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zenodo44/100

Value creation stories anonymized open data set (Immunization Agenda 2030 Full Learning Cycle, 7 March - 20 June 2022)

# Title Immunization Agenda 2030 (IA2030) 1st Movement Full Learning Cycle (FLC 2022) – “How are you doing?” Value Creation Stories Survey (Version 1.0) # Research audience 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. # Credits ## Author The Geneva Learning Foundation 18 avenue Louis Casaï CH-1209 Geneva, Switzerland research@learning.foundation ### Principal Investigator and corresponding author Reda Sadki, The Geneva Learning Foundation (TGLF) reda@learning.foundation ## Project partners Bridges to Development University of South Australia Centre for Change and Complexity in Learning (C3L) ## Roles and responsibilities - Design: The Geneva Learning Foundation - Implementation (sample collection): The Geneva Learning Foundation - Processing: The Geneva Learning Foundation, Bridges for Development, Centre for Complexity and Change in Learning (C3L) - Anonymization: The Geneva Learning Foundation and Bridges for Development - Data cleaning: Bridges to Development - Submission: The Geneva Learning Foundation ## Funding sources or sponsorship that supported the data collection Wellcome, Bill & Melinda Gates Foundation (BMGF) ## Recommended citation 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 # Description of the sample ## File list: This file is IA2030_FLC_2022_Value_Creation_Stories.README.md 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.  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.  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. 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. 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. ## Relationship between files The questions codes data set are the same code as the column variables and can be connected. ## Related data sets 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.  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) ## Other publicly accessible locations of the data 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/ ## 1. Purpose and Objectives ### Primary goal of the survey: This survey had two goals in the context of TGLF’s IA2030 Movement Full Learning Cycle programme (2022):  1. Provide an asynchronous mechanism for support between peers (participants) and from the TGLF team; and 2. collect and measure programme participants’ value creation stories (VCS) during the programme. 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. 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. ## 2. Population and Sample 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. 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). ### Languages: French and English ## 3. Survey Design and Methods Data collection period: 7 March 2022 – 20 June 2022 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?”. 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. 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.   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. ### Software- or Instrument-specific information needed to interpret the data - Automated translation of French data was performed using [Google Translate](https://translate.google.com/?sl=en&tl=fr&op=docs) - Methods used for removing or anonymizing personal identifiers or sensitive information: - 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.** - 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) ### Data collection start and end dates: 7 March 2023 until 20 June 2023 #### Events or circumstances during data collection that may have influenced results: No requests for responses were sent during TGLF’s “Term break” between 16-30 April 2022. ## 4. Data Processing and Cleaning - Incomplete or inconsistent responses: Not cleaned, as respondents were able to opt out of specific sections of survey or skip questions. - Data transformations or imputations: None - Treatment of outliers or extreme values: None ## 5. Variables and Measures 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. ## 6. Data Quality and Reliability All the responses done before or after the FLC period (7 March – 20 June 2022) were excluded of the sample. ## 7. Data Privacy and Anonymization ### Methods used for removing or anonymizing personal identifiers or sensitive information: - 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. - Limitation: MD5 hashing is a one-way function; it is not possible to dehash the data and recover the original information.  - 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.) ## 8. Data Availability and Accessibility This data set is made available on Zenodo.org in the Zenodo community “Movement for Immunization Agenda 2030 (IA2030)” https://zenodo.org/communities/ia2030/ Requests for additional information should be addressed to research@learning.foundation. 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. ### Other publicly accessible locations of the data 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/ ### Related data sets 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 ## 10. Ethical Considerations ### Ethical guidelines followed during data collection: 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) ### Informed consent and participant rights information: 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”. 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.” Data protection and confidentiality 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. # Copyright and license 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) © The Geneva Learning Foundation 2023 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/. 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.” Any mediation relating to disputes arising under the license shall be conducted in accordance with the mediation rules of the World Intellectual Property Organization. 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. 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. 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. This data set contains individual views and does not necessarily represent the decisions or the policies of the Foundation. Version 1.0 (31 March 2023): reviewed internally; reviewed externally.  # References 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. 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. 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. 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 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. 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. # Questionnaire ## Hello {{fname}} {{lname}}. How are you doing in the Movement for Immunization Agenda 2030? ## Do you need help? Do you want to share your experience? We would like to know how you are doing. - I am doing fine. - I have a problem and need help. - I want to share my experience. ## 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. ## 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. ## What do you need help with? - I do not know what I am supposed to do - I need help with my IA2030 challenge - I want to catch up - I have poor connectivity - I have a problem with technology - Something else ## Tell us more about the problem you are facing. 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. ## Have you tried taking time to read and follow the instructions?  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.  Use your email email to log in. Don’t remember your password? Click here to recover it https://www.learning.foundation/password/new.  Take the time to read the instructions – and then follow them step-by-step. Do not forget to come back to finish this questionnaire.  ## Do not suffer in silence. It sounds like you should ask for help from your Movement colleagues.  Click here to connect with colleagues https://t.me/IA2030 in the IA2030 Movement Telegram channel. - 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. - 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. - 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! - 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. - You can listen to most sessions in our podcast. This is audio-only, like listening to radio on demand. ## Could we ask you five questions about your participation? 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. Yes No ## Participation changed me as a professional (change in skills, attitudes, identity, self-confidence, feelings, etc.). ## Can you explain how participation changed you as a professional? ## Participation affected my social connections (change in the number, quality, frequency, emotions, etc.) ## Can you explain how participation affected your social connections? ## Participation helped my professional practice (get new ideas, insights, materials, procedures, etc.) ## Can you explain how participation helped your professional practice? ## Participation changed my ability to influence my world as a professional (enhance my voice, contribution, status, recognition, etc.) ## Can you explain how participation changed your ability to influence your world as a professional? ## Participation made me see my world differently (change in perspective, new understandings of the situation, redefine success, etc.) ## Can you explain how participation made you see your world differently? ## Do you remain committed to the Movement for Immunization Agenda 2030? You remain a Member even if you are not actively participating. - Yes, and I am actively participating - Yes, but I am not actively participating - No, I wish to leave the Movement ## We are sorry to see you go. Could you let us know what went wrong? What could we have done better to support you? - (Or just hit RETURN to skip.) ## What is the email you are using? We need your email to follow up and respond to what you shared with us. Do not forget to press the SUBMIT button. ## URL redirection upon completion: https://www.learning.foundation/products/movement-for-immunization-agenda-2030-full-learning-cycle-1-march-2022 ## Thank you [fname] [lname] for sharing your feedback. 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. Click here to check http://cal.ae/eudusmw the IA2030 Movement calendar so you do not miss upcoming event

