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8,537 results for “OpenITI”
Open Soil Atlas (OSA) - Raw data from Phase I (Mar-Aug 2021, Berlin)
<p>The Open Soil Atlas citizen science project collected 77 data observations, cathegorized in a set of 10 inficators, which offer information about the quality and fertility of the soil. Area of investigation: Berlin. Data collection period: March-August 2021.</p>
DeepScenario: An Open Driving Scenario Dataset for Autonomous Driving System Testing
<p>With the rapid development of autonomous driving systems (ADSs), testing ADSs under various driving conditions has become a key method to ensure the successful deployment of ADS in the real-world. However, it is impossible to test all the scenarios due to the inherent complexity and uncertainty of ADSs and the driving tasks. Further, testing of ADSs is expensive regarding time and computational resources. Therefore, a large-scale driving scenario dataset consisting of various driving conditions is needed. To this end, we present an open driving scenario dataset <em>DeepScenario</em>, containing over 30<em>K</em> <em>executable</em> driving scenarios, which are collected by 2880 test executions of three driving scenario generation strategies. Each scenario in the dataset is labeled with six attributes characterizing test results. We further show the attribute statistics and distribution of driving scenarios. For example, there are 1050 collision scenarios, in 917 scenarios there were collisions with other vehicles, 105 and 28 with pedestrians and static obstacles, respectively.</p> <p>This dataset contains:</p> <ol> <li><strong><a href="https://github.com/Simula-COMPLEX/DeepScenario/tree/main/deepscenario-dataset">deepscenario-dataset</a></strong> - DeepScenario dataset, which includes driving scenarios generated by executing three scenario generation strategies: <em>Reinforcement Learning (RL)-based Strategy</em>, <em>Random-based Strategy</em>, <em>Greedy-based Strategy</em>;</li> <li><strong><a href="https://github.com/Simula-COMPLEX/DeepScenario/tree/main/deepscenario-toolset">deepscenario-toolset</a></strong> - The toolset for <em>DeepScenario</em> dataset, including <em>ScenarioCollector</em> that can automatically collect driving scenarios, and <em>ScenarioRunner</em> that can support replaying driving scenarios. We also provide <a href="https://github.com/Simula-COMPLEX/DeepScenario/tree/main/deepscenario-toolset/lgsvl/scenariotoolset">source code</a> and <a href="https://github.com/Simula-COMPLEX/DeepScenario/tree/main/deepscenario-toolset#usage">usage examples</a> for the toolset. </li> </ol> <p>More information about DeepScenario dataset is available in our Github repository: <a href="https://github.com/Simula-COMPLEX/DeepScenario">https://github.com/Simula-COMPLEX/DeepScenario</a>.</p>
New Particle Search at CERN open classification data
<p>This is a reduced, anonymised dataset containing the classifications made in the New Particle Search at CERN demonstrator project available on Zooniverse during the implementation period - from the 19th of October, 2021, to the 23rd of October, 2023 - as part of the REINFORCE project.</p>
Cosmic Muon Images open classification data
<p>This is a reduced, anonymised dataset containing the classifications made in the Cosmic Muon Images demonstrator project available on Zooniverse during the implementation period - from the 19th of October, 2021, to the 23rd of October, 2023 - as part of the REINFORCE project.</p>
GWitchHunters open classification data
<p>This is a reduced, anonymised dataset containing the classifications made in the GWitchHunters demonstrator project available on Zooniverse during the implementation period - from the 19th of October, 2021, to the 23rd of October, 2023 - as part of the REINFORCE project.</p>
Deep Sea Explorers open classification data
<p>This is a reduced, anonymised dataset containing the classifications made in the Deep Sea Explorers demonstrator project available on Zooniverse during the implementation period - from the 19th of October, 2021, to the 23rd of October, 2023 - as part of the REINFORCE project.</p>
Linked Open Data Management Services: A Comparison
<p>Thanks to a variety of software services, it has never been easier to produce, manage and publish Linked Open Data. But until now, there has been a lack of an accessible overview to help researchers make the right choice for their use case. This dataset release will be regularly updated to reflect the latest data published in a comparison table developed in Google Sheets [1]. The comparison table includes the most commonly used LOD management software tools from NFDI4Culture to illustrate what functionalities and features a service should offer for the long-term management of FAIR research data, including:</p> <ul> <li>ConedaKOR</li> <li>LinkedDataHub</li> <li>Metaphacts</li> <li>Omeka S</li> <li>ResearchSpace</li> <li>Vitro</li> <li>Wikibase</li> <li>WissKI</li> </ul> <p>The table presents two views based on a comparison system of categories developed iteratively during workshops with expert users and developers from the respective tool communities. First, a short overview with field values coming from controlled vocabularies and multiple-choice options; and a second sheet allowing for more descriptive free text additions. The table and corresponding dataset releases for each view mode are designed to provide a well-founded basis for evaluation when deciding on a LOD management service. The Google Sheet table will remain open to collaboration and community contribution, as well as updates with new data and potentially new tools, whereas the datasets released here are meant to provide stable reference points with version control.</p> <p>The research for the comparison table was first presented as a paper at DHd2023, Open Humanities – Open Culture,<strong> </strong>13-17.03.2023, Trier and Luxembourg [2].</p> <p>[1] Non-editing access is available here: <a href="http://docs.google.com/spreadsheets/d/1FNU8857JwUNFXmXAW16lgpjLq5TkgBUuafqZF-yo8_I/edit?usp=share_link">docs.google.com/spreadsheets/d/1FNU8857JwUNFXmXAW16lgpjLq5TkgBUuafqZF-yo8_I/edit?usp=share_link</a> To get editing access contact the authors.</p> <p>[2] Full paper will be made available open access in the conference proceedings.</p>
