EarthCube Bibliography

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Abduallah, Y., J. T. L. Wang, Y. Nie, C. Liu, and H. Wang, 2021: DeepSun: machine-learning-as-a-service for solar flare prediction. Res. Astron. Astrophys., 21, 160, https://doi.org/10.1088/1674-4527/21/7/160.

——, V. K. Jordanova, H. Liu, Q. Li, J. T. L. Wang, and H. Wang, 2022: Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network. ApJS, 260, 16, https://doi.org/10.3847/1538-4365/ac5f56.

Aberger, C. R., A. Lamb, S. Tu, A. Nötzli, K. Olukotun, and C. Ré, 2017: EmptyHeaded. ACM Trans. Database Syst., 42, 1–44, https://doi.org/10.1145/3129246.

Abernathey, R. P., and Coauthors, 2021: Cloud-Native Repositories for Big Scientific Data. Comput. Sci. Eng., 23, 26–35, https://doi.org/10.1109/mcse.2021.3059437.

Al-Saadi, A., I. Paraskevakos, B. C. Gonçalves, H. J. Lynch, S. Jha, and M. Turilli, 2021: Comparing workflow application designs for high resolution satellite image analysis. Future Generation Computer Systems, 124, 315–329, https://doi.org/10.1016/j.future.2021.04.023.

Balasubramanian, V., and Coauthors, 2018: Harnessing the Power of Many: Extensible Toolkit for Scalable Ensemble Applications. Harnessing the Power of Many: Extensible Toolkit for Scalable Ensemble Applications, https://doi.org/10.1109/ipdps.2018.00063.

Bhatt, A., T. Valentic, A. Reimer, L. Lamarche, P. Reyes, and R. Cosgrove, 2020: Reproducible Software Environment: a tool enabling computational reproducibility in geospace sciences and facilitating collaboration. J. Space Weather Space Clim., 10, 12, https://doi.org/10.1051/swsc/2020011.

Blumberg, K. L., A. J. Ponsero, M. Bomhoff, E. M. Wood-Charlson, E. F. DeLong, and B. L. Hurwitz, 2021: Ontology-Enriched Specifications Enabling Findable, Accessible, Interoperable, and Reusable Marine Metagenomic Datasets in Cyberinfrastructure Systems. Front. Microbiol., 12, https://doi.org/10.3389/fmicb.2021.765268.

Bolton, D. C., P. Shokouhi, B. Rouet‐Leduc, C. Hulbert, J. Rivière, C. Marone, and P. A. Johnson, 2019: Characterizing Acoustic Signals and Searching for Precursors during the Laboratory Seismic Cycle Using Unsupervised Machine Learning. Characterizing Acoustic Signals and Searching for Precursors during the Laboratory Seismic Cycle Using Unsupervised Machine Learning, 90, 1088–1098, https://doi.org/10.1785/0220180367.

——, S. Shreedharan, J. Rivière, and C. Marone, 2020: Acoustic Energy Release During the Laboratory Seismic Cycle: Insights on Laboratory Earthquake Precursors and Prediction. JGR Solid Earth, 125, https://doi.org/10.1029/2019jb018975.

Bolukbasi, B., and Coauthors, 2013: Open Data: Crediting a Culture of Cooperation. Science, 342, 1041–4042, https://doi.org/10.1126/science.342.6162.1041-b.

Brewer, T. E., and Coauthors, 2019: Ecological and Genomic Attributes of Novel Bacterial Taxa That Thrive in Subsurface Soil Horizons. mBio, 10, https://doi.org/10.1128/mbio.01318-19.

van den Brink, L., and Coauthors, 2018: Best practices for publishing, retrieving, and using spatial data on the web. SW, 10, 95–114, https://doi.org/10.3233/sw-180305.

Bromwich, D. H., and Coauthors, 2018: The Arctic System Reanalysis, Version 2. The Arctic System Reanalysis, Version 2, 99, 805–828, https://doi.org/10.1175/bams-d-16-0215.1.