opencc-by-4.0Mar 2023View details →

zenodo44/100

Formation and cycling data for Na-ion batteries from high-throughput synthesis, coating, and assembly

Formation and cycling data from a combinatorial/high-throughput upscaling process for the production and characterization of sodium-ion batteries. The process involves batch synthesis, screen printing of electrodes, robotic cell assembly, and battery cycling. The goal of this study was to test how fast a new chemistry (to the group) could be introduced into the workflow and if we are able to enhance efficiency, accuracy, and reproducibility. The cathode material, Na0.9[Cu0.22Fe0.30Mn0.48]O2, was synthesized through a solid-state reaction (Na2CO3 (purity 99.5 %), CuO (purity 99.7 %), Fe2O3 (purity 99.9 %) and Mn2O3 (purity 98 %) at 850°C for 15h) in a pressed pellet (10 MPa) that was ground up again to make a slurry. The electrodes were prepared using screen printing, which offers simplicity, low cost, and quick coating of large areas in a reproducible manner. The binder was sodium carboxymethyl cellulose to make the electrodes water processable in air.  The assembled batteries utilized the synthesized cathode material and hard carbon as the anode, with a glass fiber separator and a 1M NaPF6 EC:EMC 3:7 with 2 wt% FEC electrolyte.

opencc-by-4.0May 2023View details →

zenodo44/100

Data describing the life cycle and material flows of neodymium contained in products

Assumptions used to calculate flows of neodymium (Nd) in Europe. For various products (consumer products and industrial goods), the dataset describes the following properties: <ul> <li>lifespan,</li> <li>product weight,</li> <li>neodymium content,</li> <li>end-of-life (EoL) fate,</li> <li>component weight,</li> <li>market share of Nd-containing components</li> </ul> The classification of products is based on UNU Keys.

opencc-by-4.0Jan 2023View details →

edi44/100

Delta smelt (Hypomesus transpacificus) life cycle model input data.

Synthesized data used for fitting delta smelt population dynamics models, essentially consisting of predictor variables (environmental conditions and indices of prey and predators) and response variables (abundance indices). Input data is sourced from a variety of both federal and California state government monitoring programs taking place within the San Francisco Estuary, California. These include California Department of Fish and Wildlife fish surveys, Interagency Ecological Program's Environmental Monitoring Program for zooplankton, California Department of Water Resources' Dayflow, and United States Geological Survey water monitoring data. The sourced data are recorded from sub-hourly to monthly time scales and at various spatial scales, aggregated at monthly or greater time scales using summary statistics (e.g. means) and are not spatially explicit but use spatial stratification approaches for statistic calculation as appropriate.

openCC (other)Apr 2024View details →

edi44/100

Hubbard Brook Experimental Forest: Landscape scale (valley-wide) soil carbon and nitrogen cycling data

The valley-wide plots are a grid of 431 sites along fifteen N–S transects established at 500-m intervals spanning the entire Hubbard Brook Valley. This dataset includes total soil carbon, nitrogen and organic matter content, potential net nitrogen mineralization and nitrification rates, microbial respiration rates, soil water content and holding capacity, soil ammonium and nitrate concentrations, soil pH, and tree composition in a subset of 100 randomly selected plots in 2000. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. An analysis of these data can be found in: Venterea, R. T., Lovett, G. M., Groffman, P. M., & Schwarz, P. A. (2003). Landscape patterns of net nitrification in a northern hardwood-conifer forest. Soil Science Soc. Amer. J., 67, 527–539. https://doi.org/10.2136/sssaj2003.5270

openCC (other)Jul 2021View details →

zenodo40/100

Code and data for "Contrasting upper and deep ocean oxygen response to protracted global warming," by Frölicher et al., Global Biogeochemical Cycles, 34, e2020GB006601: https://doi.org/10.1029/2020GB006601

This file contains the data and python/NCL scripts that have been used for the analysis in this paper.

opencc-by-4.0Aug 2020View details →

dryad40/100

Data from: First records of complete annual cycles in water rails Rallus aquaticus show evidence of itinerant breeding and a complex migration system

In water rails Rallus aquaticus, northern and eastern populations are migratory while southern and western populations are sedentary. Few details are known about the annual cycle of this elusive species. We studied movements and breeding in water rails from southernmost Norway where the species occurs year-round. Colour-ringed wintering birds occurred only occasionally at the study site in summer, and vice versa. Geolocator tracks revealed that wintering birds (n = 10) migrated eastwards in spring to breed on both sides of the Baltic Sea, whereas a single breeding bird from the study site wintered in north Italy. Ambient light records of geolocator birds further indicated that all but one incubated 2–4 clutches per season. By combining information on incubation and movement, we found evidence for itinerant breeding in three individual birds: After a first breeding attempt (one did not incubate), all moved 129–721 km to breed again. This behaviour is rarely recorded in birds and was unexpected because the water rail is described as monogamous with both parents caring for eggs and chicks. The study greatly improves our knowledge about the annual cycle and reproduction in water rails. However, more studies are warranted to evaluate the generality of our findings and causes of breeding itinerancy.

