cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13619887
title: Impact of Grid Resolution on Wave-mean Flow Interactions with High Resolution Mars Global Climate Model Simulations

description: This dataset contains NetCDF files necessary to replicate results from the 2024 paper "Impact of Grid Resolution on Wave-mean Flow Interactions with High Resolution Mars Global Climate Model Simulations"

The dataset contains NetCDF files with 1 year of zonally-averaged NASA Ames Mars Global Climate Model (MGCM) fields with 5-sol binning for each of the simulations presented in the paper: 



a "low-resolution" simulation with no parameterization for gravity waves

a "high-resolution" simulation with no parameterization for gravity waves

a "low-resolution" simulation with parameterizations for orographic and non-orographic gravity waves


Also included are:



a file describing the coordinates for the MGCM's vertical grids used in the study 

 atmospheric fields not provided in the other NetCDF files and necessary to replicate figures 3 and supplemental figure FS2 from the paper.

a README.txt detailing the content of each file in the dataset
DOI: 10.5281/zenodo.1403195
title: AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: EPIC-IIASA wheat

description: This is model output from EPIC-IIASA for wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.

The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).

References:

Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015

Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017

Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8
DOI: 10.5281/zenodo.1403197
title: AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: EPIC-IIASA soy

description: This is model output from EPIC-IIASA for soy as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.

The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).

References:

Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015

Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017

Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8
DOI: 10.5281/zenodo.1403199
title: AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: EPIC-IIASA rice

description: This is model output from EPIC-IIASA for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.

The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).

References:

Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015

Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017

Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8
DOI: 10.5281/zenodo.1403203
title: AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: EPIC-IIASA maize

description: This is model output from EPIC-IIASA for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.

The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).

References:

Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015

Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017

Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8
DOI: 10.5281/zenodo.1409546
title: AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: PEGASUS wheat

description: This is model output from PEGASUS for wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.

The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).

References:

Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015

Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017

Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8
DOI: 10.5281/zenodo.1409548
title: AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: PEGASUS soy

description: This is model output from PEGASUS for soy as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.

The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).

References:

Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015

Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017

Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8
DOI: 10.5281/zenodo.1409550
title: AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: PEGASUS maize

description: This is model output from PEGASUS for soy as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.

The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).

References:

Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015

Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017

Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8
DOI: 10.5281/zenodo.3559850
title: GPP at FLUXNET Tier 1 sites from P-model

description: Gross primary production, simulated by the P-model for each FLUXNET 2015 Tier 1 site. The model was driven by site-specific meteorological forcing and MODIS FPAR, extracted for the pixel corresponding to the site location. The CSV files contain simulated GPP values from different model setups conducted with the P-model and used for the publication Stocker et al. <em>Geosci. Mod. Dev. </em>(in review). One file is given for each temporal aggregation level (daily, 8-daily, annual, spatial [= mean annual value by site], and mean seasonal cycle [= mean per day-of-year]. Each file contains output from all model setups presented in Stocker et al. (2019), as given by column <em>setup</em>. The data differs slightly for each file: <strong>Daily</strong> gpp_pmodel_fluxnet2015_stocker19gmd_daily.csv: <em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015. <em>date: </em>YYYY-MM-DD), date_start (in _8daily, YYYY-MM-DD specifying the first day of the respective 8-day period), year (in _annual, YYYY), doy (in __meanseason, specifying the day-of-year), <em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> d<sup>-1</sup> <em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below. <strong>8-daily</strong> gpp_pmodel_fluxnet2015_stocker19gmd_8daily.csv: <em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015. <em>date_start</em> : YYYY-MM-DD specifying the first day of the respective 8-day period <em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> d<sup>-1</sup> <em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below. <strong>Annual</strong> gpp_pmodel_fluxnet2015_stocker19gmd_annual.csv: <em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015. <em>year: </em>YYYY <em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> yr<sup>-1</sup> <em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below. <strong>Spatial</strong> gpp_pmodel_fluxnet2015_stocker19gmd_spatial.csv: <em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015. <em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> yr<sup>-1</sup> <em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below. <strong>Mean seasonal cycle</strong> gpp_pmodel_fluxnet2015_stocker19gmd_meanseason.csv: <em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015. <em>doy: </em>day-of-year <em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> d<sup>-1</sup> <em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below.
DOI: 10.6073/pasta/1f780cc1b9e31f58a87d72b8eb2693ea
title: Physical and chemical data for various lakes near Toolik Research Station, Arctic LTER. Summer 1983 to 1989.

description: Decadal file describing the physical lake parameters recorded at various lakes near Toolik Research Station during summers from 1983 to 1989. Depth profiles at the sites of physical measures were collected in situ. Values measured included temperature, conductivity, pH, dissolved oxygen, Chlorophyll A, Secchi disk depth and PAR. Note that some sample depths also have additional parameters measured and available in separate files for water chemistry and primary production.
DOI: 10.6084/m9.figshare.28302242
title: Supplementary Material for: Pharmacological characterization of GB1908, a selective galectin-1 carbohydrate binding domain inhibitor for the treatment of cancer.

