Find research datasets worth reusing

Fig. 7 in In silico characterization of the impact of mutation (LEU112PRO) on the structure and function of carotenoid cleavage dioxygenase 8 in Oryza sativa

Fig. 7. "Three-dimensional" representation of intermolecular interaction in (A) Complex I, (B) Complex II, (C) Complex III and (B) Complex IV after 100 ns simulation. Green, cyan and red color represent wildtype CCD8 protein, mutant CCD8 protein and auxin/ cytokinin, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3 in In silico characterization of the impact of mutation (LEU112PRO) on the structure and function of carotenoid cleavage dioxygenase 8 in Oryza sativa

Fig. 3. The "Z-score" plot of modelled CCD8 protein produced from the "ProSA-web" server. (A) Wild-type (Before simulation), (B) Wild-type (After 100 ns simulation) and (C) Mutant (After 100 ns simulation).

Fig. 4. The stability parameters for modelled D10 in In silico characterization of the impact of mutation (LEU112PRO) on the structure and function of carotenoid cleavage dioxygenase 8 in Oryza sativa

Fig. 4. The stability parameters for modelled D10 protein: wild-type (Blue), mutant (Green), Complex I (wild-type & auxin complex) (orange), Complex II (wild-type & cytokinin complex) (purple), Complex III (mutant-auxin complex) (red) and (D) Complex IV (mutant-cytokinin complex) (black) throughout 100 ns simulation: (A) RMSD, (B) RMSF, (C) PCA of C-α movement, and (D) Radius of gyration. The trajectory projected to the two-dimensional space. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2 in In silico characterization of the impact of mutation (LEU112PRO) on the structure and function of carotenoid cleavage dioxygenase 8 in Oryza sativa

Fig. 2. "Ramachandran plot" of three-dimensional CCD8 protein produced through "PROCHECK". (A) Wild-type (Before simulation), (B) Wild-type (After 100 ns simulation) and (C) Mutant (After 100 ns simulation).

Fig. 1 in In silico characterization of the impact of mutation (LEU112PRO) on the structure and function of carotenoid cleavage dioxygenase 8 in Oryza sativa

Fig. 1. "Three-dimensional" structure of CCD8 protein. (A) "Super-impose" structure of Wild-type CCD8 protein (green) and chain A of 4RSE (Red) (A) Wild-type is made up of 10 α-helix and 25 β-strands. (B) Mutant is made up of 11 α-helix and 24 β- strands. Light grey: loop; red: α-helix and cyan: β- strands. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

opencc-zeroJun 2023View details →

Data of smoothed particle hydrodynamic giant impact simulations for the paper "A super-massive Neptune-sized planet"

This dataset contains data from the article: L. Naponiello et al.  2023 "A super-massive Neptune-sized planet". DOI: https://doi.org/10.1038/s41586-023-06499-2 The dataset comprises initial conditions and final snapshots of the smoothed particle hydrodynamics simulations selected in the paper. All the simulations were performed using <a href="https://swift.com">SWIFT</a> with initial condtions generated using <a href="https://github.com/srbonilla/WoMa">WoMa</a>. The planets are modeled with forsterite core (see  <a href="https://github.com/ststewart/aneos-forsterite-2019">Link</a>),  water middle layer (see <a href="https://ui.adsabs.harvard.edu/abs/2020A%26A...643A.105H/abstract">link</a>), and H&He mixture atmosphere top layer (see <a href="https://ui.adsabs.harvard.edu/abs/1980JGR....85..225H/abstract">link</a>). We simulated three different pre-impact compositions: water-rich by mass (22.5 % rock, 67.5 % water, 10 % H/He), equal rock and water (45 % rock, 45 % water, 10 % H/He), and rock-rich (67.5 % rock, 22.5 % water, 10 % H/He). These are located in the "data.zip" file under the folders "water rich", "equal_water_rock", and "rock_rich", respectively. In each simulation subfolder, "snapshot_0000.hdf5" is the initial condition file, where the target and impactor are separated by a distance that allows contact to occur 1 hour after the start of the simulation. "snapshot_00xx.hdf5" is the final snapshot of that simulation, where "xx" is the simulation time in hours. For example, "snapshot_0015.hdf5" is the snapshot taken after 15 hours of the simulation.

Evolution of impact melt pools on Titan - data files

Data files to produce figures in the manuscript entitled 'Evolution of impact melt pools on Titan' written by Klara Kalousova, Shigeru Wakita, Christophe Sotin, Catherine D. Neish, Jason M. Soderblom, Ondrej Soucek, and Brandon C. Johnson and submitted to Journal of Geophysical Research: Planets.

