point. However, this approach may marginalize the role of democratic values in governance. In the current complex society with anti-government sentiments, we embrace the idea of New Public Service to reiterate the importance of democratic values. Using a conjoint experiment, we compare the effects of effectiveness and democratic values in predicting public program evaluation, conditioned on citizens’ trust in government. Our results show that effectiveness and democratic values contribute similar effects in explaining policy preferences. Distrust in government strengthens the effect of democratic values but reduces the effect of effectiveness. Our findings challenge the prevalent effectiveness centric framework in public management. We suggest that citizen-state interaction should not rely only on performance merits, but also on inclusiveness and openness values.

  17. d

    Carbon Disclosure Project (CDP)

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    • dataverse.harvard.edu
    Updated Mar 6, 2024
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    Carbon Disclosure Project (2024). Carbon Disclosure Project (CDP) [Dataset]. http://doi.org/10.7910/DVN/LWDDJZ
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    Dataset updated
    Mar 6, 2024
    Dataset provided by
    Harvard Dataverse
    Authors
    Carbon Disclosure Project
    Description

    CDP was founded as the Carbon Disclosure Project in 2000, aimed at encouraging firms to disclose more information about their climate-change-related risks and opportunities. On behalf of investors and governments, the CDP surveys public companies worldwide to assess their dependencies on the world's natural resources and their strategies for reducing greenhouse gas emissions, safeguarding water resources, and preventing deforestation. The 3 annual surveys focus on climate change, forests, and water. We have access to following datasets: Climate Change Data: 2010 - 2021 Climate Change Scores: 2010 - 2021 Climate Change Questionnaires: 2010 - 2021 Water Data: 2010 - 2021 Water Scores: 2016- 2021 Water Questionnaires: 2010 - 2021 Forests Data: 2013 - 2021 Forests Scores: 2016- 2021 Forests Questionnaires: 2013 - 2021 Data files for climate change, forests, and water include tickers and ISINs for the surveyed companies. File format: All data files and scores are in Excel format. All Questionnaires are in PDF format.

  18. H

    Index-Insurance in Gujarat

    • dataverse.harvard.edu
    • dataone.org
    Updated Jun 28, 2017
    + more versions
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    Shawn hawn Cole (2017). Index-Insurance in Gujarat [Dataset]. http://doi.org/10.7910/DVN/XPJNXW
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 28, 2017
    Dataset provided by
    Harvard Dataverse
    Authors
    Shawn hawn Cole
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Gujarat
    Dataset funded by
    IFMR
    Description

    The title of the project was: "Index-Insurance in Gujarat"

  19. d

    Data from: Development and effects of a webtoon education program on...

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    • dataverse.harvard.edu
    Updated Nov 8, 2023
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    Kim, Sun-Hee (2023). Development and effects of a webtoon education program on preventive self-management related to premature labor for women of childbearing age: a randomized controlled trial [Dataset]. https://search.dataone.org/view/sha256%3A8fe4fdbb2ed9407e5f6c8e120ffa8f693544aa9b7243cc7d4f7f6c7936810182
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    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Kim, Sun-Hee
    Description

    This study developed a webtoon education program on preventive self-management related to premature labor (PSM-PL) for women of childbearing age, evaluated its effectiveness via RCT, and assessed its usability for webtoon education for women of childbearing age.

  20. d

    Tourism flows in European destinations during and after the Covid-19...

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    • dataverse.harvard.edu
    Updated Sep 25, 2024
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    Borowiecki, Karol Jan; Mitchell, Sara; Pedersen, Maja (2024). Tourism flows in European destinations during and after the Covid-19 pandemic [Dataset]. https://search.dataone.org/view/sha256%3A27308a5ac13e2e99ee74bbc8cc66fc9c4671c31bacf9a735dc80cbd667ea1d75
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    Dataset updated
    Sep 25, 2024
    Dataset provided by
    Harvard Dataverse
    Authors
    Borowiecki, Karol Jan; Mitchell, Sara; Pedersen, Maja
    Description

    The purpose of TOURism Flows in European destinations during and after the Covid-19 pandemic (TOURCO) is to use computer-based algorithms to scrape large unique data on tourism flows from a popular leading travel portal. The project cover periods before, during, and after the Covid-19 pandemic of three European countries (France, Spain, and Denmark). These data make it possible to capture pre-trends and also changes that are a result of the pandemic. The project is funded and scientifically supervised by the Mobile Lives Forum, as part of its research program on the mobility transition. The Mobile Lives Forum is a research institute created by SNCF.

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Harvard Planning & Project Management (HPPM) (2016). GIS Data Layers [Dataset]. http://doi.org/10.7910/DVN/CKYCHU

GIS Data Layers

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Sep 27, 2016
Dataset provided by
Harvard Dataverse
Authors
Harvard Planning & Project Management (HPPM)
License

https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/CKYCHUhttps://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/CKYCHU

Description

The GIS data maintained by HPPM includes information on buildings and grounds related to Harvard University. Our "standard" base layers are available to Harvard affiliates and their service providers (for example, architects) working on Harvard projects in AutoCAD DWG, ESRI SHP or File Geodatabase format. Additional datasets are sometimes available by special arrangement. http://home.hppm.harvard.edu/pages/gis-data-layers

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