Find research datasets worth reusing

IPBES Data Management Tutorials - Session 4.5: Guidelines for the use of external data

The IPBES data management tutorials are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples. The Data management of active research data chapter provides an introduction for IPBES experts on how to manage data while actively being used, analyzed, and produced to fulfill the criteria of the IPBES data management policy. This session, Guidelines for the use of external data, introduces the guiding principles for using external data, data discovery platforms, and correct citation practices.

IPBES Data Management Tutorials - Session 3.7: Data management report details: Long-term storage details

The IPBES data management tutorials are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples. The IPBES data management reports chapter provides an overview and discussion of specific elements of IPBES data management reports. This session, Data management report details: Long-term storage details, explores the reasons why IPBES recommends Zenodo as a long-term repository.

opencc-by-4.0Sep 2020View details →

IPBES Data Management Tutorials - Session 5.5: References and citation manager: Zotero

The IPBES data management tutorials are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples. The Tools for data management chapter provides IPBES authors with an overview of open source tools used frequently by the scientific community to help it implement data management for the entire data life cycle. This session, References and citation manager: Zotero, reviews why IPBES recommends Zotero to manage references and provides links to key resources.

IPBES Data Management Tutorials - Session 5.6: Publishing and sharing

The IPBES data management tutorials are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples. The Tools for data management chapter provides IPBES authors with an overview of open source tools used frequently by the scientific community to help it implement data management for the entire data life cycle. This session on publishing and sharing introduces GitHub and Zenodo as two important open access tools for sharing and publishing information.

IPBES Data Management Tutorials - Session 3.6: Data management report details: Data sharing and access considerations

The IPBES data management tutorials are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples. The Tools for data management chapter provides an overview and discussion of specific elements of IPBES data management reports. This session Data sharing and access considerations covers details on licenses, exceptions to data sharing, and intellectual property considerations.

IPBES Data Management Tutorials - Session 1.3: IPBES and data management

The IPBES data management tutorials are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples. The Introduction to the IPBES data management policy chapter provides an overview on data management within the IPBES platform, and the series of the tutorials prepared by the task force on knowledge and data that will assist experts with the implementation of the IPBES data management policy. This session, IPBES and data management, briefly covers how data management is involved in and influences each objective of the IPBES platform.

IPBES Data Management Tutorials - Session 5.2: Tools to find and attribute DOIs

The IPBES data management tutorials are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples. The Tools for data management chapter provides IPBES authors with an overview of open source tools used frequently by the scientific community to help it implement data management for the entire data life cycle. The session on tools to find and attribute DOIs covers fundamental background information on digital object identifiers and how to resolve and reserve them.

IPBES Data Management Tutorials - Session 2.4: Implementation of the data management policy

The IPBES data management tutorials are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples. The IPBES data management Policy chapter provides an introduction of the IPBES data management policy. It discusses why IPBES has a data management policy and who is responsible for what in the implementation and further development of this policy. This session on the Implementation of the data management policy provides a brief overview of the contents of the following chapters and how it all works together to improve the transparency and credibility of IPBES.

IPBES Data Management Tutorials - Session 6.2: Literature review from the Global Assessment chapter 4

The IPBES data management tutorials are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples. The chapter on Examples of implementing the IPBES data management Policy contains examples of how certain data management tasks and workflows were implemented within IPBES so that they follow the data management policy. Currently, this chapter contains legacy videos and the most recent examples can be found within the IPBES technical guidelines here: <a href="https://ict.ipbes.net/ipbes-ict-guide/data-management/technical-guidelines">https://ict.ipbes.net/ipbes-ict-guide/data-management/technical-guidelines</a> This session Literature review from the Global Assessment chapter 4 walks you through each step of the data management of the systematic literature review from the Chapter 4 of the Global Assessment.

IPBES Data Management Tutorials - Session 6.1: Data management best practices

The IPBES data management tutorials are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples. The chapter on Examples of implementing the IPBES data management Policy contains examples of how certain data management tasks and workflows were implemented within IPBES so that they follow the data management policy. Currently, this chapter contains legacy videos and the most recent examples can be found within the IPBES technical guidelines here: <a href="https://ict.ipbes.net/ipbes-ict-guide/data-management/technical-guidelines">https://ict.ipbes.net/ipbes-ict-guide/data-management/technical-guidelines</a> This session, data management best practices, provides a general review of some best practices of data management and what to expect for this chapter.

opencc-by-4.0Jan 2021View details →

IPBES Data Management Tutorials - Session 5.3: Literature access tools

The IPBES data management tutorials are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples. The Tools for data management chapter provides IPBES authors with an overview of open source tools used frequently by the scientific community to help it implement data management for the entire data life cycle. This session on literature access tools introduces Research4Life, a tool which provides experts in middle to low income countries access to scientific and grey literature.

opencc-by-4.0Aug 2024View details →

Survey Data on Apple Farming in China: Agronomic Management, Advisory Channels, and Profitability