OMCF questionnaires - Surveying volcanic crises exercises: From open-question questionnaires to a prototype checklist
<p>English / French / Italian / Spanish versions</p> <p>Volcanic crisis exercises are usually run to test response capabilities, communication protocols, and decision-making procedures by those agencies, such as volcano observatories and/or Civil Protection authorities, with responsibilities to cope with scenarios of volcanic unrest with inherent uncertainty. During the last decades, the use of questionnaires has been increased to evaluate people’s knowledge on volcanic hazards and their perception of risk, which could affect their preparedness to respond to emergency measures plans. In this paper, we show the study carried out within the EUROVOLC project in extracting information on the experience gained during volcanic-crisis exercises by the project’s participants and beyond. In particular, we first distributed an open-question questionnaire survey within the EUROVOLC-project community. Based on the results obtained, we developed a more user-friendly online multi-choice questionnaire that we submitted to different volcanological communities within and outside the project. From the answers to the on-line questionnaire, we extracted a prototype checklist for guiding those who design such exercises in the future. Here, we give details on the lessons learnt from this study, in particular about the need to increase training activities, to improve external (between players and general public/media) communication tools, equipment and protocols and to better define decision-makers needs. Our preliminary results confirm that this type of survey is a very useful tool for gathering information on participants' experience and knowledge, and to understand which data and information may be useful when designing exercises for scientists, emergency managers and others involved in a volcanic crisis.</p> <p> </p>
Compilation of open asset-level data, as of Dec 2022
<p>This dataset is a compilation of open asset-level data, which means the location of sites (e.g., operation, manufacturing, processing facilities of global supply chains), as of December 2022. This included data from 9 publicly available sources, that after data cleaning and harmonization, resulted in 189,075 data points. </p> <table> <tbody> <tr> <td><strong>Data source</strong></td> <td><strong>Number of data points</strong></td> </tr> <tr> <td><a href="https://opensupplyhub.org/">Open Supply Hub (former Open Apparel Registry)</a></td> <td>96,736</td> </tr> <tr> <td><a href="https://datasets.wri.org/dataset/globalpowerplantdatabase">Global Power Plant Database</a></td> <td>35,419</td> </tr> <tr> <td><a href="https://climatetrace.org/downloads">Climate trace</a></td> <td>19,945</td> </tr> <tr> <td><a href="https://www.fda.gov/drugs/drug-approvals-and-databases/drug-establishments-current-registration-site">FDA database</a></td> <td>12,898</td> </tr> <tr> <td><a href="https://www.globaldamwatch.org/database">Global Dam Watch</a></td> <td>11,017</td> </tr> <tr> <td><a href="https://www.ema.europa.eu/en/human-regulatory/research-development/compliance/good-manufacturing-practice/eudragmdp-database">EudraGMDP database</a></td> <td>5,181</td> </tr> <tr> <td><a href="https://www.cgfi.ac.uk/spatial-finance-initiative/geoasset-project/geoasset-databases/">Sustainable Finance Initiative GeoAsset Databases</a></td> <td>4,716</td> </tr> <tr> <td><a href="https://tailing.grida.no/disclosures">Global Tailings Portal</a></td> <td>1,956</td> </tr> <tr> <td><a href="https://www.fineprint.global/resources/mining-database/">Fine print Mining Database</a></td> <td>1,207</td> </tr> </tbody> </table> <p>This data was assigned with the industry in which the asset is. The summary table below shows the number of assets by industry.</p> <table> <tbody> <tr> <td><strong>Industry</strong></td> <td><strong>Number of assets </strong></td> </tr> <tr> <td>Textiles, Apparel & Luxury Good Production </td> <td>96,736</td> </tr> <tr> <td>Health Care, Pharma and Biotechnology</td> <td>18,079</td> </tr> <tr> <td>Energy - Solar, Wind</td> <td>16,282</td> </tr> <tr> <td>Energy - Hydropower</td> <td>14,515</td> </tr> <tr> <td>Energy - Geothermal or Combustion</td> <td>11,724</td> </tr> <tr> <td>Metals & Mining</td> <td>11,210</td> </tr> <tr> <td>Transportation Services</td> <td>4,872</td> </tr> <tr> <td>Construction Materials</td> <td>3,117</td> </tr> <tr> <td>Agriculture (animal products)</td> <td>2,388</td> </tr> <tr> <td>Agriculture (plant products)</td> <td>1,896</td> </tr> <tr> <td>Oil, Gas & Consumable Fuels</td> <td>1,194</td> </tr> <tr> <td>Water utilities / Water Service Providers</td> <td>892</td> </tr> <tr> <td>Hospitality Services</td> <td>294</td> </tr> <tr> <td>Fishing and aquaculture</td> <td>14</td> </tr> <tr> <td>Other</td> <td>5,862</td> </tr> </tbody> </table> <p><strong>Note that this compilation is based on an extensive search, however, we acknowledge that there is a significant discrepancy in data coverage/comprehensiveness among the different industries.</strong> The industry "Textiles, Apparel & Luxury Good Production" is by far the most complete, while other are clearly far from complete, for example, “Construction Materials”, "Agriculture (animal products)”, “Agriculture (plant products)”, “Oil, Gas & Consumable Fuels”, “Water utilities / Water Service Providers”, “Hospitality Services”, “Fishing and aquaculture”. <strong>Therefore, any comparison between industries should take this coverage/comprehensiveness bias into consideration.</strong></p> <p> </p>
