cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57745/kzuwod
title: RGBSCENE_2020-10-02_030.png
DOI: 10.57745/zpaaup
title: RGBVIEWFINDER_2020-10-02_030.png
DOI: 10.57745/ut2liy
title: 2020-10-02_031.png
DOI: 10.57745/0zjjtm
title: 2020-10-02_031.hdr
DOI: 10.57745/rrahfo
title: 2020-10-02_031.raw
DOI: 10.57745/b7simx
title: DARKREF_2020-10-02_031.hdr
DOI: 10.57745/dznbzk
title: DARKREF_2020-10-02_031.raw
DOI: 10.57745/ckhmpi
title: WHITEREF_2020-10-02_031.hdr
DOI: 10.57745/yskcih
title: WHITEREF_2020-10-02_031.raw
DOI: 10.57745/ybl0h4
title: 2020-10-02_031.xml
DOI: 10.57745/fwhkm3
title: REFLECTANCE_2020-10-02_031.dat
DOI: 10.57745/jj5twx
title: REFLECTANCE_2020-10-02_031.hdr
DOI: 10.57745/brlyue
title: REFLECTANCE_2020-10-02_031.png
DOI: 10.57745/pijwcr
title: RGBBACKGROUND_2020-10-02_031.png
DOI: 10.57745/qgf9p7
title: RGBSCENE_2020-10-02_031.png
DOI: 10.57745/g7mnau
title: RGBVIEWFINDER_2020-10-02_031.png
DOI: 10.57745/2trc5c
title: 2020-10-02_032.png
DOI: 10.57745/11o7ak
title: 2020-10-02_032.hdr
DOI: 10.57745/wdassi
title: 2020-10-02_032.raw
DOI: 10.57745/g8ymcc
title: DARKREF_2020-10-02_032.hdr
DOI: 10.57745/wjusfe
title: DARKREF_2020-10-02_032.raw
DOI: 10.57745/imufak
title: WHITEREF_2020-10-02_032.hdr
DOI: 10.57745/kcdau9
title: WHITEREF_2020-10-02_032.raw
DOI: 10.57745/w2jfhz
title: 2020-10-02_032.xml
DOI: 10.57745/spfvb2
title: REFLECTANCE_2020-10-02_032.dat
DOI: 10.57745/ep01jq
title: REFLECTANCE_2020-10-02_032.hdr
DOI: 10.57745/f9ha6k
title: REFLECTANCE_2020-10-02_032.png
DOI: 10.57745/u2wcno
title: RGBBACKGROUND_2020-10-02_032.png
DOI: 10.57745/7kvpqw
title: RGBSCENE_2020-10-02_032.png
DOI: 10.57745/4cgso8
title: RGBVIEWFINDER_2020-10-02_032.png
DOI: 10.57745/ghasse
title: 2020-10-02_033.png
DOI: 10.57745/o4psan
title: 2020-10-02_033.hdr
DOI: 10.57745/tcr8k3
title: 2020-10-02_033.raw
DOI: 10.57745/bkixxf
title: DARKREF_2020-10-02_033.hdr
DOI: 10.57745/cnkb38
title: DARKREF_2020-10-02_033.raw
DOI: 10.57745/pyfaks
title: WHITEREF_2020-10-02_033.hdr
DOI: 10.57745/2za2dd
title: WHITEREF_2020-10-02_033.raw
DOI: 10.57745/wwbwrf
title: 2020-10-02_033.xml
DOI: 10.57745/83jcho
title: REFLECTANCE_2020-10-02_033.dat
DOI: 10.57745/uiuoz1
title: REFLECTANCE_2020-10-02_033.hdr
DOI: 10.57745/bb0tym
title: REFLECTANCE_2020-10-02_033.png
DOI: 10.57745/gh20ya
title: RGBBACKGROUND_2020-10-02_033.png
DOI: 10.57745/1inp7n
title: RGBSCENE_2020-10-02_033.png
DOI: 10.57745/bbzsek
title: RGBVIEWFINDER_2020-10-02_033.png
DOI: 10.57745/ykxuh7
title: 2020-10-02_034.png
DOI: 10.57745/yrogc4
title: 2020-10-02_034.hdr
DOI: 10.57745/qtwulx
title: 2020-10-02_034.raw
DOI: 10.57745/jpbean
title: DARKREF_2020-10-02_034.hdr
DOI: 10.57745/k4xn7o
title: DARKREF_2020-10-02_034.raw
DOI: 10.57745/wum9f2
title: WHITEREF_2020-10-02_034.hdr
DOI: 10.57745/5gww1q
title: WHITEREF_2020-10-02_034.raw
DOI: 10.57745/eg2guf
title: 2020-10-02_034.xml
DOI: 10.57745/ubdu8n
title: REFLECTANCE_2020-10-02_034.dat
DOI: 10.57745/tzlywh
title: REFLECTANCE_2020-10-02_034.hdr
DOI: 10.57745/zzfw9d
title: REFLECTANCE_2020-10-02_034.png
DOI: 10.57745/xd0h4d
title: RGBBACKGROUND_2020-10-02_034.png
DOI: 10.57745/u9ueis
title: RGBSCENE_2020-10-02_034.png
DOI: 10.57745/ngtanm
title: RGBVIEWFINDER_2020-10-02_034.png
DOI: 10.57745/9pvhyx
title: description_variables.csv
DOI: 10.57745/q7krmi
title: README.txt
DOI: 10.57745/etjf4w
title: pc1_all.pdf
DOI: 10.57745/9yzrsj
title: pc2_all.pdf
DOI: 10.57745/xfytpn
title: pc3_all.pdf
DOI: 10.57745/p1y0ji
title: pc4_all.pdf
DOI: 10.57745/zltuys
title: sc12_all.pdf
DOI: 10.57745/ee8rif
title: sc34_all.pdf
DOI: 10.57745/a9xktn
title: all_averaged_spectra.pdf
DOI: 10.57745/tjkzc0
title: all_symptom_spectra.pdf
DOI: 10.57745/hbqboi
title: all_variety_spectra.pdf
DOI: 10.57745/zksdkl
title: sc12_all_legend.pdf
DOI: 10.57745/tedx2p
title: sc34_all_legend.pdf
DOI: 10.57745/ru6rrl
title: spectrum_mean_std.pdf
DOI: 10.57745/cpx8ih
title: description-2.tab
DOI: 10.6084/m9.figshare.23507334
title: grow_data_new

