Find research datasets worth reusing

opencc-by-4.0Oct 2022View details →

Reference data set for a Norwegian medium voltage power distribution system

This reference data set describes a representative Norwegian radial, medium voltage (MV) electric power distribution system operated at 22 kV. The data set is developed in the Norwegian research centre CINELDI and will in brief be referred to as the CINELDI MV reference system. Data for a real Norwegian distribution system were provided by a distribution grid company. The data have been anonymized and processed to obtain a simplified but still realistic grid model with 124 nodes. The data set consists of the following three parts: 1. Grid data files: describe the base version of the reference system that represents the present-day state of the grid, including information about topology, electrical parameters, and existing load points. 2. Load data files: comprise load demand time series for a year with hourly resolution and scenarios for the possible long-term development of peak load. These data describe an extended version of the reference system with information about possible new load points being added to the system in the future. 3. Reliability data files: contain data necessary for carrying out reliability of supply analyses for the system. The data set is described in detail in the following data article: I. B. Sperstad, O. B. Fosso, S. H. Jakobsen, A. O. Eggen, J. H. Evenstuen, and G. Kjølle, “Reference data set for a Norwegian medium voltage power distribution system,” Data in Brief, 109025, 2023, doi: 10.1016/j.dib.2023.109025.

Data-driven plasma modelling: Fluorocarbon ICP data set

This is an open source dataset of optical emission spectra and optical images in fluorocarbon plasmas, along with associated tool logs, captured from a Oxford Intruments Plasma Technology PP100 ICP etcher. The dataset consists of Ar, O2, Ar/O2, CF4/O2 and SF6/O2 gas mixtures etching Si wafers. The data has been split into chunks for uploading to Zenodo, to reconstruct them: $ cat generative_model-fluorocarbon_data_set.tar.xz* > generative_model-fluorocarbon_data_set.tar.xz   $ tar -xvf generative_model-fluorocarbon_data_set.tar.xz   You can find the code to train an autoencoder model using the optical emission spectra and optical images at our github repo, https://github.com/gregdaly/generative_modelling_for_optical_plasma_diagnostics

opencc-by-4.0Jul 2022View details →

Data Set on Local Government Indicators in Chile

Data Set on Local Government Indicators in Chile This repository contains a dataset in progress (20%) on local government indicators in Chile between 2010 and 2021, featuring an e-government indicator (EGI) in 2016, 2019 and 2021 in Comma-Separated Values CSV format with Unicode encoding UTF-8. GitHub repository: <a href="https://github.com/bgonzalezbustamante/local-gov-indicators">https://github.com/bgonzalezbustamante/local-gov-indicators</a>

Static and Dynamic DFT Data Sets for Polymorphs of l-Cysteine - Stability and Terahertz Spectra

Data sets associated with static and dynamic density functional calculations are reported for the four known polymorphs of l-cysteine and for models associated with the known disorder in Form I..  Static calculations are used to explore the relative free energies (within the harmonic approximation) of the polymorphs as a function of pressure.  The energetics for dihedral angle rotation are explored and the barriers for rotation between the hydrogen bonding motifs have been calculated for each polymorph. Molecular dynamics calculations are reported for each polymorph and for models of hydrogen bond disorder which are known to exist at higher temperatures. Finally static and dynamic calculations of the infrared and terahertz spectra are performed.

opencc-by-4.0Feb 2021View details →

CoVaxxy Tweet IDs data set

A collection of Tweet IDs related to Covid-19 Vaccines, gathered from Twitter since Jan 4, 2021. Please see <a href="https://arxiv.org/abs/2101.07694">https://arxiv.org/abs/2101.07694</a> for more information.

opencc-by-4.0Mar 2022View details →

Data set for the replication package of the paper "Constriction of actin rings by passive crosslinkers"

Data set for the replication package of the paper "Constriction of actin rings by passive crosslinkers".

Data set for "Light-driven peristaltic pumping by an actuating splay-bend strip"

Data set for all the experiments included in the main part of the text and in the supplementary information, together with the Mathematica notebook including all the theoretical and numerical calculations.