Supporting Data for " Impacts of the North Atlantic Biases on the Upper Troposphere/Lower Stratosphere over the Extratropical North Pacific"

This repository contains the supporting data (in the NetCDF format) for the following paper: Joshi, R. and Zhang, R., 2023, Impacts of the North Atlantic Biases on the Upper Troposphere/Lower Stratosphere over the Extratropical North Pacific, npj Clim Atmos Sci. <a href="https://doi.org/10.1038/s41612-023-00482-4">https://doi.org/10.1038/s41612-023-00482-4</a>  In this study, Robust Diagnostic Calculations (RDC) are conducted in a fully coupled high-resolution climate model to correct the North Atlantic ocean circulation and associated temperature biases present in the model. The same fully coupled high-resolution climate model is used to generate a present-day control simulation without correcting North Atlantic ocean circulation biases for comparison. The long-term mean difference between the control and RDC experiments (Control minus RDC) is used to assess the influence of the North Atlantic biases on winter upper troposphere/lower stratosphere vertical motion and temperature changes over the extratropical North Pacific. Descriptions of data files: 1. Sea Surface Temperature (SST) and ocean current at 120 m depth from control and RDC experiment as shown in Fig. 1a,d. RDC_SST_current_120m.nc Control_SST_current_120m.nc 2. Ocean temperature and current at 2500 m depth from RDC and control experiment as shown in Fig. 1b,e. RDC_temp_current_2500m.nc Control_temp_current_2500m.nc 3. Meridional ocean velocity in the North Atlantic at section 45oN from RDC and control experiment as shown in Fig. 1c,f. RDC_v45N.nc Control_v45N.nc 4. Difference of wintertime (JFM) mean near-surface (2-m) air temperature, meridional eddy heat flux at 925hPa, 95th percentile large-scale extreme precipitation, and total precipitation between the control and RDC experiments, as shown in Fig. 2. t2m_diff.nc VT_diff.nc precip95_diff.nc precip_diff.nc 5.  Difference of wintertime (JFM) mean geopotential height, temperature, and winds between the control and RDC experiments at different pressure levels, as shown in Fig. 3,4. hght_diff_spatial.nc temp_diff_spatial.nc ucomp_diff_spatial.nc  vcomp_diff_spatial.nc  6.  Two-sided student t-test at a 5% significance level for the statistical testing of wintertime mean geopotential height difference between the control and RDC experiments shown in Fig. 3. The file is a mask with values ‘1’ and ‘0’ where ‘0’ represents the region not significant at 5% significance level. hght_ttest_spatial.nc 7.  Two-sided student t-test at a 5% significance level for the statistical testing of wintertime mean air temperature difference between the control and RDC experiments shown in Fig. 4. The file is a mask with values ‘1’ and ‘0’ where ‘0’ represents the region not significant at 5% significance level. temp_ttest_spatial.nc 8.  Zonally averaged difference of winter (JFM) zonal wind, geopotential height, and air temperature between the control and RDC experiments over the extratropical North Pacific, as shown in Fig. 5. ucomp_diff_lonavg.nc temp_diff_lonavg.nc hght_diff_lonavg.nc 9.  Two-sided student t-test at a 5% significance level for the statistical testing of wintertime zonally averaged mean air temperature difference between the control and RDC experiments over extratropical North Pacific shown in Fig. 5a. The file is a mask with values ‘1’ and ‘0’ where ‘0’ represents the region not significant at 5% significance level. temp_ttest_lonavg.nc 10.  Two-sided student t-test at a 5% significance level for the statistical testing of wintertime zonally averaged mean zonal wind difference between the control and RDC experiments over extratropical North Pacific shown in Fig. 5b. The file is a mask with values ‘1’ and ‘0’ where ‘0’ represents the region not significant at 5% significance level. ucomp_ttest_lonavg.nc 11.  Difference of winter (JFM) mean vertical velocity in pressure coordinates between the control and RDC experiments, as shown in Fig. 6. omega_diff_spatial.nc 12.  Two-sided student t-test at a 5% significance level for the statistical testing of wintertime mean vertical velocity difference between the control and RDC experiments shown in Fig. 6. The file is a mask with values ‘1’ and ‘0’ where ‘0’ represents the region not significant at 5% significance level. omega_ttest_spatial.nc 13.  Contribution of the mean zonal flow advection of the temperature change to the horizontal advection as shown in Fig. 7a horizontal_advection_contribution.nc 14.  Contribution of the vertical advection of mean potential temperature by the change in omega to the adiabatic heating as shown in Fig. 7b adiabatic_heating_contribution.nc 15.  Two-sided student t-test at a 5% significance level for the statistical testing of differences in Fig. 7a and Fig. 7b. The file is a mask with values ‘1’ and ‘0’ where ‘0’ represents the region not significant at a 5% significance level. horizontal_advection_contribution_ttest.nc adiabatic_heating_contribution_ttest.nc 16.  Meridionally averaged difference of winter (JFM) geopotential height and air temperature between the control and RDC experiments over the extratropical North Pacific, as shown in Fig. 9b. hght_diff_latavg.nc temp_diff_latavg.nc 17.  Two-sided student t-test at 5% significance level for the statistical testing of wintertime meridionally averaged mean air temperature difference between the control and RDC experiments over extratropical North Pacific in Fig. 9b. The file is a mask with values ‘1’ and ‘0’ where ‘0’ represents the region not significant at 5% significance level. temp_ttest_latavg.nc Acknowledgments We acknowledge using the following data sets and model codes in this study. The World Ocean Atlas 2013 (WOA13) data were downloaded from the NOAA National Centers for Environmental Information (formerly the National Oceanographic Data) https://www.nodc.noaa.gov/cgi-bin/OC5/woa13/woa13.pl. The CSIRO ATLAS of REGIONAL SEAS 2009 version (CARS2009) data (<a href="http://www.marine.csiro.au/~dunn/cars2009/">http://www.marine.csiro.au/~dunn/cars2009/</a>) were developed and provided by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Marine and Atmospheric Research and downloaded from <a href="http://www.marine.csiro.au/atlas/">http://www.marine.csiro.au/atlas/</a>. The Japanese Ocean Flux Data set with Use of Remote Sensing Observations (J-OFURO3 V168) is for the period 1988-2013 and can be downloaded from <a href="https://www.j-ofuro.com/en/dataset">https://www.j-ofuro.com/en/dataset</a>. The code of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model version 2.5 (CM2.5) is publicly available at <a href="https://www.gfdl.noaa.gov/cm2-5-and-flor-quickstart/">https://www.gfdl.noaa.gov/cm2-5-and-flor-quickstart/</a>. The relevant citations for the above datasets and model code are listed in Joshi and Zhang 2023.