description: Pig grow data for genomic selection, containing three breed (Duroc, Landrace and Yorkshire).
DOI: 10.5519/qd.sfnotid8
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5573333 records)
DOI: 10.6084/m9.figshare.22226155.v1
title: Convergence, plasticity, and tissue residence of regulatory and effector T cell response

description: Data (clonesets) and metadata for CD4 and Treg cells in tissues for 6 lung challenges <br>
DOI: 10.6084/m9.figshare.23507343
title: pedi_data_new

description: Pig pedigree data for genomic selection, containing three breed (Duroc, Landrace and Yorkshire).
DOI: 10.5281/zenodo.8032305
title: Termlex

description: This is the first version of the Termlex extension. The Termlex proposal is devised as an extension of the Ontolex vocabulary to model different types of terminological resources.
DOI: 10.5281/zenodo.8032304
title: Termlex

description: This is the first version of the Termlex extension. The Termlex proposal is devised as an extension of the Ontolex vocabulary to model different types of terminological resources.
DOI: 10.6084/m9.figshare.20186825
title: Synthetic Printed Words and Test Protocols Data for Bangla OCR

description: Synthetic Printed word image data and test protocols word image Data repository for the paper <strong>"A Multifaceted Evaluation of Representation of Graphemes for Practically Effective Bangla OCR."</strong> In this paper, we have utilized the popular Convolutional Recurrent Neural Network (CRNN) architecture and implemented our grapheme representation strategies to design the final labels of the model. Due to the absence of a large-scale Bangla word-level printed dataset, we created a synthetically generated Bangla corpus containing 2 million samples that are representative and sufficiently varied in terms of fonts, domain, and vocabulary size to train our Bangla OCR model. To test the various aspects of our model, we have also created 6 test protocols. Finally, to establish the generalizability of our grapheme representation methods, we have performed training and testing on external handwriting datasets. Updates: 10 June 2023: The paper has been accepted for publication in International Journal on Document Analysis and Recognition (IJDAR).
DOI: 10.5517/ccdc.csd.cc2chth6
title: CCDC 2189948: Experimental Crystal Structure Determination

description: Related Article: Lan-Jun Zhang, Pei-Zhi Huang, Ke-Jing Li, Yue-Yang Cao, Yue Sun, Wei-Jiao Feng, Yu-Xian Wang, Yi-Lin He, Le-Yuan Mi, Yan-Li Wei, Qi-Zhong Lai, Jian-Jun Chen, Kun Gao|2023|Bioorg.Chem.|138|106619|doi:10.1016/j.bioorg.2023.106619
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.cc2d20yg
title: CCDC 2206485: Experimental Crystal Structure Determination

description: Related Article: Rashim Bawa, Swati Negi, Bholey Singh, Balaram Pani, Rakesh Kumar|2023|RSC Advances|13|15391|doi:10.1039/D3RA02021E
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.cc2d20zh
title: CCDC 2206486: Experimental Crystal Structure Determination

description: Related Article: Rashim Bawa, Swati Negi, Bholey Singh, Balaram Pani, Rakesh Kumar|2023|RSC Advances|13|15391|doi:10.1039/D3RA02021E