ViF-GTAD: A new Automotive Data Set with Ground Truth for ADAS/AD Development, Testing and Validation

A new dataset for automated driving, which is the subject matter of this paper, identifies and addresses a gap in existing similar perception data sets. While the most state-of-the-art perception data sets primarily focus on provision of various on-board sensor measurements along with the semantic information under various driving conditions, the provided information is often insufficient since the object list and position data provided include unknown and time-varying errors. The current paper and the associated data-set describes the first publicly available perception measurement data that include not only the on-board sensor information from camera, Lidar and radar with semantically classified objects, but also the high precision ground-truth position measurements enabled by the accurate RTK assisted GPS localization systems available on both the ego vehicle and the dynamic target objects. This paper provides insight on the capturing of the data, explicitly explaining the meta data structure and the content, as well as the potential application examples where it has been, and can potentially be, applied and implemented in relation to automated driving and environmental perception systems development, testing and validation.

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

Validation data set on land cover changes for RapidAI4EO project

This is a reference data set collected for validation of the monthly land cover maps at a 3m and at a 10m resolution produced in the WP5. The reference data set has been collected by using Geo-Wiki toolbox for visual interpretation of very high-resolution images, including Planet data and Google maps. The data set has been collected over 3 AOIs. Each reference sample site corresponds to a 30m-by-30m box and includes information about monthly land cover type over the period 2018-2020. Land cover legend is the same as in ESA WorldCover map at a 10m resolution (https://worldcover2021.esa.int/). Fields: "rowid" – unique row identifier; "sampleid" – unique sample site identifier in the Geo-Wiki database; "samplegroupid" – group id with values 257(Portugal), 258 (Belgium), 259(Sicily); "x_min","x_max","y_min","y_max" – bounding box coordinates of each sample site (30m x 30m), in WGS84 "X2018_1","X2018_2",…, "X2020_12" – dominant land cover class in each sample site in each month from January 2018 to December 2020; Land cover codes: 10 – Tree cover 20 - Shrubland 30 - Grassland 40 - Cropland 50 – Urban/built-up 60 - Bare/Sparse vegetation 80 - Water 90 - Wetland 110 - Burnt 120 – Not sure

Global Crop Type Validation Data Set for ESA WorldCereal System

This dataset was created by using a new IIASA tool, called “Street Imagery validation” (<a href="https://svweb.cloud.geo-wiki.org/">https://svweb.cloud.geo-wiki.org/</a>) where users could check street level images (e.g., Google Street Level images, Mapillary etc.) and identify the crop type where it is possible. The advantage of this tool is that there are plenty of georeferenced images with dates, going back in time. The disadvantage is that users need to check plenty of images where only few will clearly show cropland fields that are mature enough to be identified. To make the data collection more efficient, we provided our experts with preliminary maps of points in agricultural areas where street level images are available for the year 2021. Then, the experts checked those locations in an opportunistic way. The dataset is completely independent from all the existing maps and the reference datasets. There are 3 main data records uploaded: <ol> <li>sv_croptype_poly.zip – an archive with a shapefile containing all the collected polygons with crop type information. Not all the polygons correspond to actual field boundaries.</li> <li>sv_croptype_validations.csv – a table with crop type observations with centroid coordinates in WGS84</li> <li>sv_worldcereal_validation.csv – a table with a subset of crop type observations used in validation of WorldCereal crop type maps for 2021.</li> </ol> Fields: <ul> <li>"id" – unique observation identifier;</li> <li>"imgSource" – source of imagery used for visual inspection;</li> <li>"imgLoc" – image location;</li> <li>"svImgDate" – image date;</li> <li>"imageIdKey" – image unique identifier;</li> <li>"submitedAt" – date of submission of crop type observation;</li> <li>"cropType"  - crop type observation;</li> <li>"irrType" – irrigation type;</li> <li>"x", "y" – centroids of submitted polygons in WGS84.</li> </ul>

Example data set for NG-QTAIM eigenvector-following trajectories: rotary f-NAIBP motor

Example dataset for NG-QTAIM eigenvector following trajectories: the F-NAIBP molecular rotary motor.