Calyam, P., and Coauthors, 2020: Measuring success for a future vision: Defining impact in science gateways/virtual research environments. Concurrency Computat Pract Exper, 33, https://doi.org/10.1002/cpe.6099.

Cantrall, C., and T. Matsuo, 2021: Deriving column-integrated thermospheric temperature  with the N<sub>2</sub> Lyman–Birge–Hopfield (2,0) band. Atmos. Meas. Tech., 14, 6917–6928, https://doi.org/10.5194/amt-14-6917-2021.

Cash, B. A., and N. J. Burls, 2019: Predictable and Unpredictable Aspects of U.S. West Coast Rainfall and El Niño: Understanding the 2015/16 Event. Predictable and Unpredictable Aspects of U.S. West Coast Rainfall and El Niño: Understanding the 2015/16 Event, 32, 2843–2868, https://doi.org/10.1175/jcli-d-18-0181.1.

Cervone, G., L. Clemente-Harding, S. Alessandrini, and L. Delle Monache, 2017: Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble. Renewable Energy, 108, 274–286, https://doi.org/10.1016/j.renene.2017.02.052.

Chan, M. A., S. E. Peters, and B. Tikoff, 2016: The Future of Field Geology, Open Data Sharing and CyberTechnology in Earth Science. TSR, 14, 4–10, https://doi.org/10.2110/sedred.2016.1.4.

Chen, B., and Coauthors, 2020: Measurement of magnetic field and relativistic electrons along a solar flare current sheet. Nat Astron, 4, 1140–1147, https://doi.org/10.1038/s41550-020-1147-7.

Choi, I., A. J. Ponsero, M. Bomhoff, K. Youens-Clark, J. H. Hartman, and B. L. Hurwitz, 2018: Libra: scalablek-mer–based tool for massive all-vs-all metagenome comparisons. Libra: scalablek-mer–based tool for massive all-vs-all metagenome comparisons, 8, https://doi.org/10.1093/gigascience/giy165.

Choi, Y.-D., and Coauthors, 2021: Toward open and reproducible environmental modeling by integrating online data repositories, computational environments, and model Application Programming Interfaces. Environmental Modelling & Software, 135, 104888, https://doi.org/10.1016/j.envsoft.2020.104888.

Cholia, S., and Coauthors, 2020: Towards Interactive, Reproducible Analytics at Scale on HPC Systems. Towards Interactive, Reproducible Analytics at Scale on HPC Systems, https://doi.org/10.1109/urgenthpc51945.2020.00011.

Chuah, J., M. Deeds, T. Malik, Y. Choi, and J. L. Goodall, 2020: Documenting Computing Environments for Reproducible Experiments. Documenting Computing Environments for Reproducible Experiments, https://doi.org/10.3233/apc200106.

Cox, S., J. Klump, and K. Lehnert, 2018: Connecting Scientific Data and Real-World Samples. Eos, 99, https://doi.org/10.1029/2018eo090337.

Crawford, O., D. Al-Attar, J. Tromp, J. X. Mitrovica, J. Austermann, and H. C. P. Lau, 2018: Quantifying the sensitivity of post-glacial sea level change to laterally varying viscosity. Quantifying the sensitivity of post-glacial sea level change to laterally varying viscosity, 214, 1324–1363, https://doi.org/10.1093/gji/ggy184.

David, C. H., Y. Gil, C. J. Duffy, S. D. Peckham, and S. K. Venayagamoorthy, 2016: An introduction to the special issue on Geoscience Papers of the Future. Earth and Space Science, 3, 441–444, https://doi.org/10.1002/2016ea000201.

Deng, J., W. Song, D. Liu, Q. Li, G. Lin, and H. Wang, 2021: Improving the Spatial Resolution of Solar Images Using Generative Adversarial Network and Self-attention Mechanism*. ApJ, 923, 76, https://doi.org/10.3847/1538-4357/ac2aa2.

Dere, A., 2019: iSamples Sample Management Training Module for Soil Cores. iSamples Sample Management Training Module for Soil Cores, https://doi.org/10.1594/IEDA/100709.

