39 Hints to Facilitate the Use of Semantics for Data on Agriculture and Nutrition
References
- AgroPortal, release 2.0, Spet. 2020.
- Aubin, S, Caracciolo, C, Keizer, J and RDA Agrisemantics, WG. 2017a. Agrisemantics: vision for an infrastructure for semantic-based interoperability of agricultural data. DOI: 10.7490/f1000research.1114967.1
- Aubin, S, Caracciolo, C and Whitehead, B. 2018. Uses and Needs of Semantic Resources in the Area of Agriculture. RDA Agrisemantics Working Group. DOI: 10.5281/zenodo.3700849
- Aubin, S, Caracciolo, C and Zervas, P. 2017b. Landscaping the Use of Semantics to Enhance the Interoperability of Agricultural Data. RDA Agrisemantics Working Group. DOI: 10.5281/zenodo.3697332
- Baker, T, Caracciolo, C, Doroszenko, A, Finch, L, Suominen, O and Suri, S. 2017. The Global Agricultural Concept Scheme and Agrisemantics. International Conference on Dublin Core and Metadata Applications 0, 14–15.
- Caliper. [WWW Document], n.d. URL
http://stats-class.fao.uniroma2.it/caliper/ (accessed 1.27.20). - Caracciolo, C, Aubin, S and Whitehead, B. 2019a. The Agrisemantics recommendations to improve data interoperability in agriculture. F1000Research 8. DOI: 10.7490/f1000research.1117569.1
- Caracciolo, C, Aubin, S, Whitehead, B and Zervas, P. 2019b.
Semantics for Data in Agriculture: A Community-Based Wish List . In: Garoufallou, E, Sartori, F, Siatri, R and Zervas, M (Eds.), Metadata and Semantic Research. MTSR 2018. Communications in Computer and Information Science. Cham: Springer, pp. 340–345. DOI: 10.1007/978-3-030-14401-2_32 - CIAT. n.d. CGIAR BIG DATA Platform [WWW Document]. CGIAR Platform for Big Data in Agriculture. URL
https://bigdata.cgiar.org/ (accessed 4.15.20). - Crosson, P, Shalloo, L, O’Brien, D, Lanigan, GJ, Foley, PA, Boland, TM and Kenny, DA. 2011. A review of whole farm systems models of greenhouse gas emissions from beef and dairy cattle production systems. Animal Feed Science and Technology Special Issue: Greenhouse Gases in Animal Agriculture – Finding a Balance between Food and Emissions 166–167, 29–45. DOI: 10.1016/j.anifeedsci.2011.04.001
- David, R, Mabile, L, Specht, A, Sarah, S, Thomsen, M, Yahia, M, Dollé, L, Jonquet, C, Bailo, D, Bravo, E, Gachet, S, Gunderman, H, Hollebecq, J-E, Ioannidis, V, Jacob, D, Le Bras, Y, Lerigoleur, E, Cambon-Thomsen, A and Research Data Alliance – SHAring Reward and Credit (SHARC) Interest Group, TRD. 2020. FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles. Data Science Journal, 19(1), p.32. DOI: 10.5334/dsj-2020-032
- GO FAIR. n.d. GO FAIR [WWW Document]. GO FAIR. URL
https://www.go-fair.org/ (accessed 1.27.20). - Harper, L, Campbell, J, Cannon, EKS, Jung, S, Poelchau, M, Walls, R, Andorf, C, Arnaud, E, Berardini, TZ, Birkett, C, Cannon, S, Carson, J, Condon, B, Cooper, L, Dunn, N, Elsik, CG, Farmer, A, Ficklin, SP, Grant, D, Grau, E, Herndon, N, Hu, Z-L, Humann, J, Jaiswal, P, Jonquet, C, Laporte, M-A, Larmande, P, Lazo, G, McCarthy, F, Menda, N, Mungall, CJ, Munoz-Torres, MC, Naithani, S, Nelson, R, Nesdill, D, Park, C, Reecy, J, Reiser, L, Sanderson, L-A, Sen, TZ, Staton, M, Subramaniam, S, Tello-Ruiz, MK, Unda, V, Unni, D, Wang, L, Ware, D, Wegrzyn, J, Williams, J, Woodhouse, M, Yu, J and Main, D. 2018. AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture. Database (Oxford) 2018. DOI: 10.1093/database/bay088