opencc-zeroOct 2020View details →

zenodo40/100

Wrist-mounted IMU data towards the investigation of free-living human eating behavior - the Free-living Food Intake Cycle (FreeFIC) dataset

Introduction The Free-living Food Intake Cycle (FreeFIC) dataset was created by the <a href="http://mug.ee.auth.gr">Multimedia Understanding Group</a> towards the investigation of in-the-wild eating behavior. This is achieved by recording the subjects’ meals as a small part part of their everyday life, unscripted, activities. The FreeFIC dataset contains the $3D$ acceleration and orientation velocity signals ($6$ DoF) from $22$ in-the-wild sessions provided by $12$ unique subjects. All sessions were recorded using a commercial smartwatch ($6$ using the Huawei Watch 2™ and the MobVoi TicWatch™ for the rest) while the participants performed their everyday activities. In addition, FreeFIC also contains the start and end moments of each meal session as reported by the participants. Description FreeFIC includes $22$ in-the-wild sessions that belong to $12$ unique subjects. Participants were instructed to wear the smartwatch to the hand of their preference well ahead before any meal and continue to wear it throughout the day until the battery is depleted. In addition, we followed a self-report labeling model, meaning that the ground truth is provided from the participant by documenting the start and end moments of their meals to the best of their abilities as well as the hand they wear the smartwatch on. The total duration of the $22$ recordings sums up to $112.71$ hours, with a mean duration of $5.12$ hours. Additional data statistics can be obtained by executing the provided python script stats_dataset.py. Furthermore, the accompanying python script viz_dataset.py will visualize the IMU signals and ground truth intervals for each of the recordings. Information on how to execute the Python scripts can be found below. <pre><code># The script(s) and the pickle file must be located in the same directory. # Tested with Python 3.6.4 # Requirements: Numpy, Pickle and Matplotlib # Calculate and echo dataset statistics $ python stats_dataset.py # Visualize signals and ground truth $ python viz_dataset.py</code></pre> FreeFIC is also tightly related to Food Intake Cycle (FIC), a dataset we created in order to investigate the in-meal eating behavior. More information about FIC can be found <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>. Publications If you plan to use the FreeFIC dataset or any of the resources found in this page, please cite our work: <pre><code>@article{kyritsis2020data, title={A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, journal={IEEE Journal of Biomedical and Health Informatics}, year={2020}, publisher={IEEE}}</code></pre> <pre><code>@inproceedings{kyritsis2017automated, title={Detecting Meals In the Wild Using the Inertial Data of a Typical Smartwatch}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, booktitle={2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, year={2019}, organization={IEEE}} </code></pre> Technical details We provide the FreeFIC dataset as a <a href="https://docs.python.org/3/library/pickle.html">pickle</a>. The file can be loaded using Python in the following way: <pre><code class="language-python">import pickle as pkl import numpy as np with open('./FreeFIC_FreeFIC-heldout.pkl','rb') as fh: dataset = pkl.load(fh)</code></pre> The dataset variable in the snipet above is a dictionary with $5$ keys. Namely: <ul> <li>'subject_id'</li> <li>'session_id'</li> <li>'signals_raw'</li> <li>'signals_proc'</li> <li>'meal_gt'</li> </ul> The contents under a specific key can be obtained by: <pre><code class="language-python">sub = dataset['subject_id'] # for the subject id ses = dataset['session_id'] # for the session id raw = dataset['signals_raw'] # for the raw IMU signals proc = dataset['signals_proc'] # for the processed IMU signals gt = dataset['meal_gt'] # for the meal ground truth </code></pre> The sub, ses, raw, proc and gt variables in the snipet above are lists with a length equal to $22$. Elements across all lists are aligned; e.g., the $3$rd element of the list under the 'session_id' key corresponds to the $3$rd element of the list under the 'signals_proc' key. sub: list Each element of the sub list is a scalar (integer) that corresponds to the unique identifier of the subject that can take the following values: $[1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20]$. It should be emphasized that the subjects with ids $15, 16, 17, 18, 19$ and $20$ belong to the held-out part of the FreeFIC dataset (more information can be found in the publication titled "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al). Moreover, the subject identifier in FreeFIC is in-line with the subject identifier in the FIC dataset (more info <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>); i.e., FIC’s subject with id equal to $2$  is the same person as FreeFIC’s subject with id equal to $2$. ses: list Each element of this list is a scalar (integer) that corresponds to the unique identifier of the session that can range between $1$ and $5$. It should be noted that not all subjects have the same number of sessions. raw: list Each element of this list is dictionary with the 'acc' and 'gyr' keys. The data under the 'acc' key is a $N_{acc} \times 4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw accelerometer measurements in $g$ (second, third and forth columns - representing the $x, y $ and $z$ axis, respectively). The data under the 'gyr' key is a $N_{gyr} \times 4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw gyroscope measurements in ${degrees}/{second}$(second, third and forth columns - representing the $x, y $ and $z$ axis, respectively). All sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>). Finally, the length of the raw accelerometer and gyroscope numpy.ndarrays is different $(N_{acc} \neq N_{gyr})$. This behavior is predictable and is caused by the Android platform. proc: list Each element of this list is an $M\times7$  numpy.ndarray that contains the timestamps, $3D$ accelerometer and gyroscope measurements for each meal. Specifically, the first column contains the timestamps in seconds, the second, third and forth columns contain the $x,y$ and $z$ accelerometer values in $g$ and the fifth, sixth and seventh columns contain the $x,y$ and $z$ gyroscope values in ${degrees}/{second}$. Unlike elements in the raw list, processed measurements (in the proc list) have a constant sampling rate of $100$ Hz and the accelerometer/gyroscope measurements are aligned with each other. In addition, all sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>). No other preprocessing is performed on the data; e.g., the acceleration component due to the Earth's gravitational field is present at the processed acceleration measurements. The potential researcher can consult the article "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al. on how to further preprocess the IMU signals (i.e., smooth and remove the gravitational component). meal_gt: list Each element of this list is a $K\times2$ matrix. Each row represents the meal intervals for the specific in-the-wild session. The first column contains the timestamps of the meal start moments whereas the second one the timestamps of the meal end moments. All timestamps are in seconds. The number of meals $K$ varies across recordings (e.g., a recording exist where a participant consumed two meals). Ethics and funding Informed consent, including permission for third-party access to anonymised data, was obtained from all subjects prior to their engagement in the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 727688 - <a href="https://bigoprogram.eu/">BigO: Big data against childhood obesity</a>. Contact Any inquiries regarding the FreeFIC dataset should be addressed to: Dr. Konstantinos KYRITSIS Multimedia Understanding Group (MUG) Department of Electrical & Computer Engineering Aristotle University of Thessaloniki University Campus, Building C, 3rd floor Thessaloniki, Greece, GR54124 Tel: +30 2310 996359, 996365  Fax: +30 2310 996398 E-mail: kokirits [at] mug [dot] ee [dot] auth [dot] gr