Value creation stories anonymized open data set (Immunization Agenda 2030 Full Learning Cycle, 7 March - 20 June 2022)
<p># Title<br> Immunization Agenda 2030 (IA2030) 1st Movement Full Learning Cycle (FLC 2022) – “How are you doing?” Value Creation Stories Survey (Version 1.0)</p> <p># Research audience<br> Education researchers interested in the application of the “value creation stories” (VCS) conceptual framework elaborated by Etienne Wenger et al. in the study of communities of practice and other types of digital communities.</p> <p># Credits</p> <p>## Author<br> The Geneva Learning Foundation<br> 18 avenue Louis Casaï<br> CH-1209 Geneva, Switzerland<br> research@learning.foundation</p> <p>### Principal Investigator and corresponding author<br> Reda Sadki, The Geneva Learning Foundation (TGLF)<br> reda@learning.foundation</p> <p>## Project partners<br> Bridges to Development<br> University of South Australia Centre for Change and Complexity in Learning (C3L)</p> <p>## Roles and responsibilities<br> - Design: The Geneva Learning Foundation<br> - Implementation (sample collection): The Geneva Learning Foundation<br> - Processing: The Geneva Learning Foundation, Bridges for Development, Centre for Complexity and Change in Learning (C3L)<br> - Anonymization: The Geneva Learning Foundation and Bridges for Development<br> - Data cleaning: Bridges to Development<br> - Submission: The Geneva Learning Foundation</p> <p>## Funding sources or sponsorship that supported the data collection<br> Wellcome, Bill & Melinda Gates Foundation (BMGF)</p> <p>## Recommended citation<br> The Geneva Learning Foundation, 2023. Value Creation Stories (VCS) weekly feedback survey, 2022 Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) (Version 1.0). [Data Set]. The Geneva Learning Foundation. DOI: 10.5281/zenodo.7763922</p> <p># Description of the sample</p> <p>## File list:</p> <p>This file is IA2030_FLC_2022_Value_Creation_Stories.README.md</p> <p>IA2030-EN_FLC_2022_Value_Creation_Stories-questions_mapping.csv : List of the survey’s questions and their code in English as well as their unit. (21 questions) - Version 1: Geneva Learning Foundation, 31 March 2023. </p> <p>IA2030-EN_FLC_2022_Value_Creation_Stories.csv : Dataset Response of participants that replied in English. (n: 2101, obs:5601) - Version 1: Geneva Learning Foundation, 31 March 2023. </p> <p>IA2030-FR_FLC_2022_Value_Creation_Stories-questions_mapping.csv: List of the survey’s questions and their code in English as well as their unit. (21 questions) - Version 1: Geneva Learning Foundation, 31 March 2023.</p> <p>IA2030-FR_FLC_2022_Value_Creation_Stories-Google_translation.csv: Dataset Response of participants that replied in French translated to English using “Google Translate” (n: 1585, obs:4493) - Version 1: Geneva Learning Foundation, 31 March 2023.</p> <p>IA2030-FR_FLC_2022_Value_Creation_Stories.csv: Dataset Response of participants that replied in French (n: 1585, obs:4493) - Version 1: Geneva Learning Foundation, 31 March 2023. Relationship between files: The questions codes data set are the same code as the column variables and can be connected.</p> <p>## Relationship between files<br> The questions codes data set are the same code as the column variables and can be connected.</p> <p>## Related data sets<br> This is a subset of data collected by The Geneva Learning Foundation (TGLF) during the 1st IA2030 Full Learning Cycle (FLC). The complete data set is more comprehensive, and includes: demographic information (gender, country), health system information (respondent’s health system level), respondents’ analyses of challenges and priorities. </p> <p>Additional data sets for the first Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) are available from The Geneva Learning Foundation (TGLF) Insights Unit [insights@learning.foundation](insights@learning.foundation)</p> <p>## Other publicly accessible locations of the data<br> The Geneva Learning Foundation publishes data sets in relation to its Immunization Agenda 2030 (IA2030) Movement learning programme in the Zenodo open repository community: https://zenodo.org/communities/ia2030/</p> <p>## 1. Purpose and Objectives</p> <p>### Primary goal of the survey:<br> This survey had two goals in the context of TGLF’s IA2030 Movement Full Learning Cycle programme (2022): <br> 1. Provide an asynchronous mechanism for support between peers (participants) and from the TGLF team; and<br> 2. collect and measure programme participants’ value creation stories (VCS) during the programme.</p> <p>Martin de Laat’s “value creation stories” (VCS) has been used primarily in small-scale, qualitative studies of communities of practice, online forums, and education activities.</p> <p>This data set includes both quantitative (Likert) and qualitative (open text) responses to the VCS questions, collected over a period of four months (7 March – 20 June 2022) from a cohort that began with 6,185 participants on the start date.</p> <p>## 2. Population and Sample</p> <p>The target population were participants of the Geneva Learning Foundation’s Movement for Immunization Agenda 2030 (IA2030) learning programme. The initial cohort admitted to the programme was 6,185 individuals from 99 countries. Only participants who were formally admitted to the programme received the invitation to complete the survey.</p> <p>Programme participants were free to choose if and when to report (self-selection), and their responses were not checked against any other measures (self-reporting).</p> <p>### Languages: French and English</p> <p>## 3. Survey Design and Methods</p> <p>Data collection period: 7 March 2022 – 20 June 2022</p> <p>Between 7 March and 20 June 2023, participants in the Geneva Learning Foundation’s “Immunization Agenda 2030” (IA2030) Movement Full Learning Cycle (FLC) were asked to respond to a questionnaire titled “How are you doing?”.