- Johnston, AE and Poulton, PR. 2018. The importance of long-term experiments in agriculture: their management to ensure continued crop production and soil fertility; the Rothamsted experience. Eur J Soil Sci, 69: 113–125. DOI: 10.1111/ejss.12521
- Jonquet, C, Toulet, A, Arnaud, E, Aubin, S, Dzalé Yeumo, E, Emonet, V, Graybeal, J, Laporte, M-A, Musen, MA, Pesce, V and Larmande, P. 2018a. AgroPortal: A vocabulary and ontology repository for agronomy. Computers and Electronics in Agriculture, 144: 126–143. DOI: 10.1016/j.compag.2017.10.012
- Jonquet, C, Toulet, A, Dutta, B and Emonet, V 2018b. Harnessing the Power of Unified Metadata in an Ontology Repository: The Case of AgroPortal. J Data Semant, 7: 191–221. DOI: 10.1007/s13740-018-0091-5
- Musker, R and Schaap, B. 2018. Global Open Data in Agriculture and Nutrition (GODAN) initiative partner network analysis. F1000Res, 7: 47. DOI: 10.12688/f1000research.13044.1
- Musker, R, Tumeo, J, Schaap, B and Parr, M. 2018. GODAN’s Impact 2014–2018 – Improving Agriculture, Food and Nutrition with Open Data. F1000Research 7. DOI: 10.12688/f1000research.13044.1
- Nelson, GC, Valin, H, Sands, RD, Havlík, P, Ahammad, H, Deryng, D, Elliott, J, Fujimori, S, Hasegawa, T, Heyhoe, E, Kyle, P, Von Lampe, M, Lotze-Campen, H, Mason d’Croz, D, van Meijl, H, van der Mensbrugghe, D, Müller, C, Popp, A, Robertson, R, Robinson, S, Schmid, E, Schmitz, C, Tabeau, A and Willenbockel, D. 2014. Climate change effects on agriculture: Economic responses to biophysical shocks. Proc Natl Acad Sci USA, 111: 3274–3279. DOI: 10.1073/pnas.1222465110
- Nguyen, Q-D, Roussey, C, Poveda-Villalón, M, de Vaulx, C and Chanet, J-P. 2020. Development Experience of a Context-Aware System for Smart Irrigation Using CASO and IRRIG Ontologies. Applied Sciences, 10: 1803. DOI: 10.3390/app10051803
- Noy, NF, Shah, NH, Whetzel, PL, Dai, B, Dorf, M, Griffith, N, Jonquet, C, Rubin, DL, Storey, M-A, Chute, CG and Musen, MA. 2009. BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res, 37, W170–W173. DOI: 10.1093/nar/gkp440
- Perryman, SAM, Castells-Brooke, NID, Glendining, MJ, Goulding, KWT, Hawkesford, MJ, Macdonald, AJ, Ostler, RJ, Poulton, PR, Rawlings, CJ, Scott, T and Verrier, PJ. 2018. The electronic Rothamsted Archive (e-RA), an online resource for data from the Rothamsted long-term experiments. Sci Data 5. DOI: 10.1038/sdata.2018.72
- Platform for Big Data in Agriculture. 2017. Building the alliance for an agricultural data revolution [WWW Document]. CGIAR Platform for Big Data in Agriculture\Year in review 2017. URL
https://bigdata.cgiar.org/year-in-review-2017/ (accessed 1.21.20). - Platform for Big Data in Agriculture. 2018. Decoding the data ecosystem [WWW Document]. CGIAR Platform for Big Data in Agriculture|Year in review 2018. URL
https://bigdata.cgiar.org/year-in-review-2018/ (accessed 1.21.20). - Poulton, P, Johnston, J, Macdonald, A, White, R and Powlson, D. 2018. Major limitations to achieving “4 per 1000” increases in soil organic carbon stock in temperate regions: Evidence from long-term experiments at Rothamsted Research, United Kingdom. Glob Chang Biol, 24, 2563–2584. DOI: 10.1111/gcb.14066
- RDA Agrisemantics, WG. 2019. 39 Hints to Facilitate the Use of Semantics for Data on Agriculture and Nutrition.