Investigating the Impact of Internet Use in Patients with Cardiac Arrhythmias: A Cohort Study - Full Dataset SPSSv27

The full dataset used for calculations. Statistical analysis was performed on IBM SPSS V27.

opencc-zeroSep 2023View details →

dryad32/100

Data for: Assessing the biodiversity and the impact of pollinators on carob production

As the current climate crisis intensifies, drought-resistant crops are becoming more important due to their ability to withstand the increasingly hotter and drier summers. Such crops are valuable for pollinators as they provide food resources for wild and managed species. The carob tree (Ceratonia siliqua L.) represents an example of a heat- and drought-resistant crop, able to grow in dry areas with practically no inputs. The current study assessed over two growing seasons the diversity of wild bees and other pollinators relying on carob flowers, as well as the contribution of animal pollination to carob production. Carob flowers were subjected to two treatments: Open pollination, where inflorescences were left untreated, and wind pollination, where inflorescences were bagged in a mesh during blooming. Weekly observations during blooming showed that Apis mellifera was the most frequent floral visitor followed by wild bees and wasps. Carob flowers were visited by at least 10 different wild bee species. Open-pollinated flowers produced significantly more pods, with the benefit ranging from 4 to 16 times higher production, depending on the region. Open pollination led to pods with greater weight, length and number of seeds compared to pods derived from wind pollination. The results of the current study highlight the importance of animal pollination to carob production, as well as the significance of carob trees to wild bee conservation.

opencc-by-4.0Jul 2023View details →

Impact of noise on inverse design: The case of NMR spectra matching

Code and data for the corresponding preprint: Impact of noise on inverse design: The case of NMR spectra matching Dominik Lemm, Guido Falk von Rudorff, O. Anatole von Lilienfeld https://doi.org/10.48550/arXiv.2307.03969 SI_DFT_geo.xyz and SI_DFT_NMR.txt belong to the QM9NMR paper Revving up 13C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules Amit Gupta, Sabyasachi Chakraborty and Raghunathan Ramakrishnan Mach. Learn.: Sci. Technol. 2 (2021) 035010 originally hosted here: https://moldis-group.github.io/qm9nmr/ https://doi.org/10.17172/NOMAD/2021.10.16-1

opencc-by-4.0Aug 2023View details →

IA2030 Action Plans and Impact Acceleration Reports: Survey responses from 931 national and sub-national staff (Immunization Agenda 2030 Full Learning Cycle 2022)