</p> <p>Participants received a personalized email with the request to share feedback about their experience during the week. The link to share feedback was also included in other reminder and information emails sent in response to participant needs.</p> <p>The first survey was launched on the 11 of March 2022 and the last at 17 of March 2022, totalizing 15 requests. Participants could answer the survey at any time and as many times that they wished.</p> <p> <br> The group of 6,185 participants grew over the course of the Cycle, as additional participants were able to join the initiative throughout the four-month period.</p> <p>### Software- or Instrument-specific information needed to interpret the data<br> - Automated translation of French data was performed using [Google Translate](https://translate.google.com/?sl=en&tl=fr&op=docs)<br> - Methods used for removing or anonymizing personal identifiers or sensitive information:<br> - Unique identifier: Unique identifiers were anonymized using MD5 Hashing via the web site [Miracle Salad](https://www.miraclesalad.com/webtools/md5.php.).Unique identifiers can be used to identify respondents who may have answered the survey more than once, at different points in time. This approach provides a method to anonymize sensitive data using MD5 hashing.*Limitation: MD5 hashing is a one-way function; it is not possible to dehash the data and recover the original information.**<br> - Macros developed in Excel to replace Country names in qualitative responses. (No country information were collected in this survey, but some respondents referred to their specific contexts in their responses.) The macro did not account for typos, in case any country information is found please contact: [research@learning.foundation](research@learning.foundation)</p> <p>### Data collection start and end dates:<br> 7 March 2023 until 20 June 2023</p> <p>#### Events or circumstances during data collection that may have influenced results:<br> No requests for responses were sent during TGLF’s “Term break” between 16-30 April 2022.</p> <p>## 4. Data Processing and Cleaning</p> <p>- Incomplete or inconsistent responses: Not cleaned, as respondents were able to opt out of specific sections of survey or skip questions.<br> - Data transformations or imputations: None<br> - Treatment of outliers or extreme values: None</p> <p>## 5. Variables and Measures</p> <p>The survey included Likert scale questions and qualitative open texts based the conceptual framework for Value Creation Stories (VCS) developed by Wenger et. al. (2011). There are no derived or calculated variables. Items are Likert scale, multiple choice, and open text.</p> <p>## 6. Data Quality and Reliability<br> All the responses done before or after the FLC period (7 March – 20 June 2022) were excluded of the sample.</p> <p>## 7. Data Privacy and Anonymization</p> <p>### Methods used for removing or anonymizing personal identifiers or sensitive information:<br> - Unique identifier: Unique identifiers were anonymized using MD5 Hashing via the web site https://www.miraclesalad.com/webtools/md5.php. Unique identifiers can be used to identify respondents who may have answered the survey more than once, at different points in time. This approach provides a method to anonymize sensitive data using MD5 hashing.<br> - Limitation: MD5 hashing is a one-way function; it is not possible to dehash the data and recover the original information. <br> - Macros developed in Excel to replace Country names in qualitative responses. (No country information were collected in this survey, but some respondents referred to their specific contexts in their responses.)</p> <p>## 8. Data Availability and Accessibility<br> This data set is made available on Zenodo.org in the Zenodo community “Movement for Immunization Agenda 2030 (IA2030)”<br> https://zenodo.org/communities/ia2030/</p> <p>Requests for additional information should be addressed to research@learning.foundation.</p> <p>This is a subset of data collected by The Geneva Learning Foundation (TGLF) during the 1st IA2030 Full Learning Cycle (FLC).</p> <p>The complete data set is more comprehensive, and includes: demographic information (gender, country), health system information (respondent’s health system level), respondents’ analyses of challenges and priorities.</p> <p>### Other publicly accessible locations of the data<br> The Geneva Learning Foundation publishes data sets in relation to its Immunization Agenda 2030 (IA2030) Movement learning programme in the Zenodo open repository community: https://zenodo.org/communities/ia2030/</p> <p>### Related data sets<br> Additional data sets for the first Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) are available from The Geneva Learning Foundation (TGLF) Insights Unit insights@learning.foundation</p> <p>## 10. Ethical Considerations</p> <p>### Ethical guidelines followed during data collection:<br> TGLF’s research abides by the principles of the Cantonal Commission for Research Ethics (CCER), the Federal Law on Research on Human Beings (RS 810.30), Swiss Human Research Act (HRA) and the Ordinance on Organisational Aspects of the Human Research Act (HRA Organisation Ordinance, OrgO-HRA)</p> <p>### Informed consent and participant rights information:<br> In order to join TGLF’s IA2030 Full Learning Cycle programme, participants had to confirm their agreement to use of their responses “for research, learning, evaluation, communication, and advocacy, in line with the Foundation’s mission”.</p> <p>Participants were able to opt out of the VCS questions by selecting “No” when asked “Could we ask you five questions about your participation?”. They were informed these questions were asked in order to “share your feedback in the next weekly Assembly”, the weekly synchronous meeting for programme participants. The rationale for sharing such feedback was also explained; “Your contribution will help everyone understand how we are doing as a group, and also help us to better support you.”