Data set: Monthly averaged RACMO2.3p2 variables (1979-2022); Antarctica

This is a data set of monthly averaged variables from January 1979 to December 2022 simulated by the hydrostatic regional atmospheric climate model RACMO2.3p2 over Antarctica. At the lateral and ocean boundaries the model is forced by ERA5 reanalysis data every 3 hours from 1979-2022. The model is run at a horizontal resolution of 27 km and 40 vertical levels for the entire Antarctic ice sheet, which constitutes an update of the simulation forced from 1979-2018 by ERA-Interim reported in van Wessem et al., 2018. Upper air relaxation of wind, humidity and temperature is also active (Van de Berg et al., 2016). This version of the model is specifically applied to the polar regions by interactive coupling to a multilayer snow model that calculates melt, refreezing, percolation and runoff of meltwater (Ettema et al., 2010). In addition, snow albedo is calculated through a prognostic scheme for snow grain size (Kuipers Munneke et al., 2011) while a drifting snow scheme simulates the interaction of the near-surface air with drifting snow (Lenaerts et al., 2010).  This dataset is provided on a rotated polar coordinate grid. In such a rotated pole projection the grid is defined over the equator and then rotated to the area of interest. One of the advantages is that the grid distance can be defined in fraction of degrees, which results in near equidistant grid cells as long as the domain is small enough, and provides the most accurate model calculations. However, re-projecting these data on other grids is often troublesome, as after rotation the grid is non-equidistant and most software packages cannot directly handle this. Stef Lhermitte provided a nice solution for reprojecting the RACMO data on his gitlab-page: https://gitlab.tudelft.nl/slhermitte/manuals/blob/master/RACMO_reproject.md. The dataset includes the following surface- and atmospheric variables. Additional variables and higher temporal resolutuon up to 3 hourly are available on request: Surface mass balance (SMB) variables (in kg m-2 mo-1 or mm water equivalent mo-1) smb : (Specific) Surface mass balance defined as SMB = Total precipitation + sublimation - runoff snowmelt : Surface snowmelt production refreeze : Refreezing of meltwater snowfall : Solid precipitation precip : Total precipitation (snowfall + rainfall); to calculate rainfall use rainfall = precip - snowfall runoff : Surface meltwater runoff subl : Snow sublimation (including sublimation of drifting snow). Negative values are sublimation, positive values are snow deposition. erds : erosion of drifting snow Atmospheric variables t2m : 2-m Temperature q2m : 2-m Specific humidity rh2m : 2-m Relative humidity (RH) tskin : Surface/skin temperature. Calculated from closing the surface energy budget. psurf : Surface pressure u10m : Zonal wind speed at 10 m v10m : Meridional wind speed at 10 m ff10m : Wind speed at 10 m u0500 : Zonal wind speed at 500 hPa v0500 : Meridional wind speed at 500 hPa z0500 : Geopotential height at 500 hPa Surface Energy Budget (SEB) variables (in J m-2); SEB = LWnet+SWnet+SHF+LHF+GHF Values are monthly cumulative: to convert to W m-2 divide by amount of seconds in a month: 'nrdaysmonth'*24*3600. lwsn : Net longwave radiation (LWnet=LWdown-LWup) swsn : Net shortwave radiation (SWnet=SWdown-SWup) lwsd : Downwelling longwave radiation at the surface swsd : Downwelling shortwave radiation at the surface swsu : Upwelling shortwave radiation at the surface senf : Upward Sensible Heat Flux (SHF) at the surface latf : Upward Latent Heat Flux (LHF) at the surface (our simulated LHF doesn't explicitly close the SEB, as it also includes in-air sublimation, but the effect should be rougly neglible) gbot : Soil/Ground Heat Flux (GHF) Snow variables totpore : Vertically integrated pore space (m) totwat : Total liquid water content of the snowpack (kg m-2) zsnow : Total snowpack thickness (m) Grid, elevation, coordinates and masks in Height_latlon_ANT27.nc (240 by 262 grid boxes) mask2d : Full ice mask fraction (grounded ice + floating ice shelves) [0..1] maskgrounded2d : Grounded ice sheet mask fraction [0..1] height : Surface elevation (m) slope : Surface slope (m m-1) aspect : Direction of surface slope (degrees) lat : Latitude (polar) lon : Longitude (polar) Ice shelf and ice sheet drainage basins mask in TotIS_RACMO_ANT27_IMBIE2.nc This file contains masks on the RACMO grid for the drainage basins as defined in <a href="http://imbie.org/imbie-3/drainage-basins/">http://imbie.org/imbie-3/drainage-basins/</a> (Rignot et al., 2013, IMBIE2, IMBIE3), including masks seperately for the ice shelves they drain into, numbered counterclockwise from 0 to 18. mask2dF : Full ice mask including ice shelves IceShelves : Ice shelf masks GroundedIce : Grounded ice sheet drainage basins

Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 10 videos)