# IA2030 Action Plans and Impact Acceleration Reports: Survey responses from 931 national and sub-national staff (Immunization Agenda 2030 Full Learning Cycle 2022) Note: This document includes markdown syntax to facilitate machine reading. ## Table of Contents - Abstract - Research Audience - Credits - Recommended Citation - File List - Related Data Sets - Background - Data Set Structure and Contents - Data Cleaning, Data Privacy, and Anonymization - Ethical Considerations - Survey Implementation - Survey Questionnaire Items - Data Availability and Accessibility - Copyright and License - References ## Abstract The Immunization Agenda 2030 (IA2030) has been endorsed at the World Health Assembly as the world’s strategy for immunization. The Movement for IA2030 is a voluntary collective of immunization practitioners, principally from low- and middle-income countries, who have pledged to support each other to accelerate local action in support of this global immunization strategy. This data set offers insights into how to understand national and sub-national challenges facing immunization programmes and what strategies show promise for addressing these challenges over time. It contains: - raw, anonymized data in Excel CSV format. Copies of the three survey questionnaires are available for download in the Files section (below). - responses to one, two, or three surveys from 931 national and subnational immunization practitioners participating in the Movement for Immunization Agenda (IA) 2030: Survey 1: IA2030 Action Plan Survey: a 77-item questionnaire, fielded in May 2022, which asked respondents to identify a key challenge relevant to their immunization programme, root cause of the challenge, three corrective actions, and what they knew about vaccination coverage in their zone of intervention. Survey 2: IA2030 Impact Acceleration Report Survey (2022): a 27-item questionnaire, fielded in June 2022, which asked respondents to report progress on implementation of their IA2030 Action Plan, including progress on vaccination coverage indicators. Survey 3: IA2030 Impact Acceleration Report Survey (2023): a 60-item questionnaire, fielded in June 2023, which asked respondents to report progress on implementation of their IA2030 Action Plan, including progress on vaccination coverage indicators. ## Research Audience Education and global health researchers with interest in human resources for health (HRH) and the characteristics, priority challenges, and experiences of national and sub-national immunization staff participating in the Movement for Immunization Agenda (IA) 2030. ## Credits ### Author The Geneva Learning Foundation (TGLF) 18 Avenue Louis Casaï  CH1209 Geneva, Switzerland research@learning.foundation Principal Investigator and Corresponding Author Reda Sadki, TGLF reda@learning.foundation Project Partners - Biostat Global Consulting (BGC) - Bridges to Development - Centre for Change & Complexity in Learning (C3L) Partner Roles and Responsibilities - Design: TGLF - Implementation (sample collection): TGLF - Processing: TGLF, C3L - Anonymization: BGC - Submission: TGLF - Maintenance of learning analytics database where data are stored: C3L Funding Wellcome Trust, Bill & Melinda Gates Foundation (BMGF) ## Recommended Citation Action Plans and Impact Acceleration Reports: Responses from 931 national and sub-national staff (Immunization Agenda 2030 Full Learning Cycle). The Geneva Learning Foundation, 2023. (Version 1.0) [Data Set]. The Geneval Learning Foundation. https://doi.org/10.5281/zenodo.8298773 ## File List 2023-09.IA2030_Action_Plans_and_Impact_Acceleration_Reports.README.md (this document) 2022-05.IA2030_Action_Plan_Survey.docx: List of items included in the Action Plan Survey 2022-06.IA2030_Impact_Acceleration_Report_Survey.docx: List of items included in the Impact Acceleration Report Survey questionnaire administered in June 2022. 2023-06.IA2030_Impact_Acceleration_Report_Survey.docx: List of additional items included in the Impact Acceleration Survey administered in June 2023 (the complete survey questionnaire also included all items included in the June 2022 Impact Acceleration Report Survey questionnaire). 2023-09.IA2030_Action_Plans_and_Impact_Acceleration_Reports_Survey_Dataset.csv: Anonymized Action Plans and Acceleration Reports Survey Dataset. Version 1: Geneva Learning Foundation (937 observations, 173 variables). ## Related Data Sets The 2023-09.IA2030_Action_Plans_and_Impact Acceleration_Reports_Survey_Dataset.csv is a subset of data collected by The Geneva Learning Foundation (TGLF) during the first IA2030 Full Learning Cycle (FLC) during May 2022, June 2022, and June 2023. The complete IA 2030 Action Plans and Impact Acceleration Reports Survey data set is more comprehensive and includes information about respondent and programme characteristics as well as responses to open-text questions.  Researchers who would like to analyze the full set of unredacted responses are invited to contact the Geneva Learning Foundation to inquire about a data sharing agreement that would stipulate conditions of access (insights@learning.foundation). 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. https://doi.org/10.5281/zenodo.7763922  The Geneva Learning Foundation, 2023. Full Learning Cycle (2022) Application for national and sub-national immunization staff to identify challenges and join the Movement for Immunization Agenda (IA 2030) (Version 1.0) [Data Set]. The Geneval Learning Foundation. https://doi.org/10.5281/zenodo.8199552 Additional data sets for the first Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) are available from TGLF’s Insights Unit (insights@learning.foundation). ## Background The Immunization Agenda 2030 (IA2030), the global immunization strategy for 2021-2030, set ambitious targets for global immunization coverage and other key indicators (World Health Organization [WHO], 2020). In response to the WHO Director-General’s call for a social movement to ensure immunization remains a priority for global and regional health agendas and promote broad societal support for immunization (WHO, 2020), TGLF, working with its global community of over 35,000 alumni, developed a learning programme intended to contribute to a “Movement for Immunization Agenda 2030.” As part of their participation in the programme, IA2030 participants developed a locally tailored action plan designed to address a key challenge relevant to their immunization programme. A project kick-off phase, the “Impact Accelerator Launch Pad”, supported participants during their initial implementation stages, to create momentum for sustained action. One month after initiation of their Action Plan(s), participants were invited to complete an Impact Acceleration survey questionnaire to assess progress towards their goals. One year later, programme participants were invited to complete a second Impact Acceleration questionnaire to assess continued progress towards their goal. This data set includes participants’ responses to one or more questionnaires documenting their IA2030 experience: - Survey 1: IA2030 Action Plan Survey: a 77-item questionnaire, fielded in May 2022, which asked respondents to identify a key challenge relevant to their immunization programme, root cause of the challenge, three corrective actions, and what they knew about vaccination coverage in their zone of intervention. - Survey 2: IA2030 Impact Acceleration Report Survey (2022): a 27-item questionnaire, fielded in June 2022, which asked respondents to report progress on implementation of their IA2030 Action Plan, including progress on vaccination coverage indicators. - Survey 3: IA2030 Impact Acceleration Report Survey (2023): a 60-item questionnaire, fielded in June 2023, which asked respondents to report progress on implementation of their IA2030 Action Plan, including progress on vaccination coverage indicators. ## Data Set Structure and Contents The data set has 937 observations and 173 variables. Contents include the following: - 937 responses (532 English; 405 French) to the Action Plan Survey. Six individuals submitted two distinct Action Plans. - 538 responses (278 English; 260 French) to the 2022 Impact Acceleration Report Survey - 236 responses (126 English; 110 French) to the 2023 Impact Acceleration Report Survey - Column A describes the type of data included in each row of the data file - The first two rows of the data set hold the question text in English and in French - Column B contains a unique identifier for each respondent. The ID is a simple string starting with a number to indicate a unique person and then a dash, and then a 1 or a 2 to indicate whether it is their first or second entry for the 2022 Impact Acceleration Report Survey.  - Columns D-F indicate the language each respondent used for each of the three questionnaires (EN = English; FR = French) - The columns whose names start with I1 were collected as part of the Action Plan Survey, I2 as part of the 2022 Impact Acceleration Report Survey, and I3 as part of the 2023 Impact Acceleration Report Survey.  The columns appear in left-to-right order in which the original surveys were administered. ## Data Cleaning, Data Privacy, and Anonymization Duplicate surveys submitted by the same individual were removed from the data set. When a respondent submitted multiple versions of the Action Plan or 2023 Impact Acceleration Report Survey, the submission with the most complete responses was retained. Six persons submitted two distinct records for the 2022 Impact Acceleration Report Survey. In those cases, both responses appear in the dataset, paired with that person’s single retained entry for the Action Plan and their single retained entry for the 2023 Impact Acceleration Report Survey (where applicable). The data file shared on this site is intentionally incomplete. Columns containing holding personally identifying information (e.g., respondent email address, date of birth, WhatsApp number, organization name, country) and columns potentially holding personally identifying information (e.g., responses to items such as “summarize your challenge,” “tell us about your stakeholders”) have been replaced with the following phrases: - “Removed to protect personally identifying information” (English questionnaires) - “Supprimé pour protéger les informations personnelles” (French questionnaires) ## Ethical Considerations  Ethical clearance for each of the three surveys was granted by TGLF prior to its administration. Privacy was safeguarded by data management complying with the [General Data Protection Regulation](https://gdpr-info.eu/). All participants were informed that participation was voluntary and were assured their data would be confidential. Participants provided consent prior to completing each survey. Respondents did not receive any incentive for participating in any of the surveys. All information collected for the three surveys was secured with multiple levels of encryption and access controls to guarantee participant confidentiality, per [Typeform policy](https://www.typeform.com/help/a/security-at-typeform-360029259552/)). ## Survey Implementation For each of the three surveys, IA2030 participants received an email message inviting them to complete a questionnaire. Respondents accessed the questionnaire by clicking a link contained in the email. All surveys were hosted on the Typeform server and followed the same methods for protecting respondent privacy (details provided in Ethical Considerations section, above). Respondents were given the choice of completing each questionnaire in either English or French. All survey questionnaires included a mixture of multiple-choice and open-text items. Overviews of the contents of each survey are provided in the next three subsections. Copies of the survey instruments are available for download in the Files section (below). ## Survey Questionnaire Items ### IA 2030 Action Plan Survey Items - To start your IA2030 Action Plan, describe one challenge you face in your immunization work. We suggest you focus on your most difficult and important challenge. Focus on only one problem where you can take action. Be specific about your situation, not generalities. Help others understand why it matters. Explain your situation. What is the root cause of your challenge? What will be different if you overcome this challenge? - Summarize your challenge and what you want to do about it. - Is your challenge too broad, general, or vague, like “coverage is too low” or “there are unvaccinated children”? Can you take action at your level to tackle this challenge? - Immunization Agenda 2030 is organized into seven Strategic Priorities (SP). Review the list carefully to select the Strategic Priority that best fits your challenge. IA2030 Strategic Priorities are organized into 47 Key Focus Areas (KFAs). Review the list carefully to select the item that best fits your challenge. - Is your challenge related to any of these? - To tackle your challenge, define the goal of your IA2030 Action Plan. - Why is this goal important? Answer the question, “If I achieve my goal, what will be different?” What does success look like? - Have you started to take action toward this goal? - Summarize what you have already done toward this goal. Who did what, when and how? How did it turn out? Do you have any concrete results to date? What are the lessons or surprises so far? - Tell us about your stakeholders. Who will you need to work with to achieve your goal? What type of support will you need from the communities you serve? How should your organization, supervisor, and partners support you? What are the likely barriers to securing their support and commitment? What are you going to do if a stakeholder does not provide the support you need? Could the support of IA2030 Movement colleagues be helpful to you and, if so, how? - Do you need additional funding to achieve your goal? - How much time do you need to achieve your goal? (Less than one month, 1-3 months. 4-6 months, 7-12 months, 13-18 months, 19-24 month, more than 24 months) - Tell us more about resources and timeline. Provide a rough initial estimate of the time and budget required for your Action Plan. If this is premature, indicate what you need to know to be able to determine time and budget required. - What resources (time, people, money) will you need to achieve your goal? How are you going to secure these resources? Summarize the steps to secure resources. - Now, we would like you to identify three different, specific actions to achieve the goal of your IA2030 Action Plan: Action 1. What do you want to do? Have you done this before? Who will carry out this action? How much time do you need to finish this action? How innovative is this action? Tell us more about this action? - Explain why this action matters. How will this action help you achieve your goal? What needs to happen and when? What are the steps to finish this action? Who will carry out this action? How will you know when you have finished your action? How will you know if your action is successful? - What else needs to happen to achieve your goal? - What other actions need to be taken? When do they need to happen? Who will carry them out? How will these actions help to achieve your goal? What is your role in them? What control do you have over these actions? - Do you wish to share experience to help you with your IA2030 Action Plan and to help others? - What is the name of your organizational unit or area of intervention? - What is the main type of environment in: [your zone of intervention]? - Do you know the size of the vaccination target population in: [your zone of intervention]? - What