The Survey results and original data are stored in a directory structured as the table: <table style="width: 100%; height: 223.938px;"> <tbody> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;">Type</td> <td style="width: 21.7597%; height: 19.5938px;">File Name</td> <td style="width: 59.4134%; height: 19.5938px;">Description</td> </tr> <tr style="height: 47.5938px;"> <td style="width: 18.8269%; height: 47.5938px;"> Raw_Data_Spearate_Source </td> <td style="width: 21.7597%; height: 47.5938px;">raw_data_english_telephone.xlsx</td> <td style="width: 59.4134%; height: 47.5938px;">Translated data in English corresponding to the Chinese telephone interview data</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;"> </td> <td style="width: 21.7597%; height: 19.5938px;">raw_data_english_wechat.xlsx</td> <td style="width: 59.4134%; height: 19.5938px;">Translated data in English corresponding to the Chinese Wechat Mini Program data</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;">Raw_Data_Total</td> <td style="width: 21.7597%; height: 19.5938px;">raw_data_english_total.xlsx</td> <td style="width: 59.4134%; height: 19.5938px;">Combined data from raw_data_english_telephone.xlsx and raw_data_english_wechat.xlsx</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 18.8269%; height: 39.1875px;">Apple_Statistical_Data</td> <td style="width: 21.7597%; height: 39.1875px;">apple_2022_statistical_data.xlsx</td> <td style="width: 59.4134%; height: 39.1875px;">Contains data on apple planting area, production, and yield sourced from the China Statistics Bureau, along with the number of survey questionnaires collected from various provinces</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;"> </td> <td style="width: 21.7597%; height: 19.5938px;">province_eng.xlsx</td> <td style="width: 59.4134%; height: 19.5938px;">Contains the English version of the provinces' names</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;">Map_Boundary_line</td> <td style="width: 21.7597%; height: 19.5938px;">national_boundary_line.shp</td> <td style="width: 59.4134%; height: 19.5938px;">The country boundaires of China</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;"> </td> <td style="width: 21.7597%; height: 19.5938px;">province_boundary.shp</td> <td style="width: 59.4134%; height: 19.5938px;">The province boundaries of China</td> </tr> </tbody> </table> For privacy reasons, personally identifiable information such as respondents’ names, telephone numbers, and specific addresses has been anonymized in the dataset. The file raw_data_english_total.xlsx contains 96 columns, each corresponding to a question in the questionnaire.

opencc-by-4.0Oct 2023View details →

Learning Elements in Learning Management Systems (LMSs)

Results of a survey in the higher education area. Participants are professors, lecturers, and tutors.   The final definitions for the elements are: <ul> <li>Brief Overview (BO): Short summary or recap without details of the actual learning material</li> <li>Quiz (QU): Quiz questions related to the content taught</li> <li>Learning Goal (LG): Description of the competences, skills or abilities that the learners should acquire in relation to a specific learning content</li> <li>Manuscript (MS): Complete or brief elaboration of a speech, a lecture, a course, or similar</li> <li>Exercise (EX): Opportunity to apply and deepen the learned. Varied tasks are possible beside the classic exercise sheet</li> <li>Summary (SU): Elementalization (reduction to the essentials) of the actual content with details</li> <li>Auditory additional material (AAM): Material with the aim of applying and deepening the learned with audio files</li> <li>Textual additional material (TAM): Material with the aim of applying and deepening the learned with textual further information (also named additional literature)</li> <li>Visual additional material (VAM): Material with the aim of applying and deepening the learned with videos or similar</li> <li>Collaboration Tool (CT): Cooperative and interactive communication medium with the aim of knowledge sharing between learners and learners and/or lecturers, and is used for collaborative work</li> </ul> The corresponding scientific paper can be found via ORCID as of December 2023.   The presented work is supported by the ‘German Federal Ministry of Research, Technology and Space’ (BMFTR) through the granting of the funding project HASKI (FKZ: 16DHBKI035).

opencc-by-4.0Nov 2023View details →

ALL-READY Questionnare on potential drivers and barriers to the adoption of innovation management, open science, and Intellectual Property Rights (IPR) among the members of the Pilot Network

