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564 results for “June”
Ocean drifters from oil-on-water exercise in North Sea (Frigg oil field) June 2019
<p>Ocean drifters from oil-on-water exercise in North Sea (Frigg oil field) June 2019. Described in more detail in Brekke, C., Espeseth, M. M., Dagestad, K.-F., Röhrs, J., Hole, L. R., & Reigber, A. (2021). Integrated analysis of multisensor datasets and oil drift simulations - a free-floating oil experiment in the open ocean. Journal of Geophysical Research: Oceans, 126, e2020JC016499. https://doi.org/10.1029/2020JC016499</p> <p>Work is funded by grant no. 237906 (CIRFA) of the Norwegian Research Council.</p>
DATABASE OF THE DIGITAL ELEVATION MODELS OF THE SKEIÐARÁRSANDUR KETTLE-HOLES (S ICELAND), JUNE 2022 - Part II. VIDEO
<p>The database contains 83 video files in .MOV format, shot by a digital camera at 23.98 frames per second. The average length of videos is 100–600 seconds. They are documentation of fieldwork carried out in June 2022, aimed at preparing the footage to generate high-resolution digital elevation models (Part I) using the 'Structure from Motion' technique. The study covered kettle-holes of the glacial flood origin located at Skeiðarársandur in S Iceland.</p>
Moored echo and turbidity measurements in the Southern Adriatic Sea at mooring site BB and FF, March 2012-June 2020
<p>This data set includes four files (CSV format) containing observational data from two oceanographic moorings, BB and FF, located in the Southern Adriatic Sea from the period between March 2012 and June 2020. The stand-alone moorings are equipped with a 300 kHz ADCP-RDI system, which measures currents along the last 100 meters of the water column and a CTD recorder equipped with SeaPoint turbidity meter sensor located approximatively 10 m above the bottom. The turbidity sensor measures in a range of 0-25 FTU. Moorings were configured and maintained for continuous long-term monitoring following the approach of the CIESM Hydrochanges Program (www.ciesm.org/marine/programs/hydrochanges.html). The moorings are currently operational as from 2021 they have joined the southern Adriatic Sea submarine observatory system of the EMSO-ERIC European Consortium. The data were subjected to quality control (QC) and the coding numbers used, shown in a dedicated column, follow the SeaDataNet L20 measurement qualifiers flags. QC applied on echo data consists of detecting signal anomalies due to interactions with the seafloor and identifying if the signal falls below a minimum threshold for which the value is no longer considered reliable. For turbidity data, QC is addressed to the detections of possible spikes, anomalies, and sensor saturation in the recordings.</p>
Sedimentation Event Sensor images (26 October 2015–18 June 2015, 3900 m deep at Station M, NE Pacific)
<p>Images taken by the Sedimentation Event Sensor (26 October 2015–18 June 2015, 3900 m deep at Station M, NE Pacific) . See <a href="https://doi.org/10.1016/j.dsr2.2020.104763">https://doi.org/10.1016/j.dsr2.2020.104763</a> for details</p> <p> </p> <p>Huffard, C. L., Durkin, C. A., Wilson, S. E., McGill, P. R., Henthorn, R., & Smith Jr, K. L. (2020). Temporally-resolved mechanisms of deep-ocean particle flux and impact on the seafloor carbon cycle in the northeast Pacific. <em>Deep Sea Research Part II: Topical Studies in Oceanography</em>, <em>173</em>, 104763.</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>
High-quality NEID Solar Observations (January 2021 - June 2022)
<p>NExScI archives all NEID Solar observations at <a href="https://neid.ipac.caltech.edu/search_solar.php">https://neid.ipac.caltech.edu/search_solar.php</a>. NEID takes data even during inclement weather, and the official <a href="https://neid.ipac.caltech.edu/docs/NEID-DRP/index.html">NEID Data Reduction Pipeline</a> does not differentiate between those taken in excellent or poor observing conditions. This CSV file contains a list of all NEID Solar observations from January 1, 2021 to June 13, 2022, identifies which are currently believed to be of high-quality, and provides diagnostic information used (so users can easily adapting the selection criterion for their needs).