is the size of the population in: [your zone of intervention]? - Do you know the number of outreach immunization sessions that were planned and then actually carried out last year: - What was the number of planned outreach immunization sessions held last year in: [your zone of intervention]? - What was the number of outreach immunization sessions that were actually carried out last year in: [your zone of intervention]? - Do you know the Antenatal Care (ANC) Coverage in: [your zone of intervention]? - What is the Antenatal Care (ANC) Coverage in: [your zone of intervention]? - Do you know the current Penta 1 coverage in: [your zone of intervention]? - What is the current Penta 1 coverage in: [your zone of intervention]? - Do you know the current Penta 3 coverage in: [your zone of intervention]? - What is the current Penta 3 coverage in: [your zone of intervention]? - Do you know the MCV1 immunization coverage in: [your zone of intervention]? - What is the MCV1 immunization coverage in: [your zone of intervention]? - Do you know the MCV2 immunization coverage in: [your zone of intervention]? - What is the MCV2 immunization coverage in: [your zone of intervention]? - Do you know the vaccination coverage for the first dose of COVID-19 vaccine in: [your zone of intervention]? - What is the vaccination coverage for the first dose of COVID-19 vaccine in your area? - You have now prepared your IA2030 Action Plan. Are you ready to take action? If your Action Plan is accepted, you will be invited to join the 2nd IA2030 Impact Accelerator Launch Pad to work with others during four weeks to start implementing your Action Plan. - What will you seek to achieve in the FOUR WEEKS of the Launch Pad? - What is the MOST IMPORTANT ACTION that you will carry out in the first 7 days of joining the Launch Pad? ### IA2030 Impact Acceleration Report Survey Items (2022) - Has there been a CHANGE or PROGRESS since your last Acceleration Report? Tell us what has happened. -What has changed since your last Acceleration Report? - What is the implementation status of your action plan? - (If applicable) Please tell us why you have not yet started to implement your IA2030 action plan. What are the barriers or obstacles that have prevented you from starting implementation? What support do you need that will help you overcome these barriers? What will you do now to start implementing? - Tell us more about the implementation status of your action plan. What actions did you take and what changed as a result of your actions? What are the results? What were the challenges and how did you address them? What strategies or solutions were critical to implementation? Who or what helped you? What lessons did you learn? Was there anything that surprised you? Is there anything else you would like us to know? - Is your action plan integrated with the broader EPI programme? - Has participation in your Scholar Country Team’s activities been USEFUL to you? - Have you interacted personally with other members of the IA2030 Movement since your last Acceleration Report?? (If yes) What was the main reason for this interaction? - How many Movement members have you personally interacted with since your last Acceleration Report? - Name up to three Movement members that you have personally interacted with since your last Acceleration Report. - Do you have something you would like to share about implementation of your IA2030 action plan? (If yes) Please tell us what happened and how it turned out. Why does this matter? How did you identify it? What have you done to learn more about it? What strategies or solutions have you tried or would you consider? Who or what can help you? What lessons have you learned so far? Was there anything that surprised you? Anything else that we need to know? - Did you participate in the IA2030 Impact Accelerator Launch Pad from 23 May - 17 June 2022? - Has your participation in the Accelerator helped you progress toward implementation of your IA2030 action plan? Tell us how the Accelerator has helped (or not) you progress toward implementation of your IA2030 action plan. - What was the most valuable aspect of the Accelerator for you? - Would you participate in the next IA2030 Impact Accelerator Launch Pad? - Any other comments or feedback? ### IA2030 Impact Acceleration Report Survey Items (2023) *Same questions as for Accelerator Report 2022, with the addition of the following:* - How many HOURS PER WEEK on the average did you EXPECT TO SPEND to implement your plan? - How many HOURS PER WEEK on the average did you ACTUALLY DEDICATE to implement your plan? - What percentage of your work time have you spent on implementation of this action plan? - Did someone provide funding towards the implementation of your action plan? (specify) - Estimate the total budget in U.S. dollars (USD or US$) you need to implement your action plan. You will then be asked to justify this budget. - If you have not yet developed a budget for your action plan, a rough estimate will suffice. Enter this amount in U.S. dollars. - Do you need additional financial resources to successfully implement your action plan? - Of this total budget US$ ,how much are you currently short, in U.S. dollars? - Do you know where to get this additional funding for your action plan? - Justify your estimate of this budget (US$ ) and the need for additional resources (if any). - Summarize what these resources would be used for. Without these resources, what will happen? - If your action plan is successful, how many more people will be vaccinated? - If your action plan is successful, what will be different for the target population? - In the last year, has there been a significant change in vaccination coverage indicators? - This change is the result of my involvement in the IA2030 Movement (range: 1=Strongly disagree to 6=Strongly agree) - To what do you primarily attribute this change? - Explain why you primarily attribute this change to [factor] - Without [factor] what would have been different? Why? How did your involvement in the IA2030 Movement contribute to this change? ## Data Availability and Accessibility This data set is being made available through Zenodo, an open access repository. There are no restrictions on access to open data except those stated in the copyright and license. Researchers who would like to analyze the full set of unredacted responses are invited to contact the Geneva Learning Foundation to inquire about a data sharing agreement that would stipulate conditions of access (<insights@learning.foundation>). ## Copyright and License © The Geneva Learning Foundation 2023. This data set and all associated files are licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) 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 above. In any use of this data set, there should be no suggestion that the Foundation endorses any specific organization, products, or services. 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. 