cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.1403150
title: Vehicle Routing Problem With Drones Instances

description: The following instances are originally presented in the (soon to be published) paper <em>An Adaptive Large Neighborhood Search Metaheuristic for the Vehicle Routing Problem with Drones</em>, written by David Sacramento Lechado, David Pisinger and Stefan Røpke.

The data correspond to 112 instances corresponding to different scenarios for the Vehicle Routing Problem with Drones. Each instance is named <strong>n.m.t</strong>, where <strong>n</strong> is the number of customers in the scenario, <strong>m</strong> is the dimension of the grid, and <strong>t</strong> is the generic name of the scenario. Moreover, the data additionally contains 10 clustered instances, named <strong>n.m.c.t</strong>, where <strong>c</strong> refers to the cluster-feature of the instance.

The first line of each instance file indicates the number of customers in the specific instance. The second line is the header for the characteristics of each customer, where it is written <em>Coordinate X, Coordinate Y</em> and <em>Demand. </em>The following provides the previous information for each customer in the instance.

Finally, there is an extra file, named <strong>RouteData,</strong> which provides information about the value of the parameters in the main configuration of the problem. These parameters are:


	<strong>TruckSpeed: </strong>Speed in mpm (miles per minute) of the trucks.
	<strong>DroneSpeed: </strong>Speed in mpm of the drones.
	<strong>TruckCapacity: </strong>Maximum capacity of the trucks.
	<strong>DroneCapacity:</strong> Maximum allowed weight a drone can carry.
	<strong>ServiceTimeTruck:</strong> Required service time for a truck to service a customer.
	<strong>ServiceTimeDrone:</strong> Required service time for a drone to service a customer.
	<strong>Endurance: </strong>Maximum flight endurance of the battery of the drone.
	<strong>MaximumDriveTime: </strong>Maximum duration time of the routes.
	<strong>LaunchTime: </strong>Required time for launching a drone.
	<strong>RecoveryTime: </strong>Required time for recovering a drone.
	<strong>CostFactor:</strong> Corresponding parameter for computing the cost for a truck for traversing arc (i,j), given by fuel price (euro/liter), consumption rate (liter/km) and miles converter (km/miles).
	<strong>DroneFactor: </strong>Corresponding parameter for computing the cost for a drone for traversing arc (i,j) with respect to the truck cost.
DOI: 10.5281/zenodo.1402921
title: Bacterial Communities And Toxicity Of Rotting Fish Carcases On Sandy Beaches

description: Bacterial communities and toxicity of rotting fish carcases on sandy beaches
DOI: 10.5281/zenodo.1402816
title: Sensitivity Datasets - Leveraging Implicit Knowledge in Neural Networks for Functional Dissection and Engineering of Proteins

description: <strong>Leveraging Implicit Knowledge in Neural Networks for Functional Dissection and Engineering of Proteins</strong>

The Sensitivity datasets cover more than 800 proteins and are structured as follows. The sensitivity values are the mean of four DeeProtein replicates.

It is uploaded as tar.gz. and contains one directory.

File names contain the PDB<sup>1</sup> identifier and the respective chain identifier. 

The sequences and secondary structure information were downloaded from the RCSB Protein Databank and are available here: https://cdn.rcsb.org/etl/kabschSander/ss_dis.txt.gz This URL can be found with some explanation at http://www.rcsb.org/pdb/static.do?p=download/http/index.html

The secondary structure annotation relies on the DSSP Algorithm by Kabsch and Sander<sup>2</sup>.

 

<strong>The files are tab-separated and contain the following columns:</strong>


	<strong>Pos</strong> Position in the sequence, starting from zero
	<strong>AA</strong> Amino acid in that position
	<strong>sec</strong> Secondary structure as annotated in the RCSB Protein Databank
	<strong>dis</strong> if a region has not been experimentally observed (sometimes explains mismatches with crystal structures)
	<strong>GO:_______</strong> Sensitivity for the GO term


<strong>References</strong>


	The Protein Data Bank H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, P.E. Bourne (2000) Nucleic Acids Research, 28: 235-242. doi:10.1093/nar/28.1.235
	Kabsch, W. & Sander, C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577-2637, doi:10.1002/bip.360221211 (1983).
description: Authorship Statement
The following are members of the iGEM (international genetically engineered machines) Team Heidelberg 2017: Lukas Adam, Thore Bürgel, Roland Eils, Catharina Gandor, Daniel Heid, Mareike Daniela Hoffmann, Stefan Holderbach, Michael Jendrusch, Marita Klein, Irina Lehmann, Jan Mathony, Dominik Niopek, Pauline Pfuderer, Lukas Platz, Moritz Przybilla, Carolin Schmelas, Max Schwendemann, Julius Upmeier zu Belzen, Max Waldhauer (all from Germany).