opencc-by-4.0Aug 2019View details →

zenodo40/100

Wrist-mounted IMU data towards the investigation of in-meal human eating behavior - the Food Intake Cycle (FIC) dataset

Introduction The Food Intake Cycle (FIC) dataset was created by the <a href="http://mug.ee.auth.gr">Multimedia Understanding Group</a> towards the investigation of in-meal eating behavior. The FIC dataset contains the triaxial acceleration and orientation velocity signals ($6$ DoF) from $21$ meal sessions provided by $12$ unique subjects. All meals were recorded in the restaurant of Aristotle University of Thessaloniki using a commercial smartwatch, the Microsoft Band $2$™ for ten out of the twenty-one meals and the Sony Smartwatch $2$™ for the remaining meals. In addition, the start and end moments of each food intake cycle as well as of each micromovement are annotated throughout the FIC dataset. Description A total of $12$ subjects were recorded while eating their launch at the university’s cafeteria. The total duration of the $21$ meals sums up to $246$ minutes, with a mean duration of $11.7$ minutes. Each participant was free to select the food of their preference, typically consisting of a starter soup, a salad, a main course and a desert. Prior to the recording, the participant was asked to wear the smartwatch to the hand that he typically uses in his everyday life to manipulate the fork and/or the spoon. A GoPro™ Hero $5$ camera was already set at the table of the participant using a small, $23$ cm in height, tripod facing the participant, including both the food tray and upper body part in it’s field of view. The purpose of video recording was to obtain ground truth data by manually annotating the IMU sequences based on the video stream. Participants were also asked to perform a clapping hand movement both at the start and end of the meal, for synchronization purposes (as this movement is distinctive in the accelerometer signal). No other instructions were given to the participants. It should be noted that the FIC dataset does not contain instances related with liquid consumption or eating without the fork, knife and spoon (e.g. eating directly with hands). The accompanying python script viz_dataset.py will visualize the IMU signals and food intake cycle (i.e., bite) ground truth intervals for each of the recordings. Information on how to execute the Python scripts can be found below. <pre><code class="language-python"># The script(s) and the pickle file must be located in the same directory. # Tested with Python 3.6.4 # Requirements: Numpy, Pickle and Matplotlib # Visualize signals and ground truth $ python viz_dataset.py</code></pre> FIC is also tightly related to FreeFIC, a dataset we created in order to investigate the in-the-wild eating behavior. More information on FreeFIC can be found <a href="https://zenodo.org/record/4421951">here</a> and <a href="https://mug.ee.auth.gr/free-food-intake-cycle-detection/">here</a>. Annotation Micromovements For all recordings, the start and end points of all $6$ micromovements of interest were manually labeled. The micromovements of interest include: <ul> <li>pick food, wrist manipulates a fork to pick food from the plate</li> <li>upwards, wrist moves upwards, towards the mouth area</li> <li>downwards, wrist moves downwards, away from the mouth area</li> <li>mouth, wrist inserts food in mouth</li> <li>no movement, wrist exhibits no movement</li> <li>other movement, every other wrist movement</li> </ul> The annotation process was performed in such a way that the start and end times of each micro-movement span the whole meal session, without overlapping each other. Food intake cycles For all recordings, we annotated the start and end points for each intake cycle (i.e. every bite). Each food intake cycle starts with a p, ends with a d and contains an m micromovement. Publications If you plan to use the FIC dataset or any of the resources found in this page, please cite our work: <pre><code>@article{kyritsis2019modeling, title={Modeling Wrist Micromovements to Measure In-Meal Eating Behavior from Inertial Sensor Data}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, journal={IEEE journal of biomedical and health informatics}, year={2019}, publisher={IEEE}}</code></pre> <pre><code>@inproceedings{kyritsis2017food, title={Food intake detection from inertial sensors using lstm networks}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, booktitle={International Conference on Image Analysis and Processing}, pages={411--418}, year={2017}, organization={Springer}}</code></pre> <pre><code>@inproceedings{kyritsis2017automated, title={Automated analysis of in meal eating behavior using a commercial wristband IMU sensor}, author={Kyritsis, Konstantinos and Tatli, Christina Lefkothea and Diou, Christos and Delopoulos, Anastasios}, booktitle={2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, pages={2843--2846}, year={2017}, organization={IEEE}}</code></pre> Technical details We provide the FIC dataset as a <a href="https://docs.python.org/3/library/pickle.html">pickle</a>. The file can be loaded using Python in the following way: <pre><code class="language-python">import pickle as pkl import numpy as np with open('./FIC.pkl','rb') as fh: dataset = pkl.load(fh)</code></pre> The dataset variable in the snipet above is a dictionary with $6$ keys. Namely: <ul> <li>'subject_id'</li> <li>'session_id'</li> <li>'signals_raw'</li> <li>'signals_proc'</li> <li>'meal_gt'</li> <li>'bite_gt'</li> </ul> The contents under a specific key can be