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 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 interpretation and use of the material lies with the reader. In no event shall the Foundation be liable for damages arising from its use. The data set contains individual views and does not necessarily represent the decisions or the policies of the Foundation. Version 1.0 (September 2023). ## References Asiwome Kosi Abiwu, P.H., and Jones, I. (2022). IA2030 Case study 5. Paul Hilarius Asiwome Kosi Abiwu. View from the frontline: building stronger systems (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo7004284. Ateke, N.A., and Jones, I. (2022). IA2030 Case study 4. Njoh Andreas Ateke. View from the frontline: working with communities to strengthen immunization programmes (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo7004263. Ekwam, D.K., and Jones, I. (2022). IA2030 Case study 13. Pharm. Daniel Kwesi Ekwam. Covering all the bases (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo7010227. Faye, W., Jones, I., Mbuh, C., and Sadki, R. (2023). IA2030 Case study 18. Wasnam Faye. Vaccine angels - Give us the opportunity and we can perform miracles. (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo7785244. Faye, W., Jones, I., Mbuh, C., and Sadki, R. (2023). IA2030 Étude de cas 18. Wasnam Faye. Les anges de la vaccination: « Donnez-nous l’occasion et nous ferons des miracles. » (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7849329. Ferrah Conteh, Z., and Jones, I. (2022). IA2030 Case study 6. Zainab Ferrah Conteh. View from the frontline: Strenghtening the immunization programme backbone (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010079. Iwot, N., and Jones, I. (2022). IA2030 Case study 14. Ndaeyo Iwot. Building the capacities of communities (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010211. Jones, I., Mbuh, C., and Sadki, R. (2023). Listening and Learning at Teach to Reach 7 (IA2030 Listening and Learning Report 4) (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7766585. Jones, I., Mbuh, C., and Sadki, R. (2023). Teach to Reach 7: Partager pour apprendre (Rapport IA2030 «Écouter pour apprendre n° 4) (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7851783. Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030 Case study 1. A digital health platform for immunization and primary health care (PHC) engagement (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.6982953. Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030 Case study 1. A digital health platform for immunization and primary health care (PHC) engagement (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.6982953. Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030 Case Study 10. Development of zero dose situational analyses and action plans (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010177. Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030 Case study 2. Squaring the circle: safeguarding existing immunization programmes and primary healthcare during COVID-19 vaccination (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7004191. Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030 Case Study 8. Practitioners’ perceptions of key immunization challenges (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7005241. Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030 Listening and learning report 1. Working groups consultative engagement through a peer learning platform (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010140. Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030 Movement Year 1 report. Consultative engagement through a digitally enabled peer learning platform (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7119648. Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030. Listening and learning report 3. Everyone, everywhere: digital peer learning to leave no one behind (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010209. Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). Mouvement pour la vaccination à l’horizon 2030. Rapport Écouter pour apprendre n° 3. Partout, pour tous: l’apprentissage numérique par les pairs pour que personne ne reste sur le côté (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7142102. Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., Kovanovic, V., et al. (2022). IA2030 Rapport «Écouter pour apprendre» n° 2. Notre engagement pour la vaccination #VaccinesWork (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7254160. Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., Kovanovic, V., et al. (2022). IA2030.Listening and learning report 2. It takes people to make #VaccinesWork (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010196. Jones, I., Sadki, R., Brooks, A., Gasse, F., Yagnik, A., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., et al. (2022). IA2030 Case Study 9. Understanding and responding to zero-dose challenges (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010171. Jones, I., Watkins, K.E., Sadki, R., Brooks, A., Gasse, F., Yagnik, A., Mbuh, C., Zha, M., Steed, I., Sequeira, J., et al. (2022). IA2030 Case Study 7. Motivation, learning culture and programme performance (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7004304. Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V.(2022). IA2030 Year 1 report. Consultative engagement through a digitally enabled peer learning platform (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7119648. Jones, I., Watkins, K.E., Sadki, R., Brooks, A., Gasse, F., Yagnik, A., Mbuh, C., Zha, M., Steed, I., Sequeira, J., et al. (2022). IA2030 Case Study 7. Motivation, learning culture and programme performance (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7004304. Kaboré, B., and Jones, I. (2022). IA2030 Case study 12. Boureima Kaboré. Delivering services in conflict-affected areas (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010188. Kuotong Nongho, R., and Jones, I. (2022). IA2030 Case study 3. Kuotong Nongho Rogers. View from the frontline: microplanning for equity (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7004235. Mbuh, C., Gasse, F., Jones, I., Sadki, R., Brooks, A., Zha, M., Steed, I., Sequeira, J., Churchill, S., and Kovanovic, V. (2022). IA2030 Case study 16. Continuum from knowledge to performance (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7014392. Monga, F., Jones, I., Mbuh, C., and Sadki, R. (2023). IA2030 Case study 19. Franck Monga. Building a movement for IA2030 in the Democratic Republic of Congo (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7794726. Monga, F., Jones, I., Mbuh, C., and Sadki, R. (2023). IA2030 Étude de cas 19. Franck Monga. Construire un Mouvement pour la vaccination en République Démocratique du Congo (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7851476. Monzón, M.F., Jones, I., Mbuh, C., and Sadki, R. (2023). IA2030 Case study 17. María Fernanda Monzón. Speaking up for frontline staff (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7785024. Monzón, M.F., Jones, I., Mbuh, C., and Sadki, R. (2023). IA2030 Étude de cas 17. María Fernanda Monzón. Prendre la parole au nom des personnels de première ligne (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7848308. Oketch, P., and Jones, I. (2022). IA2030 Case study 15. Penina Oketch. Planning, people and performance (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010242. Sadki, R., Mbuh, C., Szylovec, A.P., and Brooks, A. (2023). Value creation stories anonymized open data set (Immunization Agenda 2030 Full Learning Cycle, 7 March - 20 June 2022). https://doi.org/10.5281/zenodo.7763922. Sadki, R. Mbuh, C., Zha, M., Gasse, F., Brooks, A. The Geneva Learning Foundation, 2023. Full Learning Cycle (2022) Application for national and sub-national immunization staff to identify challenges and join the Movement for Immunization Agenda (IA 2030) (Version 1.0) [Data Set]. The Geneval Learning Foundation.https://doi.org/10.5281/zenodo.8199552 Umbelino-Walker, I., Szylovec, A.P., Brooks, A., Sadki, R., Mbuh, C., Jones, I., Monglo, A., Dakan, B.A. (2023). Towards a sustainable model for a digital learning network in support of the Immunization Agenda 2030 -- a mixed methods study with a transdisciplinary component. Manuscript under review. WHO (2020). Immunization Agenda 2030: A Global Strategy to Leave No One Behind [Internet]. Available from: https://www.who.int/teams/immunization-vaccines-and-biologicals/strategies/ia2030.  Yagnik, A., Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., et al. (2022). IA2030 Case Study 11. Gender barriers to immunization (The Geneva Learning Foundation) https://doi.org/10.5281/zenodo.7010184.