Di, L., Z. Sun, E. Yu, J. Song, D. Tong, H. Huang, X. Wu, and B. Domenico, 2016: Coupling of Earth science models and earth observations through OGC interoperability specifications. Coupling of Earth science models and earth observations through OGC interoperability specifications, https://doi.org/10.1109/igarss.2016.7729933.

Doan, K., A. O. Oloso, K.-S. Kuo, T. L. Clune, H. Yu, B. Nelson, and J. Zhang, 2016: Evaluating the impact of data placement to spark and SciDB with an Earth Science use case. Evaluating the impact of data placement to spark and SciDB with an Earth Science use case, https://doi.org/10.1109/bigdata.2016.7840621.

Dove, N. C., and Coauthors, 2020: Continental-scale patterns of extracellular enzyme activity in the subsoil: an overlooked reservoir of microbial activity. Environ. Res. Lett., 15, 1040a1, https://doi.org/10.1088/1748-9326/abb0b3.

Duncan, C. J., M. A. Chan, E. Hajek, D. Kamola, N. M. Roberts, B. Tikoff, and J. D. Walker, 2021: Bringing sedimentology and stratigraphy into the StraboSpot data management system. Bringing sedimentology and stratigraphy into the StraboSpot data management system, 17, 1914–1927, https://doi.org/10.1130/ges02364.1.

Dye, M., D. S. Stamps, M. Mason, and E. Saria, 2022: Toward Autonomous Detection of Anomalous GNSS Data Via Applied Unsupervised Artificial Intelligence. Int. J. Semantic Computing, 16, 29–45, https://doi.org/10.1142/s1793351x22400025.

Elag, M. M., P. Kumar, L. Marini, J. D. Myers, M. Hedstrom, and B. A. Plale, 2017: Identification and characterization of information-networks in long-tail data collections. Environmental Modelling & Software, 94, 100–111, https://doi.org/10.1016/j.envsoft.2017.03.032.

Emile-Geay, J., D. Khider, N. McKay, Y. Gil, D. Garijo, and V. Ratnakar, 2018: LinkedEarth: supporting paleoclimate data standards and crowd curation. PAGES Mag, 26, 62–63, https://doi.org/10.22498/pages.26.2.62.

Essawy, B. T., J. L. Goodall, H. Xu, and Y. Gil, 2017: Evaluation of the OntoSoft Ontology for describing metadata for legacy hydrologic modeling software. Environmental Modelling & Software, 92, 317–329, https://doi.org/10.1016/j.envsoft.2017.01.024.

——, ——, W. Zell, D. Voce, M. M. Morsy, J. Sadler, Z. Yuan, and T. Malik, 2018: Integrating scientific cyberinfrastructures to improve reproducibility in computational hydrology: Example for HydroShare and GeoTrust. Environmental Modelling & Software, 105, 217–229, https://doi.org/10.1016/j.envsoft.2018.03.025.

——, ——, D. Voce, M. M. Morsy, J. M. Sadler, Y. D. Choi, D. G. Tarboton, and T. Malik, 2020: A taxonomy for reproducible and replicable research in environmental modelling. Environmental Modelling & Software, 134, 104753, https://doi.org/10.1016/j.envsoft.2020.104753.

Fan, Y., and Coauthors, 2014: DigitalCrust - a 4D data system of material properties for transforming research on crustal fluid flow. Geofluids, 15, 372–379, https://doi.org/10.1111/gfl.12114.

Farley, S. S., A. Dawson, S. J. Goring, and J. W. Williams, 2018: Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions. Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions, 68, 563–576, https://doi.org/10.1093/biosci/biy068.

Ferdowsi, B., J. D. Gartner, K. N. Johnson, A. Kasprak, K. L. Miller, W. Nardin, A. C. Ortiz, and A. Tejedor, 2021: Earthcasting: Geomorphic Forecasts for Society. Earth’s Future, 9, https://doi.org/10.1029/2021ef002088.