- Roussey, C, Stephan, B, Géraldine, A and Daniel, B. 2019. Weather Data Publication on the LOD using SOSA/SSN Ontology. Semantic Web Journal. DOI: 10.3233/SW-200375
- Schultes, E. 2019. The Yin-Yang of the Internet of FAIR Data and Services.
- Shannon, DK, Clay, DE and Kitchen, NR. 2020.
Precision Agriculture Basics . John Wiley & Sons. - Stellato, A, Rajbhandari, S, Turbati, A, Fiorelli, M, Caracciolo, C, Lorenzetti, T, Keizer, J and Pazienza, MT. 2015.
VocBench: A Web Application for Collaborative Development of Multilingual Thesauri . In: Gandon, F, Sabou, M, Sack, H, d’Amato, C, Cudré-Mauroux, P and Zimmermann, A (Eds.), The Semantic Web. Latest Advances and New Domains, Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 38–53. DOI: 10.1007/978-3-319-18818-8_3 - Villa, F, Bagstad, KJ, Voigt, B, Johnson, GW, Portela, R, Honzák, M and Batker, D. 2014. A Methodology for Adaptable and Robust Ecosystem Services Assessment. PLOS ONE, 9:
e91001 . DOI: 10.1371/journal.pone.0091001 - Villa, F, Balbi, S, Athanasiadis, IN and Caracciolo, C. 2017. Semantics for interoperability of distributed data and models: Foundations for better-connected information. F1000Res 6, 686. DOI: 10.12688/f1000research.11638.1
- VocBench [WWW Document]. n.d. URL
http://vocbench.uniroma2.it/ (accessed 5.7.20). - Whitehead, B. 2020. Agrisemantics [WWW Document]. URL
https://agrisemantics.org/ (accessed 1.27.20). - Wilkinson, MD, Dumontier, M, Aalbersberg, IjJ, Appleton, G, Axton, M, Baak, A, Blomberg, N, Boiten, J-W, Santos, LB. da S. Bourne, PE, Bouwman, J, Brookes, AJ, Clark, T, Crosas, M, Dillo, I, Dumon, O, Edmunds, S, Evelo, CT, Finkers, R, Gonzalez-Beltran, A, Gray, AJG, Groth, P, Goble, C, Grethe, JS, Heringa, J, Hoen, PAC’t, Hooft, R, Kuhn, T, Kok, R, Kok, J, Lusher, SJ, Martone, ME, Mons, A, Packer, AL, Persson, B, Rocca-Serra, P, Roos, M, van Schaik, R., Sansone, S-A, Schultes, E, Sengstag, T, Slater, T, Strawn, G, Swertz, MA, Thompson, M, Lei, J, van der Mulligen, E, van Velterop, J, Waagmeester, A, Wittenburg, P, Wolstencroft, K, Zhao, J and Mons, B. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 3: 1–9. DOI: 10.1038/sdata.2016.18
- Zeng, ML. 2008. Knowledge Organization Systems (KOS). KO, 35: 160–182. DOI: 10.5771/0943-7444-2008-2-3-160
DOI: https://doi.org/10.5334/dsj-2020-047 | Journal eISSN: 1683-1470
Language: English
Page range: 47 - 47
Submitted on: Jun 4, 2020
Accepted on: Nov 16, 2020
Published on: Dec 11, 2020
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
© 2020 Caterina Caracciolo, Sophie Aubin, Clement Jonquet, Emna Amdouni, Romain David, Leyla Garcia, Brandon Whitehead, Catherine Roussey, Armando Stellato, Ferdinando Villa, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.