</p> <p><strong>Columns:</strong></p> <p>filename: Name of file as provided by NExScI</p> <p>bjd: Barycentric Julian Date</p> <p>mask: true for observations considered of high quality; false for observations suspected to be low quality; See selection criterion specified below</p> <p>pyrflux_mean: mean flux observed by pyrheliometer during exposure</p> <p>pyrflux_rms: root mean square deviation from mean of flux observed by pyrheliometer during exposure</p> <p>expmeter_mean: mean of exposure meter during exposure (summed over wavelength channels)</p> <p>expmeter_rms: root mean square deviation from mean of exposure meter during exposure (summed over wavelength channels)</p> <p>airmass: airmass of sun a time of observation</p> <p>hour_angle: hour angle of sun at time of observation</p> <p>driftfun: value of DRIFTFUN copied from FITS header of filename</p> <p>wavecal: value of WAVECAL copied from FITS header of filename</p> <p>expmeter_mean_blue: mean of exposure meter during exposure (summed over bluest third of wavelength channels)</p> <p>expmeter_mean_green: mean of exposure meter during exposure (summed over middle third of wavelength channels)</p> <p>expmeter_mean_red: mean of exposure meter during exposure (summed over reddest third of wavelength channels)</p> <p> </p> <p>Selection criteria for setting mask to true:</p> <p>1. Require E_VER from fits file matches v1.1.* (Data processed with common minor version of NEID DRP)</p> <p>2. Require driftfun == "dailymodel0" and wavecal == "LFCplusThAr" (Wavelength calibration used)</p> <p>3. Start time of exposure lies between 17:30:00 and 22:12:00 (Removes data taken while wavelength calibration is changing rapidly)</p> <p>4. Exclude dates: October 2, 2021 to October 27, 2021 (due to a cabling issue)</p> <p>5. Require airmass <= 2.25 (Exclude data taken at high airmass)</p> <p>6. Require mean_pyroflux >= 10^2.95 (Require sufficient flux reaching the pyrheliometer)</p> <p>7. Require expmeter_mean >= 1.0e5 (Require sufficient flux reaching the exposure meter)</p> <p>8. Require rms_pyroflux <= 0.0035 * mean_pyroflux (Steady atmospheric transparency)</p> <p>9. Require expmeter_rms <= 0.003 * expmeter_mean (Steady transparency & pointing)</p> <p>10. Require expmeter_mean >= 150 * pyrflux_mean (Good pointing)</p> <p>11. Exclude dates: June 6, 2021, June 16, 2021, July 7, 2021, July 18, 2021, and July 19, 2021 (Dates with poor wavelength calibrations. This list may be updated upon further analysis.)</p>
Flash Poll June 2022 - Plant Health
<p>This survey by EFSA provides insights in terms of:</p> <p>• Europeans’ perception and knowledge of plant health, perceived benefits of healthy plants and perceived problems of risks with plant health; perceived concerns regarding the effects of plant pests and diseases on different areas;</p> <p>• Europeans’ awareness; acceptance; concerns re non-compliance; and knowledge of phytosanitary requirements for passengers carrying plants;</p> <p>• Europeans’ interest in plant health, concern about environmental matters, among others.</p> <p>The survey was implemented by the Teleperformance in 24 member states (i.e. all EU27 countries except Cyprus, Luxembourg, and Malta) between 20th and 24th of June 2022. A total of 8,600 respondents from different social and demographic groups completed the survey online in their mother tongue, with 300 to 500 respondents per country. These sample sizes provide robust results and ensure that responses are representative in each of the countries to be surveyed.</p> <p> </p> <p>The sample was nationally representative with respect to age and gender. Other demographic information collected included education, among others.</p>
June 2023 Supplement Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other)
<p><strong>June 2023 Supplement of Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other)</strong></p> <p><strong>Description</strong></p> <p>Supplementary dataset to:</p> <p>Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647</p> <p>This supplemental dataset consists of 283 RGB images and 283 associated labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts. Of these, 77 images-label pairs also have a corresponding NIR and SWIR satellite image. The 4 classes are 0=water, 1=whitewater, 2=sediment, 3=other</p> <p>These images and labels have been made using the Doodleverse software package, Doodler*. These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.