Fleishman, G. D., G. M. Nita, N. Kuroda, S. Jia, K. Tong, R. R. Wen, and Z. Zhizhuo, 2018: Revealing the Evolution of Non-thermal Electrons in Solar Flares Using 3D Modeling. ApJ, 859, 17, https://doi.org/10.3847/1538-4357/aabae9.

——, S. A. Anfinogentov, A. G. Stupishin, A. A. Kuznetsov, and G. M. Nita, 2021a: Coronal Heating Law Constrained by Microwave Gyroresonant Emission. ApJ, 909, 89, https://doi.org/10.3847/1538-4357/abdab1.

——, L. Kleint, G. G. Motorina, G. M. Nita, and E. P. Kontar, 2021b: Energy Budget of Plasma Motions, Heating, and Electron Acceleration in a Three-loop Solar Flare. ApJ, 913, 97, https://doi.org/10.3847/1538-4357/abf495.

Fredericks, J., and M. Botts, 2018: Promoting the capture of sensor data provenance: a role-based approach to enable data quality assessment, sensor management and interoperability. Open geospatial data, softw. stand., 3, https://doi.org/10.1186/s40965-018-0048-5.

Fuka, D. R., A. S. Collick, P. J. A. Kleinman, D. A. Auerbach, R. D. Harmel, and Z. M. Easton, 2016: Improving the spatial representation of soil properties and hydrology using topographically derived initialization processes in the SWAT model. Hydrol. Process., 30, 4633–4643, https://doi.org/10.1002/hyp.10899.

Gaigalas, ?, ? Di, and ? Sun, 2019: Advanced Cyberinfrastructure to Enable Search of Big Climate Datasets in THREDDS. IJGI, 8, 494, https://doi.org/10.3390/ijgi8110494.

Garijo, D., and Coauthors, 2014a: Workflow Reuse in Practice: A Study of Neuroimaging Pipeline Users. Workflow Reuse in Practice: A Study of Neuroimaging Pipeline Users, https://doi.org/10.1109/escience.2014.33.

——, O. Corcho, Y. Gil, B. A. Gutman, I. D. Dinov, P. Thompson, and A. W. Toga, 2014b: FragFlow Automated Fragment Detection in Scientific Workflows. FragFlow Automated Fragment Detection in Scientific Workflows, https://doi.org/10.1109/escience.2014.32.

——, Y. Gil, and O. Corcho, 2017: Abstract, link, publish, exploit: An end to end framework for workflow sharing. Future Generation Computer Systems, 75, 271–283, https://doi.org/10.1016/j.future.2017.01.008.

——, M. Osorio, D. Khider, V. Ratnakar, and Y. Gil, 2019: OKG-Soft: An Open Knowledge Graph with Machine Readable Scientific Software Metadata. OKG-Soft: An Open Knowledge Graph with Machine Readable Scientific Software Metadata, https://doi.org/10.1109/escience.2019.00046.

Gil, Y., and V. Ratnakar, 2016: Dynamically Generated Metadata and Replanning by Interleaving Workflow Generation and Execution. Dynamically Generated Metadata and Replanning by Interleaving Workflow Generation and Execution, https://doi.org/10.1109/icsc.2016.89.

——, and D. Garijo, 2017: Towards Automating Data Narratives. Towards Automating Data Narratives, https://doi.org/10.1145/3025171.3025193.

——, V. Ratnakar, and D. Garijo, 2015: OntoSoft. OntoSoft, https://doi.org/10.1145/2815833.2816955.

——, and Coauthors, 2016a: Toward the Geoscience Paper of the Future: Best practices for documenting and sharing research from data to software to provenance. Earth and Space Science, 3, 388–415, https://doi.org/10.1002/2015ea000136.

——, D. Garijo, S. Mishra, and V. Ratnakar, 2016b: OntoSoft: A distributed semantic registry for scientific software. OntoSoft: A distributed semantic registry for scientific software, https://doi.org/10.1109/escience.2016.7870916.