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.cc2df6gj
title: CCDC 2217227: Experimental Crystal Structure Determination

description: Related Article: Shunan Kaping, Hakani Daioo Sympli, Labet Bankynmaw Marpna, Anitha Kandasamy, Jai N. Vishwakarma|2023|J.Mol.Struct.|1288|135766|doi:10.1016/j.molstruc.2023.135766
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.cc2dfxqh
title: CCDC 2217917: Experimental Crystal Structure Determination

description: Related Article: Sourav Mondal, Tamal Ballav, Tofayel Sheikh Mohammad Tofayel, Venkataraman Ganesh|2023|Org.Lett.|25|3941|doi:10.1021/acs.orglett.3c01318
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.cc2drs32
title: CCDC 2227384: Experimental Crystal Structure Determination

description: Related Article: Lan-Jun Zhang, Pei-Zhi Huang, Ke-Jing Li, Yue-Yang Cao, Yue Sun, Wei-Jiao Feng, Yu-Xian Wang, Yi-Lin He, Le-Yuan Mi, Yan-Li Wei, Qi-Zhong Lai, Jian-Jun Chen, Kun Gao|2023|Bioorg.Chem.|138|106619|doi:10.1016/j.bioorg.2023.106619
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: 10.5281/zenodo.7935161
title: Dataset of Feasible, Edmonds' and Geographic Bi-vectors and Tri-vectors

description: The dataset consists of two sets of files in JSON format. Each file consists of an array of elements and each element is presented by a vector (it can be a bi-vector in the case of the first set or a tri-vector in the latter case). This vector is followed by the Euler characteristic of surface and a set of Edmonds’ realizations, described as a concatenation of two incidence matrices. Such bi-matrix gives rise to the corresponding graph, its dual, as well as one-to-one correspondence between their edges. The first set consists of 7 files, each containing all possible bi-vectors with all Edmonds’ realizations in a form of bi-matrices for a fixed ell (ranging from 2 to 7). The bi-vectors are stored in files with the value of ell at the end: edmonds-bi-vector-realization-ell2.json edmonds-bi-vector-realization-ell3.json edmonds-bi-vector-realization-ell4.json edmonds-bi-vector-realization-ell5.json edmonds-bi-vector-realization-ell6.json edmonds-bi-vector-realization-ell7.json The second set consists of 9 files, each containing all possible tri-vectors with all Edmonds’ realizations in a form of bi-matrices for a fixed ell (ranging from 2 to 8). The last file (for ell 9, contains only one Edmond’s realization per vector (if existing) due to time complexity). The tri-vectors are stored in files with the ell mentioned at the end: edmonds-tri-vector-realization-ell2.json edmonds-tri-vector-realization-ell3.json edmonds-tri-vector-realization-ell4.json edmonds-tri-vector-realization-ell5.json edmonds-tri-vector-realization-ell6.json edmonds-tri-vector-realization-ell7.json edmonds-tri-vector-realization-ell8.json edmonds-tri-vector-realization-ell9.json A set of utilities show use cases: Parsing functions with examples implemented in Sage: edmonds_import.sage Parsing functions with examples implemented in Java: EdmondsReaderBivector.java EdmondsReader.java
DOI: 10.5281/zenodo.7935162
title: Dataset of Feasible, Edmonds' and Geographic Bi-vectors and Tri-vectors