dryad32/100

Data from: Rye planting date impacts biomass production more than seeding rate and nitrogen fertilizer

This zipped folder contains the code and datasets used for an experiment conducted over six years (2015–2020) at the Wiregrass Research and Extension Center in Headland, AL to examine rye biomass production. Treatments included four planting dates (late October, early November, late November, and early December), two seeding rates ( 67 and 101 kg ha-1), and four nitrogen rates (0, 34, 67, and 101 kg ha-1) replicated four times for a total of 128 observations each year. Measured and/or calculated variables from the data set included biomass, total C and N concentrations for the plant material, C:N ratio, N uptake values, as well as cost info for the biomass. In addition, there are three years of peanut yield data (2015, 2017, 2019) and only two years of cotton yield data (2016, 2020). No cotton data was collected for 2018 due to hurricane damage. Initial and final soil total C values were also measured across the 0 and 101 kg N ha-1 rates for the 67 kg ha-1 seeding rate.

opencc-zeroSep 2023View details →

opencc-by-4.0Mar 2023View details →

Exploring the Impact of Inheritance on Test Code Maintainability

Dataset on Inheritance and Interface in the Test Code:   Folder Structure: <ul> <li>result_inheritance     - studied_project:          [1] refactoring_history: Dataset for mined inheritance changes.         [2] refactoring_tree: Dataset for Evolution Graph in the paper Section 3.2.1</li> <li>RQ2_A_manual         [1] Manual study result in RQ2-A.  </li> </ul>

opencc-by-4.0Aug 2022View details →

Model files for"Impact of Horizontal Resolution (Submesoscale Permitting vs Mesoscale Resolving) on Ocean Dynamic Features in the South China Sea "

These four files are the model configurations as well as  model grids for the SP and MR model mentiond in the manuscript titled with "Impact of Horizontal Resolution (Submesoscale Permitting vs Mesoscale Resolving) on Ocean Dynamic Features in the South China Sea", which would be potentially published in the Journal of Earth and Space Science. With the model grids and publicly available forcing field data introduced in the MS, it is easy to generate model input files. Finally, with the configuration files (e.g., .h, .in ), one can easily reproduce all the model results.

Simulation data for "Impact of Dynamic Phytoplankton Stoichiometry on Global Scale Patterns of Nutrient Limitation, Nitrogen Fixation, and Carbon Export"

Simulation data for our submission to GBC. Some of the files are compressed in order to minimize the size of the archive.  Data files are stored as pickled python variables. The code for analysis of this data is in the github repository: https://github.com/georgehagstrom/BGCPaperFigureCode .

opencc-by-4.0Oct 2023View details →

Supplemental Material for "Objectives and Key Results in Software Teams: Challenges, Opportunities and Impact on Development"

This package contains the research materials that were used for the paper "Objectives and Key Results in Software Teams: Challenges, Opportunities and Impact on Development". <ul> <li>Survey on Goals and Objectives.docx contains the survey instrument that was used for the study.</li> </ul>

opencc-by-4.0Oct 2023View details →

Data for "Impact of time-dependent data assimilation on ice flow model initialization and projections: a case study of Kjer Glacier, Greenland"

Data for the main figures for "Impact of time-dependent data assimilation on ice flow model initialization: A case study of Kjer Glacier, Greenland"

Supplementary data for "Buoyant impact melts on ancient Mars"

MELTS inputs and lookup tables used to determine melt fraction, composition, and density for impact partial melts on Mars.