——, ——, V. Ratnakar, D. Khider, J. Emile-Geay, and N. McKay, 2017: A Controlled Crowdsourcing Approach for Practical Ontology Extensions and Metadata Annotations. A Controlled Crowdsourcing Approach for Practical Ontology Extensions and Metadata Annotations, 231–246, https://doi.org/10.1007/978-3-319-68204-4_24.

——, and Coauthors, 2018: Intelligent systems for geosciences. Commun. ACM, 62, 76–84, https://doi.org/10.1145/3192335.

Glazner, A., and J. D. Walker, 2020: StraboTools: A Mobile App for Quantifying Fabric in Geology. GSAT, https://doi.org/10.1130/gsatg454a.1.

Goble, C., S. Cohen-Boulakia, S. Soiland-Reyes, D. Garijo, Y. Gil, M. R. Crusoe, K. Peters, and D. Schober, 2020: FAIR Computational Workflows. Data Intellegence, 2, 108–121, https://doi.org/10.1162/dint_a_00033.

Gonçalves, B. C., and H. J. Lynch, 2021: Fine-Scale Sea Ice Segmentation for High-Resolution Satellite Imagery with Weakly-Supervised CNNs. Remote Sensing, 13, 3562, https://doi.org/10.3390/rs13183562.

Gonçalves, B. C., B. Spitzbart, and H. J. Lynch, 2020: SealNet: A fully-automated pack-ice seal detection pipeline for sub-meter satellite imagery. Remote Sensing of Environment, 239, 111617, https://doi.org/10.1016/j.rse.2019.111617.

Granger, B. E., and F. Perez, 2021: Jupyter: Thinking and Storytelling With Code and Data. Comput. Sci. Eng., 23, 7–14, https://doi.org/10.1109/mcse.2021.3059263.

Griffin, J. S., and Coauthors, 2017: Microbial diversity in an intensively managed landscape is structured by landscape connectivity. Microbial diversity in an intensively managed landscape is structured by landscape connectivity, 93, https://doi.org/10.1093/femsec/fix120.

Grimm, E. C., J. Blois, T. Giesecke, R. Graham, A. Smith, and J. Williams, 2018: Constituent databases and data stewards in the Neotoma Paleoecology Database: History, growth, and new directions. PAGES Mag, 26, 64–65, https://doi.org/10.22498/pages.26.2.64.

Gui, Z., and Coauthors, 2016: Developing Subdomain Allocation Algorithms Based on Spatial and Communicational Constraints to Accelerate Dust Storm Simulation. PLoS ONE, 11, e0152250, https://doi.org/10.1371/journal.pone.0152250.

Gundersen, O. E., Y. Gil, and D. W. Aha, 2018: On Reproducible AI: Towards Reproducible Research, Open Science, and Digital Scholarship in AI Publications. AIMag, 39, 56–68, https://doi.org/10.1609/aimag.v39i3.2816.

Hallett, B., 2019: iSamples Sample Management Training Module for Rock Outcrop Samples. iSamples Sample Management Training Module for Rock Outcrop Samples, https://doi.org/10.1594/IEDA/100691.

He, Y., Y. Zhou, T. Wen, S. Zhang, F. Huang, X. Zou, X. Ma, and Y. Zhu, 2022: A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications. Applied Geochemistry, 140, 105273, https://doi.org/10.1016/j.apgeochem.2022.105273.

Held, N., J. Saunders, J. Futrelle, and M. Saito, 2018: Harnessing the Power of Scientific Python to Investigate Biogeochemistry and Metaproteomes of the Central Pacific Ocean. Harnessing the Power of Scientific Python to Investigate Biogeochemistry and Metaproteomes of the Central Pacific Ocean, https://doi.org/10.25080/majora-4af1f417-010.

Held, N. A., and Coauthors, 2020: Co-occurrence of Fe and P stress in natural populations of the marine diazotroph <i>Trichodesmium</i> Biogeosciences, 17, 2537–2551, https://doi.org/10.5194/bg-17-2537-2020.