</p> <p>Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. NIR, SWIR, Red, Green, and Blue bands only</p> <p><strong>File descriptions</strong></p> <ol> <li>classes.txt, a file containing the class names</li> <li>images.zip, a zipped folder containing the 3-band images of varying sizes and extents</li> <li>labels.zip, a zipped folder containing the 1-band label images</li> <li>overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (blue=0=water, red=1=whitewater, yellow=2=sediment, green=3=other)</li> <li>nir.zip</li> <li>swir.zip</li> </ol> <p><strong>References</strong></p> <p>Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647</p> <p>*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085<a href="https://doi.org/10.1029/2021EA002085">https://doi.org/10.1029/2021EA002085</a>. See <a href="https://github.com/Doodleverse/dash_doodler">https://github.com/Doodleverse/dash_doodler.</a></p> <p>**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a></p> <p> </p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) May 2017 - June 2018
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from May 2017 to June 2018 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C]; Salinity @ 6 m [psu]; Salinity @ 20 m [psu], Salinity @ 36 m [psu] </p>
Lake ecosystem metabolism estimates from 3 locations in Lake Sunapee, NH, USA during the summer stratified period from June to September 2018
Surface water lake ecosystem metabolism daily estimates during the 2018 summer stratified period (04 June - 22 Sept) at three locations within Lake Sunapee (NH, USA). Estimates at each site used previously published data from high-frequency temperature and dissolved oxygen sensors deployed in the lake: the Deep Site (LSPA et al., 2021a: full citation in Methods) and the Herrick Cove and Georges Mills sites (Ward et al., 2021: full citation in methods). The Deep Site was located near Loon Island in the main basin of the lake with 12 m total depth and the dissolved oxygen sensor was deployed 1 m below surface. The Herrick Cove site was in the north east cove of the lake with 6.5 m total depth at site and the dissolved oxygen sensor was deployed 1.75 m below surface. The Georges Mills site was in the northwest cove of the lake with 7 m total depth at site and the dissolved oxygen sensor deployed 1.75 m below surface. We used an inverse modeling approach, where the lake ecosystem model predicted diel changes in dissolved oxygen to estimate daily volumetric rates of gross primary production (GPP), respiration (R), and net ecosystem metabolism (NEM) using the in-lake buoy measurements at each site and wind and surface PAR from the meteorological station at the Deep Site buoy (LSPA et al., 2021b). Raw metabolism estimates were QA/QC'd to generate this final metabolism estimate dataset following protocols described in the Methods section of this dataset.
Community composition, richness, and density of endobionts from two sponge species in Crete, Greece, June 2021
These data was collected as part of a study titled "The “Single Hotel” hypothesis – Does sponge abundance affect endobionts’ diversity?" that was conducted in the island of Crete, Greece in June 2021. It includes collection of 30 sponge specimens of the common species (Agelas oroides and Sarcotragus foetidus) via SCUBA diving, their dissection and removal and identification of all endobionts living withing them (macroinvertebrates). The diversity of endobionts was then calculated and correlated with sponge properites (such as volume), and the sponges area and site of collection.
Carbon Dynamics in the Hyporheic Zone of a Headwater Mountain Stream in the Cascade Mountains, Oregon – Watershed 1 at HJA – June 2013 to March 2014
This study investigated carbon dynamics in the hyporheic zone of a steep, forested catchment in the Cascade Mountains of western Oregon, USA. Water samples were collected monthly from a headwater stream and well network during baseflow conditions from July to December 2013 and again in March 2014. We also sampled during one fall storm event, collecting pre-storm, rising leg, and extended high flow samples. The well network is located at the base of Watershed 1 (WS1) of the H.J. Andrews Experimental Forest and spans the full width of the floodplain (~14 m) along a 29 m reach of stream. We measured pH, temperature, water level, major anions, major cations, DOC, DIC, and total alkalinity. Flow paths, travel time to wells and hydraulic conductivity were available from previous studies.
Three synoptic surveys of streams throughout a 48km2 watershed near Toolik Lake, AK in spring (early-June), summer (mid-July), and fall (mid-September) 2011.