Acknowledgements
This work was funded by the Klaus-Tschira foundation, the German Research Council (DFG) and the Federal Ministry of Education and Research (BMBF). We thank Jürgen Quittek and Matthias Niepert (both NEC, Heidelberg), Thomas Wollmann (IPMB, BioQuant and German Cancer Research Center (DKFZ), Heidelberg) for helpful discussions and Marc Hemberger (BioQuant, Heidelberg) for support with IT and GPU cluster use.
description: {"references": ["The Protein Data Bank H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, P.E. Bourne (2000) Nucleic Acids Research, 28: 235-242. doi:10.1093/nar/28.1.235", "Kabsch, W. & Sander, C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577-2637, doi:10.1002/bip.360221211 (1983)."]}
DOI: 10.5281/zenodo.1403295
title: China'S Transboundary Water Resources Estimation Using Machine Learning Approaches

description: China's transboundary water resources estimated using machine learning models (random forest, gradient boosting, and stacking).
DOI: 10.5281/zenodo.1403296
title: China'S Transboundary Water Resources Estimation Using Machine Learning Approaches

description: China's transboundary water resources estimated using machine learning models (random forest, gradient boosting, and stacking).
DOI: 10.5517/ccdc.csd.cc1yycy6
title: CCDC 1815699: Experimental Crystal Structure Determination

description: Related Article: Kouichi Watanabe, Yusuke Miyazaki, Masataka Okubo, Biao Zhou, Hiroaki Tsuji, Motoi Kawatsura|2018|Org.Lett.|20|5448|doi:10.1021/acs.orglett.8b02325
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2010ps
title: CCDC 1848024: Experimental Crystal Structure Determination

description: Related Article: Timothy G. Carroll, Rachel Garwick, Guang Wu, Gabriel Ménard|2018|Inorg.Chem.|57|11543|doi:10.1021/acs.inorgchem.8b01585
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2010rv
title: CCDC 1848026: Experimental Crystal Structure Determination

description: Related Article: Timothy G. Carroll, Rachel Garwick, Guang Wu, Gabriel Ménard|2018|Inorg.Chem.|57|11543|doi:10.1021/acs.inorgchem.8b01585
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2010sw
title: CCDC 1848027: Experimental Crystal Structure Determination

description: Related Article: Timothy G. Carroll, Rachel Garwick, Guang Wu, Gabriel Ménard|2018|Inorg.Chem.|57|11543|doi:10.1021/acs.inorgchem.8b01585
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2010vy
title: CCDC 1848029: Experimental Crystal Structure Determination

description: Related Article: Timothy G. Carroll, Rachel Garwick, Guang Wu, Gabriel Ménard|2018|Inorg.Chem.|57|11543|doi:10.1021/acs.inorgchem.8b01585
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc206njf
title: CCDC 1853444: Experimental Crystal Structure Determination

description: Related Article: Shuyue Zhang, James E. Taylor, Alexandra M. Z. Slawin, Andrew D. Smith|2018|Org.Lett.|20|5482|doi:10.1021/acs.orglett.8b02423
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc206nkg
title: CCDC 1853445: Experimental Crystal Structure Determination

description: Related Article: Shuyue Zhang, James E. Taylor, Alexandra M. Z. Slawin, Andrew D. Smith|2018|Org.Lett.|20|5482|doi:10.1021/acs.orglett.8b02423
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc206nlh
title: CCDC 1853446: Experimental Crystal Structure Determination

description: Related Article: Shuyue Zhang, James E. Taylor, Alexandra M. Z. Slawin, Andrew D. Smith|2018|Org.Lett.|20|5482|doi:10.1021/acs.orglett.8b02423
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc20khmq
title: CCDC 1863863: Experimental Crystal Structure Determination

description: Related Article: Seth N. Brown|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1pdt1y
title: CCDC 1561440: Experimental Crystal Structure Determination