Hoffman, L., M. R. Mazloff, S. T. Gille, D. Giglio, and A. Varadarajan, 2022: Ocean Surface Salinity Response to Atmospheric River Precipitation in the California Current System. Ocean Surface Salinity Response to Atmospheric River Precipitation in the California Current System, 52, 1867–1885, https://doi.org/10.1175/jpo-d-21-0272.1.

Hogan, A., P. Hitzler, and K. Janowicz, 2016: Linked Dataset description papers at the Semantic Web journal: A critical assessment. SW, 7, 105–116, https://doi.org/10.3233/sw-160216.

Hsu, C. ‐T., T. Matsuo, A. Maute, R. Stoneback, and C. ‐P. Lien, 2021: Data‐Driven Ensemble Modeling of Equatorial Ionospheric Electrodynamics: A Case Study During a Minor Storm Period Under Solar Minimum Conditions. JGR Space Physics, 126, https://doi.org/10.1029/2020ja028539.

Hsu, L., B. McElroy, R. L. Martin, and W. Kim, 2013: Building a Sediment Experimentalist Network (SEN): sharing best practices for experimental methods and data management. TSR, 11, 9–12, https://doi.org/10.2110/sedred.2013.4.9.

——, R. L. Martin, B. McElroy, K. Litwin-Miller, and W. Kim, 2015: Data management, sharing, and reuse in experimental geomorphology: Challenges, strategies, and scientific opportunities. Geomorphology, 244, 180–189, https://doi.org/10.1016/j.geomorph.2015.03.039.

Hu, W., and G. Cervone, 2019: Dynamically Optimized Unstructured Grid (DOUG) for Analog Ensemble of numerical weather predictions using evolutionary algorithms. Computers & Geosciences, 133, 104299, https://doi.org/10.1016/j.cageo.2019.07.003.

——, D. Del Vento, and S. Su, 2017: Proceedings of the 2020 Improving Scientific Software Conference. Proceedings of the 2020 Improving Scientific Software Conference, https://doi.org/10.5065/P2JJ-9878.

——, G. Cervone, A. Merzky, M. Turilli, and S. Jha, 2022: A new hourly dataset for photovoltaic energy production for the continental USA. Data in Brief, 40, 107824, https://doi.org/10.1016/j.dib.2022.107824.

Hulbert, C., B. Rouet-Leduc, P. A. Johnson, C. X. Ren, J. Rivière, D. C. Bolton, and C. Marone, 2018: Similarity of fast and slow earthquakes illuminated by machine learning. Nature Geosci, 12, 69–74, https://doi.org/10.1038/s41561-018-0272-8.

Husson, J. M., and S. E. Peters, 2017: Atmospheric oxygenation driven by unsteady growth of the continental sedimentary reservoir. Earth and Planetary Science Letters, 460, 68–75, https://doi.org/10.1016/j.epsl.2016.12.012.

Illarionov, E., A. Kosovichev, and A. Tlatov, 2020: Machine-learning Approach to Identification of Coronal Holes in Solar Disk Images and Synoptic Maps. ApJ, 903, 115, https://doi.org/10.3847/1538-4357/abb94d.

Janowicz, K., and Coauthors, 2016: Moon Landing or Safari? A Study of Systematic Errors and Their Causes in Geographic Linked Data. Moon Landing or Safari? A Study of Systematic Errors and Their Causes in Geographic Linked Data, 275–290, https://doi.org/10.1007/978-3-319-45738-3_18.

Jenkins, C., 2018: Sediment Accumulation Rates For the Mississippi Delta Region: a Time-interval Synthesis. Sediment Accumulation Rates For the Mississippi Delta Region: a Time-interval Synthesis, 88, 301–309, https://doi.org/10.2110/jsr.2018.15.

Jia, X., and Coauthors, 2021: Physics-Guided Machine Learning from Simulation Data: An Application in Modeling Lake and River Systems. Physics-Guided Machine Learning from Simulation Data: An Application in Modeling Lake and River Systems, https://doi.org/10.1109/icdm51629.2021.00037.