description: The dataset consists of two sets of files in JSON format. Each file consists of an array of elements and each element is presented by a vector (it can be a bi-vector in the case of the first set or a tri-vector in the latter case). This vector is followed by the Euler characteristic of surface and a set of Edmonds’ realizations, described as a concatenation of two incidence matrices. Such bi-matrix gives rise to the corresponding graph, its dual, as well as one-to-one correspondence between their edges. The first set consists of 7 files, each containing all possible bi-vectors with all Edmonds’ realizations in a form of bi-matrices for a fixed ell (ranging from 2 to 7). The bi-vectors are stored in files with the value of ell at the end: edmonds-bi-vector-realization-ell2.json edmonds-bi-vector-realization-ell3.json edmonds-bi-vector-realization-ell4.json edmonds-bi-vector-realization-ell5.json edmonds-bi-vector-realization-ell6.json edmonds-bi-vector-realization-ell7.json The second set consists of 9 files, each containing all possible tri-vectors with all Edmonds’ realizations in a form of bi-matrices for a fixed ell (ranging from 2 to 8). The last file (for ell 9, contains only one Edmond’s realization per vector (if existing) due to time complexity). The tri-vectors are stored in files with the ell mentioned at the end: edmonds-tri-vector-realization-ell2.json edmonds-tri-vector-realization-ell3.json edmonds-tri-vector-realization-ell4.json edmonds-tri-vector-realization-ell5.json edmonds-tri-vector-realization-ell6.json edmonds-tri-vector-realization-ell7.json edmonds-tri-vector-realization-ell8.json edmonds-tri-vector-realization-ell9.json A set of utilities show use cases: Parsing functions with examples implemented in Sage: edmonds_import.sage Parsing functions with examples implemented in Java: EdmondsReaderBivector.java EdmondsReader.java
DOI: 10.5281/zenodo.8032314
title: Dataset of Feasible, Edmonds' and Geographic Bi-vectors and Tri-vectors

description: The dataset consists of two sets of files in JSON format. Each file consists of an array of elements and each element is presented by a vector (it can be a bi-vector in the case of the first set or a tri-vector in the latter case). This vector is followed by the Euler characteristic of surface and a set of Edmonds’ realizations, described as a concatenation of two incidence matrices. Such bi-matrix gives rise to the corresponding graph, its dual, as well as one-to-one correspondence between their edges. The first set consists of 7 files, each containing all possible bi-vectors with all Edmonds’ realizations in a form of bi-matrices for a fixed ell (ranging from 2 to 7). The bi-vectors are stored in files with the value of ell at the end: edmonds-bi-vector-realization-ell2.json edmonds-bi-vector-realization-ell3.json edmonds-bi-vector-realization-ell4.json edmonds-bi-vector-realization-ell5.json edmonds-bi-vector-realization-ell6.json edmonds-bi-vector-realization-ell7.json The second set consists of 9 files, each containing all possible tri-vectors with all Edmonds’ realizations in a form of bi-matrices for a fixed ell (ranging from 2 to 8). The last file (for ell 9, contains only one Edmond’s realization per vector (if existing) due to time complexity). The tri-vectors are stored in files with the ell mentioned at the end: edmonds-tri-vector-realization-ell2.json edmonds-tri-vector-realization-ell3.json edmonds-tri-vector-realization-ell4.json edmonds-tri-vector-realization-ell5.json edmonds-tri-vector-realization-ell6.json edmonds-tri-vector-realization-ell7.json edmonds-tri-vector-realization-ell8.json edmonds-tri-vector-realization-ell9.json A set of utilities show use cases: Parsing functions with examples implemented in Sage: edmonds_import.sage Parsing functions with examples implemented in Java: EdmondsReaderBivector.java EdmondsReader.java
DOI: 10.6084/m9.figshare.23507352
title: geno_data_new