</p> <p>Data protection and confidentiality<br> Consent was requested during the application and submitted of action plan period for sharing data, in line with the Geneva Learning Foundation’s data protection and confidentiality policy.</p> <p># Copyright and license<br> The Geneva Learning Foundation © 2022. This data set and all associated files are licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)</p> <p>© The Geneva Learning Foundation 2023</p> <p>Some rights reserved. This work is available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International; https://creativecommons.org/licenses/by-nc-sa/4.0/.</p> <p>Under the terms of this license, you may copy, redistribute and adapt the data set for non-commercial purposes, provided the work is appropriately cited, as indicated below. In any use of this data set, there should be no suggestion that the Foundation endorses any specific organization, products or services. The use of the Foundation logo is not permitted. If you use the data set, then you must license your work under the same or equivalent Creative Commons license. If you create a translation of this data set, you should add the following disclaimer along with the suggested citation: “This translation was not created by the Geneva Learning Foundation. The Foundation is not responsible for the content or accuracy of this translation. The original English edition shall be the binding and authentic edition.”</p> <p>Any mediation relating to disputes arising under the license shall be conducted in accordance with the mediation rules of the World Intellectual Property Organization.</p> <p>General disclaimers. The designations employed and the presentation of the data set do not imply the expression of any opinion whatsoever on the part of the Foundation concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement.</p> <p>The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by the Foundation in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters.</p> <p>All reasonable precautions have been taken by the Foundation to verify the information contained in this data set. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall the Foundation be liable for damages arising from its use.</p> <p>This data set contains individual views and does not necessarily represent the decisions or the policies of the Foundation.</p> <p>Version 1.0 (31 March 2023): reviewed internally; reviewed externally. </p> <p># References<br> Wenger, E., Trayner, B., de Laat, M., 2011. Promoting and assessing value creation in communities and networks: a conceptual framework (Rapport No. 18). Oitpen Universiteit, Ruud de Moor Centrum.</p> <p>Wenger, E., Trayner, B., de Laat, M., 2011. Promoting and assessing value creation in communities and networks: a conceptual framework (Rapport No. 18). Oitpen Universiteit, Ruud de Moor Centrum.</p> <p>Victoria J. Marsick, Rachel Fichter, Karen E. Watkins, 2022. From Work-based Learning to Learning-based Work: Exploring the Changing Relationship between Learning and Work, in: The SAGE Handbook of Learning and Work. SAGE Publications.</p> <p>Watkins, K.E., Sandmann, L.R., Dailey, C.A., Li, B., Yang, S.-E., Galen, R.S., Sadki, R., 2022. Accelerating problem-solving capacities of sub-national public health professionals: an evaluation of a digital immunization training intervention. BMC Health Serv Res 22, 736. https://doi.org/10.1186/s12913-022-08138-4</p> <p>Watkins, K.E., Kim, K., 2019. Measuring the Impact of the WHO Scholar Programme Courses for Immunization (2016-2018) (Evaluation report). University of Georgia at Athens, Athens, United States.</p> <p>Watkins, K.E., Bhattarai, A., 2019. Analysis of the Impact Accelerator Launch Pad Individual Acceleration Reports in July 2019. University of Georgia at Athens, Athens, United States.</p> <p># Questionnaire</p> <p>## Hello {{fname}} {{lname}}. How are you doing in the Movement for Immunization Agenda 2030?</p> <p>## Do you need help? Do you want to share your experience? We would like to know how you are doing.<br> - I am doing fine.<br> - I have a problem and need help.<br> - I want to share my experience.</p> <p>## Tell us more about what you want to share. Be specific and detailed so that we can understand. Share your lessons learned, successes, and challenges.</p> <p>## Did you complete your action for the week? If you did it, how did it turn out? What did you learn in the process? Did anything surprise you? What will you do next? If you did not complete your action, what will you do differently next week? This is a good way to write your thoughts if you did not get to speak in the last session. You can also record an audio message in the IA2030 Movement Dialogue https://t.me/+-PwJxPpyWfQ0ZjRk or share an idea or practice https://accelerator.wazoku.com/ccc/learning in the Ideas Engine.</p> <p>## What do you need help with?<br> - I do not know what I am supposed to do<br> - I need help with my IA2030 challenge<br> - I want to catch up<br> - I have poor connectivity<br> - I have a problem with technology<br> - Something else</p> <p>## Tell us more about the problem you are facing.<br> What have you tried to solve this problem? Where did you get stuck? The more information you provide, the better colleagues will be able to help you.</p> <p>## Have you tried taking time to read and follow the instructions? </p> <p>Click here https://www.learning.foundation/products/movement-for-immunization-agenda-2030-full-learning-cycle-1-march-2022 to access the video tutorials and slide decks on www.learning.foundation https://www.learning.foundation/login. </p> <p>Use your email email to log in. Don’t remember your password?</p> <p>Click here to recover it https://www.learning.foundation/password/new. </p> <p>Take the time to read the instructions – and then follow them step-by-step. Do not forget to come back to finish this questionnaire. </p> <p>## Do not suffer in silence. It sounds like you should ask for help from your Movement colleagues. </p> <p>Click here to connect with colleagues https://t.me/IA2030 in the IA2030 Movement Telegram channel.