obtained by: <pre><code>sub = dataset['subject_id'] # for the subject id ses = dataset['session_id'] # for the session id raw = dataset['signals_raw'] # for the raw IMU signals proc = dataset['signals_proc'] # for the processed IMU signals mm = dataset['mm_gt'] # for the micromovement ground truth bite = dataset['bite_gt'] # for the bite ground truth</code></pre> The sub, ses, raw, proc, mm and gt variables in the snipet above are lists with a length equal to $21$. Elements across all lists are aligned; e.g., the 3rd element of the list under the 'session_id' key corresponds to the 3rd element of the list under the 'signals_proc' key. sub: list Each element of the sub list is a scalar (integer) that corresponds to the unique identifier of the subject that can take values between $1$ and $12$. Moreover, the subject identifier in FIC is in-line with the subject identifier in the FreeFIC dataset (information available <a href="https://zenodo.org/record/4421951">here</a> and <a href="https://mug.ee.auth.gr/free-food-intake-cycle-detection/">here</a>); i.e., FIC’s subject with id equal to 2  is the same person as FreeFIC’s subject with id equal to 2. ses: list Each element of this list is a scalar (integer) that corresponds to the unique identifier of the session that can range between 1 and $3$. It should be noted that not all subjects have the same number of sessions. raw: list Each element of this list is dictionary with the 'acc', 'gyr' and 'offset' keys. The data under the 'acc' key is a $N_{acc}\times4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw accelerometer measurements in $g$ (second, third and forth columns - representing the $x, y$ and $z$ axis, respectively). The data under the 'gyr' key is a $N_{gyr} \times 4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw gyroscope measurements in $degrees/second$(second, third and forth columns - representing the $x, y$ and $z$ axis, respectively). All sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FreeFIC dataset (information available <a href="https://zenodo.org/record/4420039">here</a> and <a href="https://mug.ee.auth.gr/free-food-intake-cycle-detection/">here</a>). Finally, the length of the raw accelerometer and gyroscope numpy.ndarrays is different $N_{acc} \neq N_{gyr}$. This behavior is predictable and is caused by the Android/MS Band platforms. The offset key contains a float that is used to align the IMU sensor streams with the videos that were used for annotation purposes (videos are not provided). proc: list Each element of this list is an $M \times 7$  numpy.ndarray that contains the timestamps, $3D$ accelerometer and gyroscope measurements for each meal. Specifically, the first column contains the timestamps in seconds, the second, third and forth columns contain the $x,y$ and $z$ accelerometer values in $g$ and the fifth, sixth and seventh columns contain the $x, y$ and $z$ gyroscope values in $degrees/second$. Unlike elements in the raw list, processed measurements (in the proc list) have a constant sampling rate of 100 Hz and the accelerometer/gyroscope measurements are aligned with each other. In addition, all sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FreeFIC dataset (information available <a href="https://zenodo.org/record/4420039">here</a> and <a href="https://mug.ee.auth.gr/free-food-intake-cycle-detection/">here</a>). No other preprocessing is performed on the data; e.g., the acceleration component due to the Earth's gravitational field is present at the processed acceleration measurements. The potential researcher can consult the article "Modeling Wrist Micromovements to Measure In-Meal Eating Behavior from Inertial Sensor Data" by Kyritsis et al. on how to further preprocess the IMU signals (i.e., smooth and remove the gravitational component). mm: list Each element of this list is a $K \times 3$ numpy.ndarray. Each row represents a single micromovement interval. The first column contains the timestamps of the start moments in seconds, the second column the timestamps of the end moments in seconds and the third column a number representing the type of the micromovement. The identifier to micromovement mapping is provided below: $[1] \rightarrow$ no movement $[2] \rightarrow$ upwards $[3] \rightarrow$ downwards $[4] \rightarrow$ pick food $[5] \rightarrow$ mouth $[6] \rightarrow$ other movement bite: list Each element of this list is a $L\times2$ numpy.ndarray. Each row represents a single food intake event (i.e., a bite). The first column contains the start moments while the second column contains the end moments of each intake event. Both the start and end moments are provided in seconds. Ethics and funding Informed consent, including permission for third-party access to anonymised data, was obtained from all subjects prior to their engagement in the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 727688 - <a href="https://bigoprogram.eu/">BigO: Big data against childhood obesity</a>. Contact Any inquiries regarding the FIC dataset should be addressed to: Dr. Konstantinos KYRITSIS Multimedia Understanding Group (MUG) Department of Electrical & Computer Engineering Aristotle University of Thessaloniki University Campus, Building C, 3rd floor Thessaloniki, Greece, GR54124 Tel: +30 2310 996359, 996365  Fax: +30 2310 996398 E-mail: kokirits [at] mug [dot] ee [dot] auth [dot] gr

opencc-by-4.0Sep 2017View details →

zenodo40/100

Code and Source Data for "Knowledge-Guided Machine Learning can improve C cycle quantification in agroecosystems"