To determine temporal and spatial patterns in arctic stream biogeochemistry we conducted three synoptic surveys of streams throughout a 48km2 watershed near Toolik Lake, AK in spring (early-June), summer (mid-July), and fall (mid-September) 2011. During each synoptic survey, we sampled 52 sites within a period of four days to minimize the effect of temporal hydrologic variability. At each site we measured stream temperature, pH, and conductivity and sampled water for solute analysis.
Belowground foodweb biomass from moist acidic tundra nutrient addition plots (since 1989, 1996, 2006) sampled June and August 2010.
Biomass of belowground community groups (bacteria, fungi, protozoa, nematodes, rotifers, tardigrades) determined for organic and mineral soils in moist acidic tundra.
Precipitation cations and anions for June, July and August from a wet/dry precipitation, University of Alaska Fairbanks Toolik Field Station, North Slope of Alaska (68 degrees 37' 42"N, 149 degrees 35' 46"W), Arctic LTER 1989 to 2003
Precipitation, collected from a wet/dry precipitation collector located near University of Alaska Fairbanks Toolik Field Station, North Slope of Alaska (68 degrees 37' 42"N, 149 degrees 35' 46"W) was sent out for standardized EPA rain water analysis. Nutrient chemistry was also run on a sub sample at the field station.
Number of cyanobacteria in Toolik Lake at 1 meter depth during June, July and August 1996 , Arctic LTER, summer 1996.
Number of cyanobacteria in Toolik Lake at 1 meter depth during June, July and August 1996. Samples were transported to the Dept. of Fisheries and Oceans in West Vancouver, British Columbia, Canada for analysis.
Known-fate survival information for radio-tagged snowshoe hares captured in Bonanza Creek Experimental Forest from June 2008 to November 2012
This dataset contains known-fate survival information for radio-tagged snowshoe hares captured in two 200 x 450 m live-trapping grids in Bonanza Creek Experimental Forest from June 2008 to November 2012. The data can be sorted and viewed by year, site, number at risk, and number of mortalities.
NOAA Daily Surface Meteorologic Data at NCDC Tavernier Station (ID-088841)(FCE), South Florida from June 1936 to May 2009
The National Climatic Data Center's (NOAA) daily mean, maximum, and minmium air temperatures and daily precipitation collected at Tavernier Station (Coop ID-088841).
Groundwater and surface water phosphorus concentrations, Everglades National Park (FCE), South Florida for June, July, August and November 2003
Seawater intrusion into a coastal aquifer mixes with the discharging fresh water to form a zone of mixed composition. This mixing zone is considered to be geochemical important in a carbonate aquifer as an area of enhanced carbonate mineral dissolution and,or precipitation. Ion exchange reactions are also common within the mixing zone as the dominant cation in seawater, sodium, replaces other ions adsorbed to the aquifer matrix. Phosphorus has a strong affinity for adsorption to calcium carbonate minerals. Both the dissolution of calcium carbonate minerals as well as ion exchange reactions have the potential to release phosphorus from the aquifer matrix to the groundwater. Discharge of this phosphorus-laden groundwater, as induced by the fresh/saline water interface, may then be an additional source of phosphorus to the overlying coastal environments. Both surface water and groundwater were collected across the seawater intrusion zone of the coastal Everglades in south Florida during the summer of 2003. Hydrogen sulfide was released from the groundwater wells during sampling, indicating the groundwater was most likely anoxic. In order to reduce the potential exposure of the groundwater to oxygen during sampling, groundwater samples were collected in the following manner. Groundwater wells were first purged of at least three well volumes prior to sampling. Water samples were then collected using a submersible pump with a 16-gauge needle fitted at the end of the discharge hose. The needle was pushed through a rubber stopper covering an acid-washed vacutainer that was first flushed with nitrogen gas to remove and oxygen, then evacuated with a vacuum pump to establish a negative pressure within the vacutainer. Water samples were stored on ice and transported to the laboratory. Samples were processed for total phosphorus using colorimetery following dry-oxidation/acid hydroloysis methods within 1 to 5 days following sample collection. Salinity of the groundwater and surf
Water Temperature measured at Shark River, Everglades National Park (FCE) from July 2007 to June 2011
Between 2007 and 2011 we placed HOBO continuous water temperature loggers at 17 locations in the Shark River Estuary for various lengths of times. These stations expanded FCE's spatial coverage in terms of temperature monitoring in the estuary.
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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.