Find research datasets worth reusing

AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: pAPSIM sorghum

This is model output from pAPSIM for sorghum 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

AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: pDSSAT wheat

This is model output from pDSSAT 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

AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: EPIC-IIASA maize

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

AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: pAPSIM wheat

This is model output from pAPSIM 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

opencc-by-4.0Oct 2018View details →

MarTREC Data Set for Report: Economic Impact of the GIWW on the States It Serves

Data set used for the report, "Economic Impact of the Gulf Intracoastal Waterway on the States It Serves".  The purpose of the report was to examine the economic impact of the GIWW on the five states it serves: Texas, Louisiana, Mississippi, Alabama, and Florida. This project involved several research tasks. First, researchers estimated the economic impact of the GIWW by looking specifically at the impact of the commodities using the GIWW in the relevant coastal counties in each of these five states. Once this task was completed, researchers then estimated the impact of the GIWW on other modes, modeling the possible adverse impacts that would result if the GIWW were to become permanently unavailable and shippers would instead have to use the next economically feasible transportation mode. Researchers examined the cost of shifting additional traffic to other modes of transportation.

opencc-by-4.0Oct 2018View details →

data_set_ANN + results

Excel sheets:   IN - input (independent) variable values for all 64 data points   variable ID 1 = frequency of the 1st free vibration mode (Hz)   variable ID 3 = frequency of the 2nd free vibration mode (Hz)   variable ID 3 = frequency of the 3rd free vibration mode (Hz)   TARGET - target (dependent) variable values for all 64 data points   variable ID 1 = damage location (m) = distance from beam edge   TARGET vs OUTPUT - target (expected) vs. output (yielded by the proposed ANN) values     Check reference below (to be added when the paper is published) <a href="https://www.researchgate.net/publication/328870831_12_Neural_Networks_-_Structural_Damage_-_Localization">https://www.researchgate.net/publication/328870831_12_Neural_Networks_-_Structural_Damage_-_Localization</a>

opencc-by-4.0Oct 2018View details →

Data sets for PF MX beamline users' workshop 2018

Data sets for PF MX beamline users' workshop 2018

opencc-by-4.0Jan 2019View details →

Updated example thaumatin data set from Diamond Light Source VMXi

Example data set from the VMXi beamline at Diamond Light Source recorded in situ from a thaumatin crystal with processing results:   <pre>For AUTOMATIC/DEFAULT/NATIVE Overall Low High High resolution limit 1.85 5.02 1.85 Low resolution limit 50.49 50.50 1.88 Completeness 68.0 100.0 2.7 Multiplicity 3.3 3.9 1.0 I/sigma 12.9 24.8 1.4 Rmerge(I) 0.056 0.031 0.386 Rmerge(I+/-) 0.047 0.027 0.000 Rmeas(I) 0.065 0.035 0.545 Rmeas(I+/-) 0.061 0.035 0.000 Rpim(I) 0.032 0.017 0.386 Rpim(I+/-) 0.038 0.022 0.000 CC half 0.998 0.999 0.000 Wilson B factor 20.100 Anomalous completeness 58.0 99.8 0.1 Anomalous multiplicity 1.9 2.4 1.0 Anomalous correlation -0.015 -0.107 0.000 Anomalous slope 0.928 Total observations 53105 5240 32 Total unique 15898 1349 31 Assuming spacegroup: P 41 21 2 Other likely alternatives are: P 43 21 2 Unit cell (with estimated std devs): 58.6330(3) 58.6330(3) 151.4573(10) 90.0 90.0 90.0 </pre>

opencc-by-4.0Jan 2019View details →

Data set associated with the paper "Implementation of the Vector Vorticity Dynamical Core on Cubed Sphere for Use in the Quasi-3-D Multiscale Modeling Framework"

New data set associated with the revision of the paper "Development of a Global Quasi-3-D Multiscale Modeling Framework:  I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component" The title of the paper has been changed to "Implementation of the Vector Vorticity Dynamical Core on Cubed Sphere for Use in the Quasi-3-D Multiscale Modeling Framework" New simulated data set of the advection test is in the folder ADVEC_NEW; New simulated data set of the barotropic instability test is in the folder BARO_NEW; New simulated data set of the baroclinic instability test is in the folder BCL_NEW