<br> - When you join Telegram, please introduce yourself and explain the problem that you are facing. Your colleagues can only help you if you describe the issue and explain what you have already done to solve it.<br> - We encourage you to share your challenge in the next short session where we share experience and problem-solve. Click here to register https://us02web.zoom.us/j/86171141804, and then come back to finish this questionnaire.<br> - Surely, someone will be able to help you. But you do have to register https://us02web.zoom.us/j/86171141804 and actually show up at the right time!<br> - Poor connectivity? Click here to listen https://podcasts.google.com/feed/aHR0cHM6Ly9saXN0ZW5ib3guYXBwL2YvODRTTFI0eTY5X05h to our low-bandwidth podcast. And then come back to finish this questionnaire.<br> - You can listen to most sessions in our podcast. This is audio-only, like listening to radio on demand.</p> <p>## Could we ask you five questions about your participation?<br> We will share your feedback in the next weekly Assembly. Your contribution will help everyone understand how we are doing as a group, and also help us to better support you.</p> <p>Yes<br> No</p> <p>## Participation changed me as a professional<br> (change in skills, attitudes, identity, self-confidence, feelings, etc.).</p> <p>## Can you explain how participation changed you as a professional?</p> <p>## Participation affected my social connections<br> (change in the number, quality, frequency, emotions, etc.)</p> <p>## Can you explain how participation affected your social connections?</p> <p>## Participation helped my professional practice<br> (get new ideas, insights, materials, procedures, etc.)</p> <p>## Can you explain how participation helped your professional practice?</p> <p>## Participation changed my ability to influence my world as a professional<br> (enhance my voice, contribution, status, recognition, etc.)</p> <p>## Can you explain how participation changed your ability to influence your world as a professional?</p> <p>## Participation made me see my world differently<br> (change in perspective, new understandings of the situation, redefine success, etc.)</p> <p>## Can you explain how participation made you see your world differently?</p> <p>## Do you remain committed to the Movement for Immunization Agenda 2030?<br> You remain a Member even if you are not actively participating.<br> - Yes, and I am actively participating<br> - Yes, but I am not actively participating<br> - No, I wish to leave the Movement</p> <p>## We are sorry to see you go. Could you let us know what went wrong? What could we have done better to support you?<br> - (Or just hit RETURN to skip.)</p> <p>## What is the email you are using?<br> We need your email to follow up and respond to what you shared with us. Do not forget to press the SUBMIT button.</p> <p>## URL redirection upon completion:<br> https://www.learning.foundation/products/movement-for-immunization-agenda-2030-full-learning-cycle-1-march-2022</p> <p>## Thank you [fname] [lname] for sharing your feedback.<br> We will share your feedback in the next weekly Assembly. Your contribution will help everyone understand how we are doing as a group, and also help us to better support you.</p> <p>Click here to check http://cal.ae/eudusmw the IA2030 Movement calendar so you do not miss upcoming event</p>
Content Analysis of Canada's Science Based Departments and Agencies Open Science Action Plans
<p>Dataset and codebook for a content analysis of Science-based departments and agencies open science action plans in response to the Government of Canada's Roadmap for Open Science. </p>
SDF format of Crystallography Open Database - April 2023
<p>This is a dump & transform to SDF of the crystallography open database that I computed & published in the context of a blog post. You can use that as alternative to extract angle, dihedral or distance statistics from freely & openly accessible crystal structures. </p>
Openness process in Valladolid city
<p>Nature-Based Solutions require planning approaches and governance architectures that support accessibility to green spaces, while maintaining their quality for the provision of ecosystem services. Urban environmental problems are often difficult to handle and successful solutions require combined efforts of different scientific disciplines but also an active dialogue between stakeholders from policy and society (Lemos and Morehouse, 2005).</p> <p>In this context, transdisciplinary approaches for knowledge co-production provide insights about the ways and the rationale for engaging with multiple knowledge holders: experts and scientists as well as citizens and practitioners (Bergmann et al., 2012, Jahn et al., 2012). The scientific frameworks of urban ecosystem services were brought into the interface between policy and science to inform urban planning and governance (Frantzeskaki and Tilie, 2014).</p> <p>The quality of the URBAN GreenUP project implementation depends on social learning and adequate technical solutions. This is possible through the support and cooperation between the involved parties and the resulting input of knowledge (Luyet, 2012).</p> <p>Participation is often reduced to the dissemination of information and the holding of workshops. These approaches generally do not take into account either the heterogeneity of stakeholders, or the complexity of the decision making process (Luyet, 2012).</p> <p>The KPI “Openness of participatory processes” is based on the participation actions delivered in the city of Valladolid. There are defined two steps, data collection and data evaluation.</p>
Interoperability open Datasets