Classify video clips with natural scenes of actions performed by people visible in the videos. See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a> This example datasets consists of the 10 most numerous video from the UCF101 dataset. For the top 5 version, see: <a href="https://doi.org/10.5281/zenodo.7924745">https://doi.org/10.5281/zenodo.7924745</a> . Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>). Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a> For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example). I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date.  Cite this dataset as: Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a> To download the dataset via the command line, please use: <pre><code class="language-bash">wget -q https://zenodo.org/record/7882861/files/ucf101_top10.tar.gz -O ucf101_top10.tar.gz tar xf ucf101_top10.tar.gz</code></pre>

opencc-by-4.0May 2023View details →

TEAMx-PC22 (TEAMx pre-campaign 2022) - ACINN Doppler wind lidar data sets (SL88, SLXR142)

ABSTRACT The data sets found here were collected with <a href="http://acinn.uibk.ac.at/">ACINN</a>'s Doppler wind lidars SL88 and SLXR142 in Innsbruck, Austria, in summer 2022 in the framework of the TEAMx pre-campaign 2022 (TEAMx-PC22). The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in Serafin et al. (2020) and in Rotach et al. (2022). DATA SET DESCRIPTION 1. Spatial coverage and locations Measurements with the SL88 and SLXR142 lidar were collected during TEAMx-PC22 in Innsbruck, Austria, at the Campus Innrain of the University of Innsbruck. More specifically, the SLXR142 lidar was located on the rooftop of one of the university buildings (Bruno-Sander-Haus) at Innrain 52f. The SL88 lidar was located in the forecourt of the Campus Innrain, the so-called GEIWI-Forum, next to the Bruno-Sander-Haus. The exact lidar locations are: <ul> <li>SL88: 47.264083°N / 11.384986°E / 575 m MSL</li> <li>SLXR142: 47.26431°N / 11.38529°E / 613 m MSL</li> </ul> 2. Temporal coverage The TEAMx-PC22 lasted from mid-May 2022 to early October 2022. However, the SL88 data set contains a shorter period from 11 August to 02 October 2022 (1 Hz data, vertical stares). The SLXR142 data set covers an extended period from 01 May to 31 October 2022 (VAD products, 10-min averages) as this lidar was operated in a semi-permanent mode. 3. Instrument details General Measurements were taken with two scanning Doppler wind lidars, model Stream Line (SL88) and Stream Line XR (SLXR142), manufactured by HALO Photonics. The SL88 and SLXR142 are part of the Innsbruck Atmospheric Observatory (IAO; Karl et al. 2020). Available here are vertical profiles of radial velocity and backscatter data based on vertical stares at 1 Hz for the SL88 lidar and vertical profiles of horizontal winds (10-min averages) derived from plan position indicator (PPI) scans by applying the VAD method for the SLXR142 lidar. PPI scans were performed as continuous motion scans (CSM mode) at an azimuth angle of 70°. For continuous motion scans, the scanner moves continuously (changing its azimuth angle) while data is being acquired. Data correction No corrections were applied to the data (level0 data). 4. Data file structure File format Provided are data in netCDF format. File names contain date and time information in UTC. The following wildcard characters are used in the file examples below: yyyy - year; mm - month, dd - day; HH - hour, MM - minute, `SS` - second. NetCDF data files are zipped together into the following zip files. Zip files SL88.zip contains netCDF files of SL88 data structured into subdirectories (one subdirectory for each month, yyyymm, and one for each day, yyyymmdd). SLXR142.zip contains netCDF files of SLXR142 data structured into subdirectories (one subdirectory for each month, yyyymm). NetCDF files for uncorrected SL88 data Stare_88_yyyymmdd_HH_l0.nc contains vertical stare measurements aggregated together in one netCDF file for each hour (uncorrected level0 data). NetCDF files for SLXR142 data products yyyymmdd.nc contains vertical profiles of the horizontal wind vector derived from PPI scans by applying the VAD technique. Each vertical profile is based on several PPI scans conducted at an elevation angle of 70° within 10 minutes. Hence, each profile represents a 10-min average. Profiles are aggregated together for each day in a separate netCDF file. 6. Contact Contact alexander.gohm(at)uibk.ac.at for any questions regarding the data set. 7. References Karl, T., A. Gohm, M.W. Rotach, H.C. Ward, M. Graus, A. Cede, G. Wohlfahrt, A. Hammerle, M. Haid, M. Tiefengraber, C. Lamprecht, J. Vergeiner, A. Kreuter, J. Wagner, M. Staudinger, 2020: Studying urban climate and air quality in the Alps: The Innsbruck Atmospheric Observatory. Bulletin of the American Meteorological Society, 101, E488–E507, <a href="https://doi.org/10.1175/bams-d-19-0270.1">https://doi.org/10.1175/bams-d-19-0270.1</a> Serafin, S., M. W. Rotach, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. De Wekker, M. Evans, V. Grubi&scaron;ić, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Raudzens Bailey, J. Schmidli, G. Wohlfahrt, B. Zardi, 2020: Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment. Innsbruck University Press. <a href="https://doi.org/10.15203/99106-003-1">https://doi.org/10.15203/99106-003-1</a> Rotach, M. W., S. Serafin, H. C. Ward, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. D. Wekker, V. Grubi&scaron;ic, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Bailey, J.  Schmidli, C. Wittmann, G. Wohlfahrt, D. Zardi, 2022: A collaborative effort to better understand, measure, and model atmospheric exchange processes over mountains. Bulletin of the American Meteorological Society, 103, E1282–E1295. <a href="https://doi.org/10.1175/bams-d-21-0232.1">https://doi.org/10.1175/bams-d-21-0232.1</a>