SeSaMe: A Data Set of Semantically Similar Java Methods

This is the data set presented in the paper Kamp, M., Kreutzer P., Philippsen M.: SeSaMe: A Data Set of Semantically Similar Java Methods. 16th International Conference on Mining Software Repositories (MSR 2019), Montreal, QC, Canada. 2019

Example treatment of dynamic shadows in Eiger data set

Following discussion in    https://github.com/HDRMX/NXmx/wiki/Treatment-of-(Dynamic)-Shadows-in-Eiger-Data-Sets   example Eiger data set with shadow mask added under /entry/shadow/dynamic_mask - uploading for discussion purposes only, data set may be revised in future. Clearly any and all are welcome to look at / inspect the files and provide commentary.    N.B. the shadow model has not been optimised.

Contextual maritime data set (RDF triples)

This data set is the RDF conversion w.r.t. the datAcron ontology, of the contextual maritime data available at https://zenodo.org/record/1167595 . It has been generated by the RDF-Gen method on the data sets describing sea ports (World Port Index, Ports of Brittany, SeaDataNet fishing ports) and protected regions (fishing areas, fishing interdiction, Natura2000).

Data Set: Evaluation of Domain Randomization Techniques for Transfer Learning - Part 0

Transfer Learning Data Set: The total data set contains 1.44m synthetic images and 10k real-world images of size 299x224. Due to file size limitation, the data set is split into two sets. This data set (part 0: DOI 10.5281/zenodo.2581311) contains the real-world images and the synthetic images of perspective 00. The second part (part 1: DOI 10.5281/zenodo.2581469) contains perspective 01 and 10 of the synthetic images. 1. 10k real world images     the filename labels the image:         -first 6 digits represent the id.         -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp         -10. digit is empty (reserved for 2nd perspective)         -12. digit labels the graspbox: 0 -> green box, 1 -> yellow box         -14. digit labels the distractors : 0 -> no distractors, 1 -> distractors         -c in the end stands for acolor image, d is reserved for depth (not in use).      2. 1.44m synthetic images     the folder labels the enabled technique:         -first 2 digits label the perspective:  00 -> standard, 01 -> shake, 10 -> random         -3. digit labels the graspbox: 0 -> defaul green box, 1 -> random box         -4. digit labels the distractors: 0 -> no distractors, 1 -> distractors         -5. digit labels the lighting: 0 -> default lighting, 1 -> random lighting         -6. digit labels the mesh randomization: 0 -> default mesh color, 1 -> random mesh color              the filename consists of 3 parts:         -first 6 digits represent the id.         -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp         -10.-15. digit equals the folder name and represents the enabled technique         -c in the end stands for a color image, d is reserved for depth (not in use).

Data Set: Evaluation of Domain Randomization Techniques for Transfer Learning - Part 1

Transfer Learning Data Set: The total data set contains 1.44m synthetic images and 10k real-world images of size 299x224. Due to file size limitation, the data set is split into two sets. The first part (part 0: DOI 10.5281/zenodo.2581311) contains the real-world images and the synthetic images of perspective 00. This data set (part 1: DOI 10.5281/zenodo.2581469) contains perspective 01 and 10 of the synthetic images. 1. 10k real world images     the filename labels the image:         -first 6 digits represent the id.         -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp         -10. digit is empty (reserved for 2nd perspective)         -12. digit labels the graspbox: 0 -> green box, 1 -> yellow box         -14. digit labels the distractors : 0 -> no distractors, 1 -> distractors         -c in the end stands for acolor image, d is reserved for depth (not in use).      2. 1.44m synthetic images     the folder labels the enabled technique:         -first 2 digits label the perspective:  00 -> standard, 01 -> shake, 10 -> random         -3. digit labels the graspbox: 0 -> defaul green box, 1 -> random box         -4. digit labels the distractors: 0 -> no distractors, 1 -> distractors         -5. digit labels the lighting: 0 -> default lighting, 1 -> random lighting         -6. digit labels the mesh randomization: 0 -> default mesh color, 1 -> random mesh color              the filename consists of 3 parts:         -first 6 digits represent the id.         -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp         -10.-15. digit equals the folder name and represents the enabled technique         -c in the end stands for a color image, d is reserved for depth (not in use).