Datasets for code and Source Data for the study "Knowledge-Guided Machine Learning can improve C cycle quantification in agroecosystems" https://doi.org/10.1038/s41467-023-43860-5. All files belong to Licheng Liu and Zhenong Jin at University of Minnesota. deposit_code_v2.zip contains packaged codes and sample runs for KGML-ag-Carbon training, validation and implementations. Source Data.zip contains data for generating the figures inside the study.  Note: We used Pytorch 1.6.0 (<a href="https://pytorch.org/get-started/previous-versions/">https://pytorch.org/get-started/previous-versions/</a>, last access: 21 Oct 2023) and Python 3.7.11 (<a href="https://www.python.org/downloads/release/python-3711/">https://www.python.org/downloads/release/python-3711/</a>, last access: 21 Oct 2023) as the programming environment for model development. Statistical analysis, such as linear regression, was conducted using Statsmodels 0.14.0 (<a href="https://github.com/statsmodels/statsmodels/">https://github.com/statsmodels/statsmodels/</a>, last access: 21 Oct 2023) In order to use a GPU to speed-up the training process, we installed the CUDA Toolkit 10.1.243 (<a href="https://developer.nvidia.com/cuda-toolkit">https://developer.nvidia.com/cuda-toolkit</a>, last access: 21 Oct 2023).  To use the full kgml_lib function, please create a new environment with the same python and libs above.

opencc-by-4.0Nov 2023View details →

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Code and data for publication "Assessing carbon cycle projections from complex and simple models under SSP scenarios" published in "Climatic Change"

Data and scripts for the article "Assessing carbon cycle projections from complex and simple models under SSP scenarios" by I. Melnikova, P. Ciais, O. Boucher and K. Tanaka was accepted for publication in Climatic Change (https://doi.org/10.1007/s10584-023-03639-5) We use bash, CDO, and python.SSP2.xlsx contains preprocessed annual estimates of climate and carbon cycle variables from ESMs and SCMs used in the paper.Two bash scripts contain preprocessing cdo commands for ESM output.s SCMs were preprocessed directly in python.Jupyter notebook (python) contains preprocessing of data and plotting of all figures of the manuscript. The folder "additional" contains some more Excel files needed to run Jupyter-Notebook. Please adapt the folder names.If you have any questions, please contact the corresponding author Irina MELNIKOVA at melnikova . irina@nies.go.jp

opencc-by-4.0Nov 2023View details →

zenodo40/100

Changes in core-mantle boundary heat flux patterns throughout the supercontinent cycle: Data

This repository accompanies the paper   ``` Dannberg, J., Gassmoeller, R., Thallner, D., LaCombe, F., Sprain, C.: Changes in core-mantle boundary heat flux patterns throughout the supercontinent cycle. ```   This repository contains instructions for how to obtain the boundary conditions from GPlates, ASPECT code, data and model setups, and scripts for converting the ASPECT model output to spherical harmonics so it can be used in geodynamic simulations. To reproduce the workflow follow the steps:   - To create the velocity boundary conditions for the ASPECT models, download the plate reconstruction from 'Merdith, A.S., Williams, S.E., Collins, A.S., Tetley, M.G., Mulder, J.A., Blades, M.L., Young, A., Armistead, S.E., Cannon, J., Zahirovic, S. and Müller, R.D., 2021. Extending full-plate tectonic models into deep time: Linking the Neoproterozoic and the Phanerozoic. Earth-Science Reviews, 214, p.103477', which can be found here:   https://doi.org/10.5281/zenodo.4485738   To create the 'lat_lon_velocity' files, take the following steps in GPlates:   1. Load all of the files from the Merdith et al, 2021 plate reconstruction into a Feature Collection which can be saved as a project (the project for our visualization is 'project.gproj'). 2. Establish the output grid (ours is lat_lon_velocity_domain_91_181): Features -> Generate Velocity Domain Points -> Latitude Longitude -> Number of latitudinal grid intervals=91, Number of longitudinal grid intervals=181, number of nodes=16652. 3. To output the point velocities we used for the models: Reconstruction -> Export -> Add Export -> Velocities, GPML(*.gpml), velocity_%nMa - The data files we created following this workflow are part of this data publication and can be found in the `lat_lon_velocity` folder.   - The global spherical convection models of the publication were created using two different ASPECT configurations:   Models `thermal`, `thermochemical`, and `p-T-dependent` were run using:   ``` ----------------------------------------------------------------------------- -- This is ASPECT, the Advanced Solver for Problems in Earth's ConvecTion. -- . version 2.4.0-pre (limit_shear_heating, 4f45a72fe) -- . using deal.II 9.4.0-pre (3d869ba6cd1fd462624e09dc232e34ed17880701) -- . with 64 bit indices and vectorization level 2 (256 bits) -- . using Trilinos 12.18.1 -- . using p4est 2.3.2 ----------------------------------------------------------------------------- ```   Models `strong basalt` and `weak ppv` were run using:   ``` ----------------------------------------------------------------------------- -- This is ASPECT, the Advanced Solver for Problems in Earth's ConvecTion. -- . version 2.5.0-pre (limit_shear_heating_and_ppv, a4812c95a) -- . using deal.II 9.4.2 -- . with 64 bit indices and vectorization level 3 (512 bits) -- . using Trilinos 13.2.0 -- . using p4est 2.3.2 ----------------------------------------------------------------------------- ```   - The two modified ASPECT versions are included in this data package. The repository including full version history is until further notice available as branch `limit_shear_heating` and branch `limit_shear_heating_and_ppv` in the repository `https://github.com/jdannberg/aspect.git`.   - Running these models also requires plugins that are located in the `shared_libs` folder in this repository and that need to be compiled. Navigate into this directory and follow the steps:   1. `cmake -D Aspect_DIR=PATH_TO_ASPECT` (replace `PATH_TO_ASPECT` with the directory where you compiled ASPECT). 2. `make` - Now the models in this repository can be started. You should start them from the `input_files` directory so that all paths are set correctly and you can start them with the ASPECT executable in your build folder.   - The files in `aspect_input_files` correspond to the models presented in the paper following the same naming scheme.   - To convert the ASPECT heat flux output to spherical harmonics we used the script `analyze_heatflux_mpi_gmt.py` in `SPH_scripts`, which requires modification to point to the correct ASPECT statistics file and the correct output directory.   - The final heat flux output is included in this data package in the `heat_flux` folder, which includes archives of the processed heat flux output in 1 Myr time intervals with 0 being the start of the model run and the highest timestep number representing the present day state.

