cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc29zhf7
title: CCDC 2144469: Experimental Crystal Structure Determination

description: Related Article: David Perl, Seok J. Lee, Alan Ferguson, Geoffrey B. Jameson, Shane G. Telfer|2023|Nature Chemistry|15|1358|doi:10.1038/s41557-023-01277-z
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.10043597
title: MSV000085119_pos

description: ENPKG ready dataset, build from the files in positive ionization mode of https://doi.org/doi:10.25345/C5S401
DOI: 10.5281/zenodo.10043596
title: MSV000085119_pos

description: ENPKG ready dataset, build from the files in positive ionization mode of https://doi.org/doi:10.25345/C5S401
DOI: 10.5281/zenodo.10043639
title: MSV000085119_pos

description: ENPKG ready dataset, build from the files in positive ionization mode of https://doi.org/doi:10.25345/C5S401
DOI: 10.5281/zenodo.10043743
title: MSV000085119_pos

description: ENPKG ready dataset, build from the files in positive ionization mode of https://doi.org/doi:10.25345/C5S401
DOI: 10.6084/m9.figshare.24441205
title: Data set of anthropometric measurement , FMD, and exercise testing obtained from collegiate student athletes

description: The data include anthropometric measurement , FMD, and exercise testing obtained from collegiate student athletes.
DOI: 10.5281/zenodo.10033773
title: Model Data/Diagnostics for the RIFTJet Sensitivity Experiments

description: © Crown Copyright, Met Office
The accompanying data is made available under the terms of the Non-Commercial Government Licence (http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/).
DOI: 10.5281/zenodo.10033772
title: Model Data/Diagnostics for the RIFTJet Sensitivity Experiments

description: © Crown Copyright, Met Office
The accompanying data is made available under the terms of the Non-Commercial Government Licence (http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/).
DOI: 10.6084/m9.figshare.24146913.v3
title: Metagenome-assembled genome catalog obtained using stool metagenomes of melanoma patients

description: Stool metagenomes from seven published studies were used for obtained of metagenome-assembled genome catalog [Frankel et al., 2017; Gopalakrishnan et al., 2018; Matson et al., 2018; Baruch et al., 2021; Davar et al., 2021; Spencer et al., 2021; Lee et al., 2022].References:1) Frankel, Arthur E., et al. "Metagenomic shotgun sequencing and unbiased metabolomic profiling identify specific human gut microbiota and metabolites associated with immune checkpoint therapy efficacy in melanoma patients." Neoplasia 19.10 (2017): 848-855.2) Gopalakrishnan, Vancheswaran, et al. "Gut microbiome modulates response to anti–PD-1 immunotherapy in melanoma patients." Science 359.6371 (2018): 97-103.3) Matson, Vyara, et al. "The commensal microbiome is associated with anti–PD-1 efficacy in metastatic melanoma patients." <i>Science</i> 359.6371 (2018): 104-108.4) Baruch, Erez N., et al. "Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients." Science 371.6529 (2021): 602-609.5) Davar, Diwakar, et al. "Fecal microbiota transplant overcomes resistance to anti–PD-1 therapy in melanoma patients." Science 371.6529 (2021): 595-602.6) Lee, Karla A., et al. "Cross-cohort gut microbiome associations with immune checkpoint inhibitor response in advanced melanoma." Nature Medicine 28.3 (2022): 535-544.7) Spencer, Christine N., et al. "Dietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response." Science 374.6575 (2021): 1632-1640.
DOI: 10.25405/data.ncl.24210762
title: Laminar osteochondral co-cultures

description: Data underpinning paper entitled Effect of cell density and co-culture on ECM production and mechanical properties of novel laminar osteochondral constructs
DOI: 10.25405/data.ncl.24210762.v1
title: Laminar osteochondral co-cultures

description: Data underpinning paper entitled Effect of cell density and co-culture on ECM production and mechanical properties of novel laminar osteochondral constructs
DOI: 10.5281/zenodo.10043628
title: DOM_Chym_Inhibith_(No MeOH)_mzml

description: DOM Vs Chymotrypsin with inhibithors Chymostatin and Pepstatin A at differenc conc
DOI: 10.5281/zenodo.10043627
title: DOM_Chym_Inhibith_(No MeOH)_mzml

description: DOM Vs Chymotrypsin with inhibithors Chymostatin and Pepstatin A at differenc conc
DOI: 10.6084/m9.figshare.24441217
title: Sequence and alignment data

description: Sequence data and alignment
DOI: 10.5281/zenodo.10043568
title: DOM_Chym_Inhibith_(No MeOH)
DOI: 10.5281/zenodo.10043567
title: DOM_Chym_Inhibith_(No MeOH)
DOI: 10.25345/c5j38kt86
title: MassIVE MSV000093194 - Proteome of the tears of non-infectious uveitis patients responders and non-responders to adalimumab treatment
title: Tear proteome of patients with non-infectious uveitis responders and non-responders to adalimumab treatment.
DOI: 10.5519/qd.abies6rv
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (8 records)
DOI: 10.5519/qd.kqtdd9fk
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2 records)
DOI: 10.21961/mdm:45864
title: TRR295 Retune Core Data Set