description: Related Article: Li Dai, Lili Lin, Jianfeng Zheng, Dong Zhang, Xiaohua Liu, Xiaoming Feng|2018|Org.Lett.|20|5314|doi:10.1021/acs.orglett.8b02239
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1pknsp
title: CCDC 1566113: Experimental Crystal Structure Determination

description: Related Article: Devesh M. Sawant, Shivani Sharma, Ramdas S. Pathare, Gaurav Joshi, Sourav Kalra, Sukanya Sukanya, Antim K. Maurya, Ramesh K. Metre, Vijai K. Agnihotri, Shahnawaz Khan, Raj Kumar, R. T. Pardasani|2018|Chem.Commun.|54|11530|doi:10.1039/C8CC05845H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1pkv7b
title: CCDC 1566282: Experimental Crystal Structure Determination

description: Related Article: Devesh M. Sawant, Shivani Sharma, Ramdas S. Pathare, Gaurav Joshi, Sourav Kalra, Sukanya Sukanya, Antim K. Maurya, Ramesh K. Metre, Vijai K. Agnihotri, Shahnawaz Khan, Raj Kumar, R. T. Pardasani|2018|Chem.Commun.|54|11530|doi:10.1039/C8CC05845H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1pkv8c
title: CCDC 1566283: Experimental Crystal Structure Determination

description: Related Article: Devesh M. Sawant, Shivani Sharma, Ramdas S. Pathare, Gaurav Joshi, Sourav Kalra, Sukanya Sukanya, Antim K. Maurya, Ramesh K. Metre, Vijai K. Agnihotri, Shahnawaz Khan, Raj Kumar, R. T. Pardasani|2018|Chem.Commun.|54|11530|doi:10.1039/C8CC05845H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1pkv9d
title: CCDC 1566284: Experimental Crystal Structure Determination

description: Related Article: Devesh M. Sawant, Shivani Sharma, Ramdas S. Pathare, Gaurav Joshi, Sourav Kalra, Sukanya Sukanya, Antim K. Maurya, Ramesh K. Metre, Vijai K. Agnihotri, Shahnawaz Khan, Raj Kumar, R. T. Pardasani|2018|Chem.Commun.|54|11530|doi:10.1039/C8CC05845H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1pkvbf
title: CCDC 1566285: Experimental Crystal Structure Determination

description: Related Article: Devesh M. Sawant, Shivani Sharma, Ramdas S. Pathare, Gaurav Joshi, Sourav Kalra, Sukanya Sukanya, Antim K. Maurya, Ramesh K. Metre, Vijai K. Agnihotri, Shahnawaz Khan, Raj Kumar, R. T. Pardasani|2018|Chem.Commun.|54|11530|doi:10.1039/C8CC05845H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc20kbjg
title: CCDC 1863705: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Hendrik Oeynhausen, Heinrich Wamhoff|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc20kblj
title: CCDC 1863707: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, U. Eisfeld, Heinrich Wamhoff|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6073/pasta/f3c90e16cafefc6daed9fdcb97cf7bf9
title: Plum Island LTER phytoplankton identification using HPLC and Chem Taxonomy along transects in the Plum Island Sound estuary, Massachusetts

description: Water column samples are collected along an estuarine salinity gradient as part of our monitoring surveys of the Parker River estuary each spring and late summer (typically high vs low freshwater input). Samples are filtered, and stored frozen for later pigment analyses by HPLC. Pigment data are then analyzed by CHEMTAX, calibrated to a matrix of pigment ratios based on taxonomy and enumeration of selected subsamples by microspcopy. Data are presented in terms of chlorophyll a concentrations partitionaed among the major phytoplankton groups as determined by CHEMTAX.
For 2003-2006, sampling stations along the Plum Island Sound-Parker River were at fixed geographic locations at specific "Bends" in the river. In 2008, we began sampling the water column in salinity space rather than at specific geographic locations along the river. This sampling approach was adopted in order to follow particular water masses in this macrotidal estuary. In practical terms, it means that sampling locations, or stations, are not static. Therefore, we created four bounding boxes to encompass three regions of the river plus the sound, and mapped the 11 sampling locations (latitude and longitude are logged at each station) from each transect along the mainstem of the estuary, so each station may be located within one of these bounding boxes as well as in salinity space. When needed, station coordinates have also been used to map the specific location of a station from a specific transect.
Bounding boxes :
Sound (Plum Island Sound; EST-PR-SoundBND) 0 to ~9.5 km, proceeding upstream from the mouth of the sound;
Lower Parker River (EST-PR-LowerParkerBND) , ~9.5 - 14.5 km;
Middle Parker River (EST-PRMiddleParkerBND), ~14.5 - 18.75 km,
Upper Parker River (EST-PR-UpperParker BND)., ~18.75 to 24.25 km (the Parker R. Dam).
DOI: 10.5281/zenodo.1403708
title: Rcs-Image Dataset