opencc-by-4.0May 2023View details →

Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 5 videos)

Classify video clips with natural scenes of actions performed by people visible in the videos. See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a> This example datasets consists of the 5 most numerous video from the UCF101 dataset. For the top 10 version see: <a href="https://doi.org/10.5281/zenodo.7882861">https://doi.org/10.5281/zenodo.7882861</a> . Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>). Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a> For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example). I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date.  Cite this dataset as: Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a> To download the dataset via the command line, please use: <pre><code class="language-bash">wget -q https://zenodo.org/record/7924745/files/ucf101_top5.tar.gz -O ucf101_top5.tar.gz tar xf ucf101_top5.tar.gz</code></pre>

opencc-by-4.0May 2023View details →

Data set and classification method for low quality web traffic identification in video marketing campaigns

Final outcomes of the InPreVi (AI4Media) project developed in 2022.  1. Data set describing the statistics of the video ad marketing campaigns 2. Script for web traffic classification

opencc-by-4.0Jun 2023View details →

Lake Mendota metabolism model and data set

This is a zipped file that includes the model code, written in R, as well as the input and output data for the model. This publication accompanies the manuscript entitled, Legacy phosphorus and ecosystem memory control future water quality in a eutrophic lake

opencc-by-4.0Jun 2023View details →

DA-TCA test data sets (KIAPS-GIST)

About the data sets: The test datasets from SMOS/ASCAT will be utilized to validate the efficacy of SMAP data assimilation in numerical weather forecasting. This project is being conducted in collaboration with the Korea Institute of Atmospheric Prediction Systems (KIAPS) and the Gwangju Institute of Science and Technology (GIST) in South Korea. About the project: Soil moisture assimilation from satellites can significantly improve weather prediction accuracy by providing valuable information about the moisture content in the soil. When integrated into numerical weather forecasting models, satellite-derived soil moisture data enhances the representation of land surface processes and interactions between the atmosphere and the Earth's surface. Here's how soil moisture assimilation benefits weather prediction: <ol> <li> Initialization of Models: Accurate initial conditions are crucial for reliable weather forecasting. Satellite-derived soil moisture data serves as an important source of information for initializing models. By incorporating this data, forecast models can start with more realistic representations of the state of the land surface, enabling a better starting point for predictions. </li> <li> Land-Atmosphere Interaction: Soil moisture plays a significant role in land-atmosphere interactions. It influences the partitioning of energy, the transfer of moisture, and the formation of clouds and precipitation. Assimilating satellite-derived soil moisture data allows forecast models to better capture these interactions and improve the representation of feedback mechanisms between the land surface and the atmosphere. </li> <li> Precipitation Forecasting: Soil moisture assimilation also enhances precipitation forecasting. The availability of accurate soil moisture data helps in understanding soil moisture-atmosphere feedback processes, which impact the formation, intensity, and movement of precipitation systems. By incorporating this information into forecasting models, more accurate precipitation predictions can be made. </li> <li> Drought and Flood Monitoring: Satellite-derived soil moisture data aids in monitoring and predicting droughts and floods. Real-time updates of soil moisture conditions can be integrated into forecasting models, allowing for timely and accurate assessments of soil moisture deficits or surpluses. This information is crucial for managing water resources, agriculture, and mitigating the impacts of extreme weather events. </li> </ol> Related code in Github: https://github.com/Hyunglok-Kim/HydroAI/blob/main/Ex_in_TCA.ipynb