<p>The</p> <p>The open datasets include two categories:</p> <ul> <li>bibliographic dataset which includes a subset of data from <a href="http://orcid.org">ORCID registry</a> (orcid.zip + orcid_data.zip) and a <a href="https://pubmed.ncbi.nlm.nih.gov/">PubMed</a> (pubmed.zip + pubmed_data.zip).</li> <li>clinical trials databases: which consist of three subsets of data from <a href="https://www.clinicaltrialsregister.eu/">EU Clinical Trials Register</a> (euctr.zip + euctr_data.zip), <a href="http://clinicaltrials.gov/ct2/home">CT.gov</a> (ctgov.zip + ctgov_data.zip), and the <a href="http://drks.de/search/en">German Trials </a>(drks.zip + drks_data.zip).</li> </ul> <p> </p> <p>N.B: For each dataset, there are two files available: one containing the SQL code required to create the database schema in PostgreSQL and another file with the format name "[dataset_name]_data.zip" that includes SQL insertion of a collection of data.</p>
Open Source and Open Science Sustainability Year-long Study - Pseudonymised
<p>Research software is often abandoned or shut down, for one reason or another. While some reasons may be straightforward, e.g. a sole maintainer has moved on, or grant funding has ceased - some projects are able to withstand these barriers and may remain active and maintained despite adversity.</p> <p>This study monitors open source projects over the period of a year, measuring common performance indicators, to see if any indicators are common to projects that remain sustainable and active.</p> <p>This study uses mixed methods:</p> <ol> <li>Initial survey gathers info about the project age, leadership, and GitHub (or other source control) URLs. Participants are asked to add <a href="https://sustainable-open-science-and-software.github.io/readme_notice">a short notice to their readme</a>.</li> <li>After the initial survey, we gathered information about the GitHub projects such as number of contributors, number of PRs, time taken to close/merge these PRs, and issues closed. Some of this info is gathered using scripts, and other parts are gathered manually. An example of a manual metric is the Code of Conduct - while we can programmatically check for the <em>existence</em> of CodeOfConduct.md, we can’t easily check for enforcement contacts without manual checks.</li> <li>6 months and 12 months after the initial survey, we send follow up surveys, and in month 12 we re-run the GitHub metrics to compare to month 0.</li> </ol> <p> </p> <p>For more study info see: <a href="https://sustainable-open-science-and-software.github.io/">https://sustainable-open-science-and-software.github.io/</a></p> <p> </p>
CONUS NG-IDF 2.0: Open Area
<p>The <strong>NG-IDF: Open Area</strong> datasets cover more than 200,000 sites at approximately 6 km resolution in the years 1951–2013 across the CONUS for the <strong>Open Area </strong>(i.e., no vegetation) land use land cover (LULC).</p> <p>These [<strong>daily time series</strong> + <strong>annual maximum</strong>] datasets provide information on extreme hydrological events and their associated hydrometeorological drivers at a continental scale.</p> <p>They include 1) daily time series of precipitation (P), throughfall (TF), water available for runoff (W), and snow water equivalent (SWE); 2) annual maximum time series of P, W, TF, snowmelt, rain-on-snow (ROS); and 3) NG-IDF curves and their uncertainties.</p> <p>See the "Open_readmefirst.txt" file for details.</p>
AgrImOnIA: Open Access dataset correlating livestock and air quality in the Lombardy region, Italy
<p>The AgrImOnIA dataset is a comprehensive dataset relating air quality and livestock (expressed as the density of bovines and swine bred) along with weather and other variables. The AgrImOnIA Dataset represents the first step of the <a href="http://www.agrimonia.net">AgrImOnIA project</a>. The purpose of this dataset is to give the opportunity to assess the impact of agriculture on air quality in Lombardy through statistical techniques capable of highlighting the relationship between the livestock sector and air pollutants concentrations.</p> <p>The building process of the dataset is detailed in the <strong>companion paper:</strong></p> <p>A. Fassò, J. Rodeschini, A. Fusta Moro, Q. Shaboviq, P. Maranzano, M. Cameletti, F. Finazzi, N. Golini, R. Ignaccolo, and P. Otto (2023). Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy. <em>SCIENTIFIC DATA</em>, 1-19.</p> <p>available <a href="https://rdcu.be/c7T9H">here</a>.</p> <p>This dataset is a collection of estimated daily values for a range of measurements of different dimensions as: air quality, meteorology, emissions, livestock animals and land use. Data are related to Lombardy and the surrounding area for 2016-2021, inclusive. The surrounding area is obtained by applying a 0.3° buffer on Lombardy borders.</p> <p>The data uses several aggregation and interpolation methods to estimate the measurement for all days.</p> <p>The files in the record, renamed according to their version (es. .._v_3_0_0), are:</p> <ul> <li> <p>Agrimonia_Dataset.csv(.mat and .Rdata) which is built by joining the daily time series related to the AQ, WE, EM, LI and LA variables. In order to simplify access to variables in the Agrimonia dataset, the variable name starts with the dimension of the variable, i.e., the name of the variables related to the AQ dimension start with 'AQ_'. This file is archived also in the format for MATLAB and R software. </p> </li> <li> <p>Metadata_Agrimonia.csv which provides further information about the Agrimonia variables: e.g. sources used, original names of the variables imported, transformations applied.</p> </li> <li> <p>Metadata_AQ_imputation_uncertainty.csv which contains the daily uncertainty estimate of the imputed observation for the AQ to mitigate missing data in the hourly time series. </p> </li> <li> <p>Metadata_LA_CORINE_labels.csv which contains the label and the description associated with the CLC class. </p> </li> <li> <p>Metadata_monitoring_network_registry.csv which contains all details about the AQ monitoring station used to build the dataset. Information about air quality monitoring stations include: station type, municipality code, environment type, altitude, pollutants sampled and other. Each row represents a single sensor.