description: Pig genotype data for genomic selection, containing three breed (Duroc, Landrace and Yorkshire).
DOI: 10.25625/8rllzt/uu7ikj
title: Data_N2O_OilPalmPlantation_Jambi_Indonesia.tab

description: :unav
DOI: 10.5281/zenodo.7950760
title: Dataset for Interfacial Chemistry Effects in the Electrochemical Performance of Silicon Electrodes under Lithium-ion Battery Conditions

description: This dataset provides the raw data to the manuscript "<strong>Interfacial Chemistry Effects in the Electrochemical Performance of Silicon Electrodes under Lithium-ion Battery Conditions"</strong> published in Small (https://doi.org/10.1002/smll.202303442) Specifically, the following measurements are provided: Scanning electrochemical cell microscopy (SECCM) data Scanning electron microscopy (SEM) images Secondary ion mass spectrometry (SIMS) data Transmission electron microscopy (TEM) images X-ray photoelectron spectroscopy (XPS) data
DOI: 10.5281/zenodo.7950759
title: Dataset for Interfacial Chemistry Effects in the Electrochemical Performance of Silicon Electrodes under Lithium-ion Battery Conditions

description: This dataset provides the raw data to the manuscript "<strong>Interfacial Chemistry Effects in the Electrochemical Performance of Silicon Electrodes under Lithium-ion Battery Conditions"</strong> published in Small (https://doi.org/10.1002/smll.202303442) Specifically, the following measurements are provided: Scanning electrochemical cell microscopy (SECCM) data Scanning electron microscopy (SEM) images Secondary ion mass spectrometry (SIMS) data Transmission electron microscopy (TEM) images X-ray photoelectron spectroscopy (XPS) data
DOI: 10.25625/8rllzt
title: N2O flux (eddy covariance and vented soil chamber) and microclimatic measurements, PTPN6 oil palm plantation, Jambi, Indonesia

description: This dataset contains data of ecosystem-scale CO2 and N2O fluxes based on eddy covariance measurements, data of point-scale N2O fluxes based on vented soil chamber measurements, and measurements of microclimatic variables in PTPN6 oil palm plantation, Jambi Province, Indonesia, during the period August 2017 to April 2019.
DOI: 10.6084/m9.figshare.23507385
title: <strong>Differential effects of reward and punishment on reinforcement-based</strong> <strong>motor learning and generalization</strong>

description: Group 1 is for the reward while group 2 is for the punishment. A is short for reach angle. V is short for movement variability. LR1 is short for learning rate during the initial learning. LR2 is short for learning rate during the generalization test. LE1 is short for learning extent during the initial learning. LE2 is short for learning extent during the generalization test. <br>
DOI: 10.5281/zenodo.8029805
title: Data underlying the study on 'Lability and the rigidification of word order. Evidence from Early Middle English', published in 'Linguistics'

description: see the publication for a description of the data
DOI: 10.5281/zenodo.8029804
title: Data underlying the study on 'Lability and the rigidification of word order. Evidence from Early Middle English', published in 'Linguistics'

description: see the publication for a description of the data
DOI: 10.5281/zenodo.8032339
title: IMP-MARL dataset