description: Note: There is a single zip file of the project available under the name "RCS Image Dataset.zip "

A synthetic dataset for object detection, semantic segmentation and depth recognition was created using images from a virtual environment in Unreal Engine. This dataset that could be used to retrain neural networks on virtual aerial images, for object detection, segmentation and depth planning.

There is ground truth for full pixel level semantic segmentation and object detction by way of bounding box coordinates in .exif files. The main  labels present in the dataset are train truck and gas cylinders  The size of the data is currently more than 10GB, with 1000 aerial images, from 100 different waypoints at 10 different heights. For each raw image, there is also an associated depth image, a segmented image, object ground truths with bounding box, and the metadata .exif file.

If you found this dataset useful for your reserach, please cite as,

<pre>@article{smyth2018virtual,
  title={A Virtual Environment with Multi-Robot Navigation, Analytics, and Decision Support for Critical Incident Investigation},
  author={Smyth, David L and Fennell, James and Abinesh, Sai and Karimi, Nazli B and Glavin, Frank G and Ullah, Ihsan and Drury, Brett and Madden, Michael G},
  journal={arXiv preprint arXiv:1806.04497},
  year={2018}
}</pre>
description: Note: There is a single zip file of the project available under the name "RCS Image Dataset.zip "
description: {"references": ["Smyth et al, (2018 ). Smyth, D. L., Fennell, J., Abinesh, S., Karimi, N. B., Glavin, F. G., Ullah, I., ... & Madden, M. G. (2018). A Virtual Environment with Multi-Robot Navigation, Analytics, and Decision Support for Critical Incident Investigation. arXiv preprint arXiv:1806.04497."]}
DOI: 10.5281/zenodo.1403709
title: Rcs-Image Dataset

description: A synthetic dataset for object detection, semantic segmentation and depth recognition was created using images from a virtual environment in Unreal Engine. This dataset that could be used to retrain neural networks on virtual aerial images, for object detection, segmentation and depth planning.

There is ground truth for full pixel level semantic segmentation and object detction by way of bounding box coordinates in .exif files. The main  labels present in the dataset are train truck and gas cylinders  The size of the data is currently more than 10GB, with 1000 aerial images, from 100 different waypoints at 10 different heights. For each raw image, there is also an associated depth image, a segmented image, object ground truths with bounding box, and the metadata .exif file.

If you found this dataset useful for your reserach, please cite as,

<pre>@article{smyth2018virtual,
  title={A Virtual Environment with Multi-Robot Navigation, Analytics, and Decision Support for Critical Incident Investigation},
  author={Smyth, David L and Fennell, James and Abinesh, Sai and Karimi, Nazli B and Glavin, Frank G and Ullah, Ihsan and Drury, Brett and Madden, Michael G},
  journal={arXiv preprint arXiv:1806.04497},
  year={2018}
}</pre>
description: {"references": ["Smyth et al, (2018 ). Smyth, D. L., Fennell, J., Abinesh, S., Karimi, N. B., Glavin, F. G., Ullah, I., ... & Madden, M. G. (2018). A Virtual Environment with Multi-Robot Navigation, Analytics, and Decision Support for Critical Incident Investigation. arXiv preprint arXiv:1806.04497."]}
DOI: 10.5281/zenodo.1403163
title: Embeddings_Bridging

description: A general word representation resource for bridging

Related paper: Yufang Hou. 2018. A Deterministic Algorithm for Bridging Anaphora Resolution. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31–November 4, 2018

 

 

<br>
DOI: 10.5281/zenodo.1403164
title: Embeddings_Bridging

description: A general word representation resource for bridging

Related paper: Yufang Hou. 2018. A Deterministic Algorithm for Bridging Anaphora Resolution. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31–November 4, 2018

 

 

<br>