description: Introduction: Galectin-1 (Gal-1) is a lectin that has been shown to be involved in a number of pro-tumorigenic mechanisms and has also been shown to be immune-suppressive. Therefore, pharmacological blockade of Gal-1 has the potential to be therapeutically beneficial in cancers that overexpress this lectin where it is hypothesized to be driving cancer progression. 
Methods: GB1908 is a novel, selective and high affinity inhibitor of the Gal-1 carbohydrate recognition domain and in this study we have pharmacologically characterized this small molecule in a range of in vitro and in vivo systems in the context of cancer therapy. In addition, we used a data-driven approach to identify the cancer types which may benefit from Gal-1 inhibitor therapy. 
Results: The selectivity of GB1908 for Gal-1 compared with galectin-3 (Gal-3) was confirmed in biophysical and cellular assays. GB1908 attenuated Gal-1-induced T cell (Jurkat) apoptosis and reduced the production of immunosuppressive cytokines in a stromal non-small cell lung cancer (NSCLC) tumor microenvironment model. Breast carcinoma and metastatic skin cutaneous melanoma were identified as cancers in which high Gal-1 expression correlated with poorer survival outcomes in patients. Treatment with GB1908 slowed tumor growth in syngeneic mouse models of these cancers.
Conclusion: The inhibition of both tumor growth and immune-suppressive cytokines, in cancers in which high Gal-1 is associated with poorer survival outcomes, suggests a potential therapeutic benefit for Gal-1 inhibitors such as GB1908.
DOI: 10.6084/m9.figshare.28303454
title: Supplementary Material for: Hidden Resources for Cancer Patients – Interactions between spirituality, religiosity and selected factors influencing patient’s self-care

description: Introduction. Religiosity and spirituality (SpR) can be vital in helping people face life's challenges. While Spiritual Care (SpC) is used in palliative care, this study explores effects for cancer patient’s self-care (SC) in earlier stages . 
Methods. Using validated instruments we surveyed patients about SpR (SpNQ-20, GrAw-7, SpREUK), and factors of SC: well-being (WHO-5, L-1), self-efficacy (ASKU), ability to change (PIAC), as well as lay etiology and the use of Complementary and alternative Medicine (CAM). Data were analyzed using SPSS according a three-step plan with descriptive methods, Spearman correlations and mediation analysis.
Results. We included 108 patients (41 female, 63 males, four no data) with a median age of 66 years (range 30-89). Welch tests show a less well-being, self-efficacy and ability to change in our study population (p &lt; 0.05) compared to non-cancer controls. Whitney-U-test has documented increased well-being, CAM use if the patients were S/R+ (p &lt; 0.05). S/R self-categorization had no impact on PIAC and ASKU. Perceptual spirituality (GrAw-7) correlates with all factors of SC (p &lt; 0.05): WHO-5 (rs = 0.25), PIAC (rs = 0.25), ASKU (rs = 0.296), L-1 (rs = 0.35) and CAM use (rs = 0.39, p &lt; 0.001). Mediation analysis demonstrates that the impact of Spirituality on self-care (CAM use) is mediated by religiosity, GrAw-7 and spiritual needs. 
Conclusion. Spirituality and hidden spiritual needs are a valuable resource. By integrating SpC early in the treatment, we can create support ways and improve SC, well-being and coping.
DOI: 10.6084/m9.figshare.28320866
title: Supplementary Material for: Analysis Method of Real-World Digital Biomarkers for Clinical Impact in Cancer Patients

description: Introduction: Wearable technologies can enhance measurements completed from home by participants in decentralized clinical trials. These measurements have shown promise in monitoring patient wellness outside the clinical setting. However, there are challenges in handling data and its interpretation when using consumer wearables, requiring input from statisticians and data scientists. This paper describes three methods to estimate daily steps to address gaps in data from the Apple Watch in cancer patients and uses one of these methods in an analysis of the association between daily step count estimates and clinical events for these patients.
Methods: A cohort of 50 cancer patients used the DigiBioMarC app integrated with an Apple Watch for 28 days. We identified different gap types in watch data based on their length and context to estimate daily steps. Cox proportional hazards regression models were used to determine the association between step count and time to death or time to first clinical event. Decision tree modeling and participant clustering were also employed to identify digital biomarkers of physical activity that were predictive of clinical event occurrence and hazard ratio to clinical events, respectively. 
Results: Among the three methods explored to address missing steps, the method that identified different step data gap types according to their duration and context provided the most reasonable estimate of daily steps. Ten hours of waking time was used to differentiate between sufficient and insufficient measurement days. Daily step count on sufficient days was the most promising predictor of time to first clinical event (p = 0.068). This finding was consistent in participant cluster and decision tree analyses where the participant cluster that emerged naturally from the different levels of daily steps was the group with the highest steps on sufficient days and lowest hazard probability of mortality and clinical events. Additionally, daily steps on sufficient days can also be used as a predictor of whether a participant will have clinical events with an accuracy of 83.3%.
Conclusion: We have developed an effective way to estimate daily steps from consumer wearable data containing unknown data gaps. Daily step counts on days with sufficient sampling is a strong predictor of the timing and occurrence of clinical events, with individuals exhibiting higher daily step counts having reduced hazard of death or clinical events.