opencc-by-4.0Oct 2023View details →

zenodo40/100

Data and code for "Carbon cycle instability for high-CO2 exoplanets: implications for habitability"

Data supporting "Carbon cycle instability for high-CO2 exoplanets: implications for habitability," Graham and Pierrehumbert, 2024, Astrophysical Journal (under review at time of upload) <ul> <li>Global climate model (GCM) simulation output necessary to reproduce figures in paper. Netcdf (.nc) format. <ul> <li>For simulations receiving instellation of 1250 W m-2, the format is atmos_monthly_[co2 mixing ratio]ppmv_S1250.nc</li> <li>For other simulations, the format is: atmos_monthly_[co2 partial pressure]bar_S[675, 750, 800, or 1000].nc</li> </ul> </li> <li>Continental configuration land mask used in GCM simulations. Netcdf (.nc) format. <ul> <li>land.nc</li> </ul> </li> <li>Python scripts to <ul> <li>post-process climate simulation data to calculate global weathering rates according to WHAK or MAC weathering  <ul> <li>isca_weathering_calc.py</li> </ul> </li> <li>reproduce all model-based figures  <ul> <li>figures 1-8: isca_plot_maker.py</li> <li>figure 9: isca_weathering_calc.py</li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0Apr 2024View details →

zenodo40/100

Data set for the study "Assessing the lifetime of anthropogenic CO2 and its sensitivity to different carbon cycle processes"

This repository contains the data necessary to reproduce the results of the paper:  "Assessing the lifetime of anthropogenic CO2 and its sensitivity to different carbon cycle processes"  <a href="https://doi.org/10.5194/bg-22-2767-2025" target="_blank" rel="noopener">https://doi.org/10.5194/bg-22-2767-2025</a> <h3>Data organization:</h3> The Zenodo upload is organized as the following inside of <code>results.zip</code>: <ul> <li>Data analysis and figure generation are given by "*.pynb" and "*.m" files </li> <li>Data files as NetCDF output are organized with the following structure inside of <code>data</code>: <ul> <li>Experiment: <code>REF</code>, <code>noLAND</code>, <code>noWEATH</code>, <code>ECS2</code>, <code>ECS4</code>, <code>intCH4</code>, <code>PATH1</code>, <code>PATH2</code>, and <code>PULSE</code> <ul> <li>Emissions scenario: <code>0_gtc</code>, <code>500_gtc</code>, <code>1000_gtc</code>, <code>2000_gtc</code>, <code>3000_gtc</code>, <code>4000_gtc</code>, and <code>5000_gtc</code> <ul> <li>Component: atmosphere (<code>atm</code>), land (<code>lnd</code>), ocean (<code>ocn</code>), biogeochemistry (<code>bgc</code>), and the carbon cycle (<code>co2</code>) <ul> <li>Note: for <code>intCH4</code>, there is another file concerning methane (<code>ch4</code>)</li> <li>Note: surface ocean pH and surface ocean DIC were not part of the standard output in the original CLIMBER-X model. Instead, these variables were calculated during post-processing using 2D spatial data. Since the 2D data was only output every 1 kyr, the first millennium of data was missing. To address this, we re-ran the experiments with surface ocean pH and DIC included in the output for the first 1 kyr. This is why there are additional individual files for pH, DIC, and the Revelle factor (see "fig5_7_8_9.ipynb" for further details). </li> </ul> </li> <li>File type: for each component, files are divided into timeseries (<code>*_ts.nc</code>) or 2D data with a 1 kyr output frequency (<code>*.nc</code>) <ul> <li>Note: due to size constraints of the Zenodo repository, only some 2D spatial data presented in the publication (for the <code>REF</code> experiment) is available. However, this is not an exhaustive dataset. For inquiries regarding additional data, please contact the corresponding author to explore potential availability.</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0Oct 2024View details →

zenodo40/100

Data and code for "Relationships between aboveground plant traits and carbon cycling in tundra plant communities"

Paper See the preprint for more detailed description of the performend analyses <a href="https://doi.org/10.1101/865899">here.</a> Description of subdirectories The structure of this repository loosely follows that recommended by <a href="https://doi.org/10.1371/journal.pcbi.1005510">Wilson et al. 2017</a>. docs Contains data documentation and metadata. data Holds raw, unedited data src Contains analysis scripts. The src/new_analyses.R script generates all the figures for this project. The other scripts prepare the data for analysis, and must be run before src/new_analyses.R results Contains all analysis results, cleaned, analysis-ready data, figures, etc. Some of the figures (such as measurement schematics) have been generated by hand, and are thus not linked to any scripts.