Model sets and data used in the preprint "Evaluating functional dispersal and its eco-epidemiological implications in a nest ectoparasite"

Model sets and data used in the preprint "Evaluating functional dispersal and its eco-epidemiological implications in a nest ectoparasite", reviewed and recommended by Peer Community In Ecology (https://dx.doi.org/10.24072/pci.ecology.100013). See preprint and supplementary materials.

Link Discovery on AIS and Contextual data sets

This data set contains the links discovered between AIS synopses provided in https://zenodo.org/record/2576152 and contextual data sets available at https://zenodo.org/record/2576584 . Specifically, the detected relations and the contextual data sets used are: a) C1 ports of Brittany, World Port Index and SeaDataNet fishing ports for proximity relation "nearto", stored in AIS_nearto_ports.ttl.7z b) C4 fishing areas (European Commission) for "within" relation stored in AIS_within_fishingAreas.ttl.7z c) C5 fishing interdiction for "within" relation stored in AIS_within_FishingConstraints.ttl.7z d) C5 Natura2000 for "within" relation stored in AIS_within_natura2000.ttl.7z Please notice that the attached data sets contain only the detected links. Description of the resources is provided in the corresponding TTL files.

On-farm study reveals positive relationship between gas transport capacity and organic carbon content in arable soil (Data set)

Data used for "On-farm study reveals positive relationship between gas transport capacity and organic carbon content in arable soil" by Colombi T, Walder F, Büchi L, Sommer M, Liu K, Six J, van der Heijden M, Charles R and Keller T. (2019). SOIL. 5, 91-105, https://doi.org/10.5194/soil-5-91-2019. .txt file "MetaInformation_On-farm study reveals positive relationship between gas transport capacity and organic carbon content in arable soil" contains all necessary meta-information

Training/evaluation data sets and databases for the operation of bio-answerfinder biomedical question answering system

A zip file containing training/evaluation data sets for the bio-answerfinder biomedical question answering system training and evaluation. The zip file also contains SQLite databases for named entity lookups, morphology, nominalizations, acronyms, PubMED trained GLoVe word/phrase embeddings and vocabulary with document frequencies and SciCrunch ontology data for named entities such as proteins, anatomical structures,

Deformed iron data set

This is a reduced (software binned) data set. The full resolution version of this data set is published here: 10.5281/zenodo.1214829 Data from Electron Backscatter Diffraction analysis for a small (83 x 110) point map captured using a Bruker eFlash HR (1st generation) with full pattern resolution on a FEI Quanta instrument. The orientation data can be loaded using MTEX 5.0.3 (<a href="http://mtex-toolbox.github.io/">http://mtex-toolbox.github.io/</a>). The data is released to facilitate the development of new EBSD analysis methodologies, including AstroEBSD (<a href="https://github.com/benjaminbritton/AstroEBSD/">https://github.com/benjaminbritton/AstroEBSD/</a>) which has been developed by the Experimental Micromechanics Research Group (<a href="http://www.expmicromech.com/">http://www.expmicromech.com</a>) & the Oxford Micromechanics group (<a href="http://users.ox.ac.uk/~ajw/">http://users.ox.ac.uk/~ajw/</a>). The data is from a lightly deformed sample of interstitial free steel (Ferrite). Orientation analysis was performed using eSprit 2.1 and this is contained within the h5 file. Figures from this data set are provided to illustrate the correct representation of the data. The x axis points right to left, the y axis points top to bottom, and the z axis is out of the page (as per conventions described in <a href="http://dx.doi.org/10.1016/j.matchar.2016.04.008">http://dx.doi.org/10.1016/j.matchar.2016.04.008</a>). Data has been captured with a 0.15 um step size. This data was collected within the Harvey Flower EM Suite within the Department of Materials, Imperial College London. The equipment was funded under the Shell-Imperial Advanced Interfaces in Materials Science University Technology Center. Please contact Dr Ben Britton if you have any queries or require further information (b.britton@imperial.ac.uk).