Background & Summary: The ALL-READY project unites a diverse consortium of Research Infrastructures (RI) and Living Labs, instrumental in developing new methodologies and technologies in agroecology. The project focuses on effective management of innovation, adherence to open science principles, and strategic application of Intellectual Property Rights (IPR). Task 6.4 of the project, which concentrates on Innovation and IPR Management, seeks to understand the dynamics influencing the adoption of these practices among its members. Recognizing the need for end-to-end data management, the project emphasizes standardized data collection and management while adhering to FAIR principles.Methods: The questionnaire was developed by LifeWatch ERIC to capture data reflecting current practices and perceptions in agroecology. It included 26 questions divided into four sections, focusing on existing practices, potential drivers, and barriers in innovation management, open science, and IPR. The survey was disseminated via an online platform to the ALLREADY Pilot Network, ensuring a representative sample from diverse organizations. The data collection process was closely monitored, and the responses were analyzed using a mixed-methods approach to extract meaningful insights.Data Records of the ALLREADY Project Questionnaire: The dataset, collected through an online survey platform, underwent a meticulous process of data preparation, download, formatting, and anonymization. It consists of one text file containing metadata (Readme.txt) and a single CSV file encompassing all questionnaire responses. The dataset provides a comprehensive view of innovation management, open science adoption, and IPR handling within the agroecology sector, particularly among the network of RIs and Living Labs involved in the project.Technical Validation of the ALLREADY Project Questionnaire: Several critical steps were taken to ensure the accuracy, reliability, and overall quality of the data collected. This included development and testing of the questionnaire, rigorous monitoring of the data collection process, and thorough checks for data quality and completeness. The representativeness of the sample was analyzed specifically with respect to the Pilot Network rather than the broader population involved in agroecology. Strategies were employed to counter survey fatigue and maintain respondent engagement.Usage Notes for the ALLREADY Project Questionnaire: The dataset's proper usage is vital for ensuring the validity and reproducibility of research. Researchers are advised to consider the nature of the data, the representativeness of the dataset, and its generalizability. The dataset allows for comprehensive analysis and integration of different sections, and analysts have the flexibility to handle open and write-in responses according to their research needs. Additional information to facilitate analysis is provided in a separate documentation file.

opencc-by-4.0Nov 2024View details →

Database of permacultural adoption responses in Mexicali, BC, Mexico. based on Circular Economy, Knowledge Management, and Sustainability policies

Database documenting the perspectives of citizens in Mexicali, Baja California, Mexico, regarding the adoption of permaculture practices. The study is analyzed through the lenses of Knowledge Management, Circular Economy, and Sustainability Policies. The data was collected during the summer of 2024.

opencc-by-4.0Nov 2021View details →

Data belonging to: Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.

Data belonging to the paper Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013. Data includes analysis scripts (R Language) and all used data files. Data is composed of location information of the different sites, environmental conditions on site and vegetation composition.

opencc-by-4.0Oct 2020View details →

Sensor deployment to support the integrated energy management system in residential buildings in ReCO2ST LoRa Dataset

LoRa Radio Testing Datasets for preliminary performance tests. These datasets were taken in order to ensure that the LoRa radios were capable of transmitting through concrete and testing various preamble settings of the radio. As per the paper, "Although these testing methodologies were indicative but not exact or perfect, to test in a manner that was qualitative would have been both costly and beyond the scope of the project."  These tests were to help us verify feasibility of the chosen LoRa Radio

opencc-by-4.0Dec 2021View details →

Data to support the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362

Soil organic carbon content and water content at the different pressure points, as measured by Ioanna Panagea for  the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe",  https://doi.org/10.3390/land10121362 from the the long term experiments  belonging in some of the SoilCare project partners.

RiceFloodIT: Water Management in the Italian Rice Paddies Estimated from MODIS data

This repository includes two datasets used in Ranghetti et al. (2018) and Ranghetti & Boschetti (2022) to analyse the magnitude of a decreasing trend in the extent of submerged rice paddies during the rice-sowing period in the Italian rice district: methods used to generate these data from MODIS remote sensing imagery are described in these papers. <ul> <li>ffavg_2021.csv: this dataset includes values of yearly FFavg (averaged Flooding Fraction) at pixel level. Each record represent the FFavg value of a specific pixel in a specific year. <ul> <li>x and y identifies the latitude and longitude of each record (in UTM32 coordinates);</li> <li>subdistrict represent the sub-district ID of each pixel ("A" to "G");</li> <li>year is the year whose each record refers to;</li> <li>ff is the FFavg value (range 0-1);</li> <li>count is the number of MODIS images used to generate each FFavg aggregated value.</li> </ul> </li> <li>ws_2021.csv: this dataset includes values of WS (proportion of Water-Seeded rice surface) at sub-district and district levels. <ul> <li>subdistrict represent the sub-district ID of each record ("A" to "G", plus "all" which identifies values aggregated at district level);</li> <li>year is the year whose each record refers to;</li> <li>ws is the WS value (range 0-1);</li> <li>count is the number of pixels used to generate each WS aggregated record.</li> </ul> </li> </ul> Current data version (2021.01) includes estimated values in the period 2000-2021. References: Ranghetti, Luigi, Elisa Cardarelli, Mirco Boschetti, Lorenzo Busetto and Mauro Fasola. 2018. “Assessment of Water Management Changes in the Italian Rice Paddies from 2000 to 2016 Using Satellite Data: A Contribution to Agro-Ecological Studies.” Remote Sensing 10 (3). doi:<a href="https://doi.org/10.3390/rs10030416">10.3390/rs10030416</a>. Ranghetti, Luigi and Mirco Boschetti. 2022. “Updated trends of water management practice in the Italian rice paddies from remotely sensed imagery.” European Journal of Remote Sensing 55 (1), pp. 1-9. doi:<a href="https://doi.org/10.1080/22797254.2021.2002726">10.1080/22797254.2021.2002726</a>.