opencc-by-4.0Apr 2021View details →

zenodo40/100

Data for Universal prediction of cell cycle position using transfer learning

The repo contains the data for Universal prediction of cell cycle position using transfer learning (https://www.biorxiv.org/content/10.1101/2021.04.06.438463v2). The scripts to analyze and generate all figures could be found at https://github.com/hansenlab/tricycle_paper_figs v1.1 update: add neurosphere_scvelo.qs - an R SingleCellExperiment object saved as qs file that has spliced counts, unspliced counts, and all outputs from scvelo.

opencc-by-4.0Sep 2021View details →

zenodo40/100

Life cycle-based environmental impacts of energy scenarios - additional data

This data set documents additional results of the paper "Life cycle-based environmental impacts of energy system transformation strategies for Germany: Are climate and environmental protection conflicting goals?" (Tobias Naegler and co-authors, published in Energy Reports (2020), https://doi.org/10.1016/j.egyr.2022.03.143). It shows life cycle-based environmental impacts for 10 different transformation strategies for the German energy and transport system.

opencc-by-4.0Feb 2022View details →

zenodo40/100

LiBforSecUse Data Release - Impedance spectra of life cycle tests of commercial 18650 cells

The EMPIR project LiBforSecUse aimed to develop empirical measurement models to estimate the residual capacity of second-use Li-ion battery cells with impedance-based measurement and evaluation methods. The models have been established based on a series of life cycle tests of commercial 18650 (graphite/NMC) cells including regular impedance spectroscopy and capacity measurements. The measured data are made publicly available here. They can be downloaded to verify the models established within the project and they may be used for further investigations. However, the user is asked to pay tribute to the project and the researchers providing the data by citing this data source. A pdf file is added to give more detailed information on the data.

opencc-by-4.0Mar 2022View details →

zenodo40/100

Supplementary Data & Software for "Balancing the marine sulfur cycle", Johnson & Adkins (submitted)

These files are the supplementary data and software for the manuscript "Balancing the marine sulfur cycle" by Johnson & Adkins (submitted). The MATLAB workspace file "DeepSeaSO4_ClusterAnalysisData.mat" contains organized data structures with DSDP, ODP, and IODP site metadata, porosity data, pore water sulfate concentration data, methane concentration data, total organic carbon wt% data, and calcium carbonate wt% data + their corresponding depths. These data served as the basis for the cluster analysis performed by the included MATLAB script "SO4_conc_cluster_analysis.m" in the study. The "DatasetS1.xlsx" spreadsheet file contains all compiled pyrite sulfur isotopic data, pyrite abundance data, and total sulfur abundance data included within the literature compilation discussed in the manuscript. Pyrite sulfur isotopic data references are listed and are cited in the main text. DOI URLs for the total sulfur abundance data from the PANGAEA repository are also included in the spreadsheet. These data have additionally been saved into the MATLAB workspace files "Pyrite_d34S_Comp.mat" and "Pyrite Abundance_Comp.mat" for import into MATLAB by other scripts. The MATLAB workspace file "POROSITY.mat" includes vectors of latitude (in degrees), longitude (in degrees), water depth (in mbsl), sediment depth (in mbsf), porosity (in vol%), and corresponding PANGAEA DOI URLs along with the data tables from which this information was extracted. This file is called by "PorosityPlotter.m" to extract initial porosity values, compile coring methods, and make plots of initial porosity as a function of water depth for each coring method. The MATLAB script "hypsometry.m" loads the global topographic data included in "topography.mat", calculates associated surface areas across different ocean depth intervals, and determines average porosity and sedimentary pyrite parameters across those intervals in tandem with "S_Iso_Mass.m". "S_Iso_Mass.m" uses the averages to calculate the steady-state sulfur isotopic composition of seawater. The script "phi_waterdepth_analysis.m" loads the cluster analysis data and uses the corresponding porosity data to calculate linear regressions between initial porosity and water depth for use by other scripts; the workspace files "phi_waterdepth_fit.mat" and "phi_waterdepth_trimmed_fit.mat" are saved versions of the regressions used in the manuscript and called by "hypsometry.m" for calculating averages.  The additional included files "PyriteSAbundanceCompilationSources.pdf" and "PorosityCompilationSources.pdf" files include full citations to the data included in the pyrite S abundance and porosity compilations (respectively).

opencc-by-4.0Apr 2022View details →

dryad40/100

Data from: Rapid-cycling Brassica rapa evolves even earlier flowering under experimental drought

Premise Changes in climate can impose selection on populations and may lead to rapid evolution. One such climatic stress is drought, which plant populations may respond to with escape (rapid growth and early flowering) or avoidance (slow growth and efficient water use). However, it is unclear if drought escape would be a viable strategy for populations that already flower early from prior selection. Methods In an experimental evolution study, we subjected rapid-cycling Brassica rapa (RCBr), which was previously selected for early flowering, to four generations of experimental drought or watered conditions. We then grew ancestral and descendant populations concurrently under drought and watered conditions to assess evolution, plasticity, and adaptation. Results RCBr evolved under drought had earlier flowering and lower water-use efficiency than RCBr evolved under watered conditions, indicating evolutionary divergence. The drought descendants also had a trend of earlier flowering compared to ancestors, indicating evolution. Evolution of earlier flowering under drought followed the direction of selection and increased fitness, and was consistent with studies in natural and experimental populations of this species, suggesting adaptive evolution. Conclusions We found evidence for rapid adaptive evolution of drought escape in RCBr and little evidence for constraints on flowering, even though RCBr already flowers extremely early. Our results suggest that some populations may harbor sufficient genetic variation for evolution even after strong selection has occurred. Our study also illustrates the utility of combining artificial selection, experimental evolution, and the resurrection approach to study the evolution of functional traits.

opencc-zeroApr 2022View details →

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