</p> </li> <li> <p>Metadata_LA_SIARL_labels.csv which contains the label and the description associated with the SIARL class.</p> </li> <li> <p>AGC_Dataset.csv(.mat and .Rdata) that includes daily data of almost all variables available in the Agrimonia Dataset (excluding AQ variables) on an equidistant grid covering the Lombardy region and its surrounding area. </p> </li> </ul> <p>The Agrimonia dataset can be reproduced using the code available at the GitHub page: <a href="https://github.com/AgrImOnIA-project/AgrImOnIA_Data">https://github.com/AgrImOnIA-project/AgrImOnIA_Data</a></p> <p><strong>UPDATE 31/05/2023</strong> <strong>- NEW RELEASE - V 3.0.0</strong></p> <p>A new version of the dataset is released: Agrimonia_Dataset_v_3_0_0.csv (.Rdata and .mat), where variable <em>WE_rh_min, WE_rh_mean and WE_rh_max </em>have been recomputed due to some bugs<em>.</em></p> <p>In addition, two new columns are added, they are <em>LI_pigs_v2 and LI_bovine_v2 </em>and represents the density of the pigs and bovine (expressed as animals per kilometer squared) of a square of size ~ 10 x 10 km centered at the station localisation.</p> <p>A new dataset is released: the Agrimonia Grid Covariates (AGC) that includes daily information for the period from 2016 to 2020 of almost all variables within the Agrimonia Dataset on a equidistant grid containing the Lombardy region and its surrounding area. The AGC does not include AQ variables as they come from the monitoring stations that are irregularly spread over the area considered.</p> <p><strong>UPDATE 11/03/2023</strong> <strong>- NEW RELEASE - V 2.0.2</strong></p> <p>A new version of the dataset is released: Agrimonia_Dataset_v_2_0_2.csv (.Rdata), where variable <em>WE_tot_precipitation </em>have been recomputed due to some bugs<em>.</em></p> <p>A new version of the metadata is available: Metadata_Agrimonia_v_2_0_2.csv where the spatial resolution of the variable <em>WE_precipitation_t </em>is corrected.</p> <ul> </ul> <p><strong>UPDATE 24/01/2023</strong> <strong>- NEW RELEASE - V 2.0.1</strong></p> <p>minor bug fixed</p> <p><strong>UPDATE 16/01/2023</strong> <strong>- NEW RELEASE - V 2.0.0</strong></p> <p>A new version of the dataset is released, Agrimonia_Dataset_v_2_0_0.csv (.Rdata) and Metadata_monitoring_network_registry_v_2_0_0.csv. Some minor points have been addressed:</p> <ul> <li>Added values for <em>LA_land_use</em> variable for Switzerland stations (in Agrimonia Dataset_v_2_0_0.csv)</li> <li>Deleted incorrect values for <em>LA_soil_use</em> variable for stations outside Lombardy region during 2018 (in Agrimonia Dataset_v_2_0_0.csv)</li> <li>Fixed duplicate sensors corresponding to the same pollutant within the same station<em> </em>(in Metadata_monitoring_network_registry_v_2_0_0.csv)</li> </ul>
zbMATH Open Access Subset
<p>The dataset contains two tables as csv files.</p> <p>1) documents_in_oa_series</p> <p>is a list of zbMath Open documents in serials where the description contains the word "Open Access". Note that some documents did not appear yet or might have been retracted. Thus when fetching information from the oai-pmh API or the website, be prepared to handle non-existing documents.</p> <p>2) oa_links</p> <p>Lists all links from zbMATH Open documents to fulltext matched by either unpaywall or arxiv.</p> <p>The meaning of the fields is:</p> <ul> <li><strong>zbmath_id</strong> Unique identifier from zbMATH Open. Prefix with <code>https://zbmath.org/</code> to visit additional information on the article. For example, <code>5635019</code> is associated with <a href="https://zbmath.org/5635019">https://zbmath.org/5635019</a></li> <li><strong>link</strong> link to the fulltext. Note that those links are not fully reliable. We estimate a success rate of 90%</li> </ul> <p>Note that there is an overlap between 1 and 2. So some documents are published in OA serials and have arxiv or unpaywall links at the same time.</p> <p> </p>
Open-population models for estimating roadkill rates - Data and R Code
<p>Roadkill carcass capture-recapture data, capture histories for four and eight-occasion designs, and R code (with JAGS code) for roadkill rates estimation.</p>
CS3MESH4EOSC Final Event Science Mesh - Unlocking Open Science and Collaborative Research Landscape
<p>The recap video of CS3MESH4EOSC final event. The CS3MESH4EOSC final event, took place on 22 June 2023, at the EGI Conference in Poznan (Poland), showcased how the Science Mesh is contributing to an easier and more robust open science across Europe, thanks to novel approaches for data sharing and synchronisation.</p> <p><strong>The first half of the event</strong> will count with live demonstrations, where each data service from the Science Mesh will be presented from a user-perspective point of view. Event attendees will get practical information on how they can join the Science Mesh as a researcher, a software developer or a service provider. The event will also bring together representatives of Science Mesh use cases, who will explain how Science Mesh is making a difference in their lives, thanks to easier data sharing and synchronisation. A panel discussion with representatives of different sectors, from research to industry and education, will discuss the most urgent trends & priorities for cross-border science collaboration between different sciences.</p> <p><strong>The second half of the event</strong> will be focused on the technical novelties within the Science technical foundation. A series of demonstrations will be presented, followed by a panel discussion, with representatives of CS3MESH4EOSC members that are part of the EOSC Task Forces, on how the Science Mesh contributes to EOSC’s success and the overall EOSC Strategic Research and Innovation Agenda (SRIA).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.