Jiang, H., J. Wang, C. Liu, J. Jing, H. Liu, J. T. L. Wang, and H. Wang, 2020: Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning. ApJS, 250, 5, https://doi.org/10.3847/1538-4365/aba4aa.

Jiang, P., M. Elag, P. Kumar, S. D. Peckham, L. Marini, and L. Rui, 2017: A service-oriented architecture for coupling web service models using the Basic Model Interface (BMI). Environmental Modelling & Software, 92, 107–118, https://doi.org/10.1016/j.envsoft.2017.01.021.

Jiang, Y., and Coauthors, 2018: A Smart Web-Based Geospatial Data Discovery System with Oceanographic Data as an Example. IJGI, 7, 62, https://doi.org/10.3390/ijgi7020062.

Kadlec, J., B. StClair, D. P. Ames, and R. A. Gill, 2015: WaterML R package for managing ecological experiment data on a CUAHSI HydroServer. Ecological Informatics, 28, 19–28, https://doi.org/10.1016/j.ecoinf.2015.05.002.

——, A. W. Miller, and D. P. Ames, 2016: Extracting Snow Cover Time Series Data from Open Access Web Mapping Tile Services. J Am Water Resour Assoc, 52, 916–932, https://doi.org/10.1111/1752-1688.12387.

Karnauskas, K. B., and D. Giglio, 2022: Argo Reveals the Scales and Provenance of Equatorial Island Upwelling Systems. Geophysical Research Letters, 49, https://doi.org/10.1029/2022gl098744.

Kelbert, A., 2014: Science and Cyberinfrastructure: The Chicken and Egg Problem. Eos Trans. AGU, 95, 458–459, https://doi.org/10.1002/2014eo490006.

Kenigsberg, A. R., J. Rivière, C. Marone, and D. M. Saffer, 2020: Evolution of Elastic and Mechanical Properties During Fault Shear: The Roles of Clay Content, Fabric Development, and Porosity. J. Geophys. Res. Solid Earth, 125, https://doi.org/10.1029/2019jb018612.

Kerkez, B., and Coauthors, 2016: Cloud Hosted Real‐time Data Services for the Geosciences (CHORDS). Geosci. Data J., 3, 4–8, https://doi.org/10.1002/gdj3.36.

Khalsa, S. J. S., C. A. Mattmann, and R. Duerr, 2017: Deep web crawling for insights from polar data. Deep web crawling for insights from polar data, https://doi.org/10.1109/igarss.2017.8126974.

Khider, D., and Coauthors, 2019: PaCTS 1.0: A Crowdsourced Reporting Standard for Paleoclimate Data. Paleoceanography and Paleoclimatology, 34, 1570–1596, https://doi.org/10.1029/2019pa003632.

Kosovichev, A., 2021: Intelligent Databases and Machine-Learning Analysis Tools for Heliophysics. Intelligent Databases and Machine-Learning Analysis Tools for Heliophysics, https://doi.org/10.6084/M9.FIGSHARE.14848713.V1.

Krisnadhi, A., and Coauthors, 2015: The GeoLink Modular Oceanography Ontology. The GeoLink Modular Oceanography Ontology, 301–309, https://doi.org/10.1007/978-3-319-25010-6_19.

Kumar, P., 2015: Hydrocomplexity: Addressing water security and emergent environmental risks. Water Resour. Res., 51, 5827–5838, https://doi.org/10.1002/2015wr017342.

Kuo, K.-S., A. Oloso, K. Doan, T. L. Clune, and H. Yu, 2016: Implications of data placement strategy to Big Data technologies based on shared-nothing architecture for geosciences. Implications of data placement strategy to Big Data technologies based on shared-nothing architecture for geosciences, https://doi.org/10.1109/igarss.2016.7730983.

——, Y. Pan, F. Zhu, J. Wang, M. L. Rilee, and H. Yu, 2018: A Big Earth Data Platform Exploiting Transparent Multimodal Parallelization. A Big Earth Data Platform Exploiting Transparent Multimodal Parallelization, https://doi.org/10.1109/igarss.2018.8518304.

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