description: IMP-MARL dataset Authors: Pascal Leroy (pleroy@uliege.be), Pablo G. Morato (pgmdo@dtu.dk), Jonathan Pisane, Athanasios Kolios, Damien Ernst GitHub repository: https://github.com/moratodpg/imp_marl IMP-MARL environments:<br> struct_uc: k-out-of-n system<br> 2-out-of-3, 4-out-of-5, 9-out-of-10, 48-out-of-50, 95-out-of-100<br> (with and without campaign cost)<br> struct_c: correlated k-out-of-n system<br> 2-out-of-3, 4-out-of-5, 9-out-of-10, 48-out-of-50, 95-out-of-100<br> (with and without campaign cost)<br> owf: offshore wind farm<br> 1 wind turbine, 2 wind turbines, 5 wind turbines, 25 wind turbines, 50 wind turbines<br> (with and without campaign cost) MARL methods investigated: QMIX, QVMIX, QPLEX, COMA, FACMAC, IQL, DQN. This dataset contains:<br> MARL logs.zip =&gt; The configuration, execution, and result files of all investigated MARL methods.<br> Each zip file contains the logs from an IMP-MARL environment.<br> There, test and train files are stored for each method:<br> cout.txt contains stored console ouputs.<br> config.json contains the list of all parameter values for the experiments.<br> info.json is the log file, listing reward values, for example.<br> run.json contains simply logged information about the host, typically hardware.<br> best_agent_networks.zip =&gt;The controller networks’ weights of the best MARL-based policies. <br> Controller files are stored as a pytorch file for each method/environment investigated (agent.th).<br> heur_logs.zip =&gt; The configuration, execution, and result files of the computed expert-based heuristic policies. <br> /heur_search =&gt; logs resulting from all evaluated heuristic policies, stored as numpy files.<br> /heur_search =&gt; results from the best performing heuristics over 10,000 policy realisations, stored as pickle files.
DOI: 10.5281/zenodo.8032338
title: IMP-MARL dataset

description: IMP-MARL dataset Authors: Pascal Leroy (pleroy@uliege.be), Pablo G. Morato (pgmdo@dtu.dk), Jonathan Pisane, Athanasios Kolios, Damien Ernst GitHub repository: https://github.com/moratodpg/imp_marl IMP-MARL environments:<br> struct_uc: k-out-of-n system<br> 2-out-of-3, 4-out-of-5, 9-out-of-10, 48-out-of-50, 95-out-of-100<br> (with and without campaign cost)<br> struct_c: correlated k-out-of-n system<br> 2-out-of-3, 4-out-of-5, 9-out-of-10, 48-out-of-50, 95-out-of-100<br> (with and without campaign cost)<br> owf: offshore wind farm<br> 1 wind turbine, 2 wind turbines, 5 wind turbines, 25 wind turbines, 50 wind turbines<br> (with and without campaign cost) MARL methods investigated: QMIX, QVMIX, QPLEX, COMA, FACMAC, IQL, DQN. This dataset contains:<br> MARL logs.zip =&gt; The configuration, execution, and result files of all investigated MARL methods.<br> Each zip file contains the logs from an IMP-MARL environment.<br> There, test and train files are stored for each method:<br> cout.txt contains stored console ouputs.<br> config.json contains the list of all parameter values for the experiments.<br> info.json is the log file, listing reward values, for example.<br> run.json contains simply logged information about the host, typically hardware.<br> best_agent_networks.zip =&gt;The controller networks’ weights of the best MARL-based policies. <br> Controller files are stored as a pytorch file for each method/environment investigated (agent.th).<br> heur_logs.zip =&gt; The configuration, execution, and result files of the computed expert-based heuristic policies. <br> /heur_search =&gt; logs resulting from all evaluated heuristic policies, stored as numpy files.<br> /heur_search =&gt; results from the best performing heuristics over 10,000 policy realisations, stored as pickle files.
DOI: 10.6084/m9.figshare.22261912.v6
title: Supplementary Data for PhD thesis

description: Supplementary Dataset, MAGs (with locus_tag) and meta-transcripts