description: DRKS-ID der Studie: DRKS00028750 Allgemeinverständliche Kurzbeschreibung Die zentrale Patientenkohorte des SFB/TR ReTune beinhaltet einen klinischen Kerndatensatz von Patienten mit Parkinson, Tremor oder Dystonie. Sie dient als eine Informationsplattform, um Effekte der Behandlung mit Tiefen Hirnstimulation auf die Patient:innen mit unterschiedlichen Erkrankungen zu untersuchen.
DOI: 10.6084/m9.figshare.24441247
title: COSMIC SIZES

description: This study empirically evaluates the functionality coverage of 18 mobile applications (apps) for Postnatal care including a recently developed app in Morocco ‘Mamma&Baby’. This evaluation is based on a comparison ofthe COSMIC _ISO 19761 functional size of these apps with the score obtained in a previous evaluation based on functionsextraction through a quality assessment questionnaire. This comparison allows to discuss the relationship between the functional size of the 18 apps, their users' ratings in the Play Store as well as the number of downloads. While for most of the assessed apps, there is only a small shift between the rankings of the two evaluations, for some apps, the shift is huge due to the number of features added and not covered by the score previously obtained. This study illustrates the use of COSMIC as an effective method for corrective or evolutionary updates since it takes into account all the functions and features of postnatal apps. For the 'Mamma&Baby' app, efforts are required to boost the number of downloads, optimize its visibility, and attract the highest number of users.
DOI: 10.5517/ccdc.csd.cc29zhg8
title: CCDC 2144470: Experimental Crystal Structure Determination

description: Related Article: David Perl, Seok J. Lee, Alan Ferguson, Geoffrey B. Jameson, Shane G. Telfer|2023|Nature Chemistry|15|1358|doi:10.1038/s41557-023-01277-z
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.cc29zhh9
title: CCDC 2144471: Experimental Crystal Structure Determination

description: Related Article: David Perl, Seok J. Lee, Alan Ferguson, Geoffrey B. Jameson, Shane G. Telfer|2023|Nature Chemistry|15|1358|doi:10.1038/s41557-023-01277-z
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.cc29zhkc
title: CCDC 2144473: Experimental Crystal Structure Determination

description: Related Article: David Perl, Seok J. Lee, Alan Ferguson, Geoffrey B. Jameson, Shane G. Telfer|2023|Nature Chemistry|15|1358|doi:10.1038/s41557-023-01277-z
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.cc29zh3x
title: CCDC 2144459: Experimental Crystal Structure Determination

description: Related Article: David Perl, Seok J. Lee, Alan Ferguson, Geoffrey B. Jameson, Shane G. Telfer|2023|Nature Chemistry|15|1358|doi:10.1038/s41557-023-01277-z
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.cc29zm20
title: CCDC 2144582: Experimental Crystal Structure Determination

description: Related Article: David Perl, Seok J. Lee, Alan Ferguson, Geoffrey B. Jameson, Shane G. Telfer|2023|Nature Chemistry|15|1358|doi:10.1038/s41557-023-01277-z
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: 10.5519/qd.34e0qbid
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (22 records)
DOI: 10.5281/zenodo.10043863
title: ChemM_NoTreat(QTOF)_32mix_mzml
DOI: 10.5281/zenodo.10043862
title: ChemM_NoTreat(QTOF)_32mix_mzml
DOI: 10.25345/c5db7w15z
title: MassIVE MSV000093195 - Neoadjuvant Chemo- or Chemo-Radiation-Therapy of Pancreatic Ductal Adenocarcinoma Differentially Shift ECM Composition, Complement Activation, Energy Metabolism, and Ribosomal Proteins of the Residual Tumor Mass
title: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with limited curative therapy options. Biological tumor alterations induced by neoadjuvant therapy remain unexplored. In the present study, we performed mass spectrometry-based proteomics to unravel these changes. We identified fundamental proteome alterations between treatment-naive PDAC and the residual tumor mass after either neoadjuvant chemo- or chemo-radiation therapy. A highlight is the integration of publicly accessible proteome data using adequate correction.
DOI: 10.5281/zenodo.10043849
title: HYPERCOG – training material dataset

description: The Hypercog training pack is aimed at teaching cyberphysical systems to two different types of profiles : 
1) the operators at the 3 industrial sites impacted by the implementation of the HyperCOG solution (Solvay, Sidenor, Çimsa);
 2) Master students who would like to get familiar with cyberphysical systems.
The main objective for operators training was to understand the human environment where the solutions are implemented. For students it was to identify the skills needed to become familiar with cyber-physical systems. The ultimate goal was to train operators to overcome the skill gap, so they can use HyperCOG solutions in their daily work, and to produce training materials on cyber-physical systems based on the Hypercog experience, aimed at Master students .
DOI: 10.5281/zenodo.10043848
title: HYPERCOG – training material dataset

description: The Hypercog training pack is aimed at teaching cyberphysical systems to two different types of profiles : 
1) the operators at the 3 industrial sites impacted by the implementation of the HyperCOG solution (Solvay, Sidenor, Çimsa);
 2) Master students who would like to get familiar with cyberphysical systems.
The main objective for operators training was to understand the human environment where the solutions are implemented. For students it was to identify the skills needed to become familiar with cyber-physical systems. The ultimate goal was to train operators to overcome the skill gap, so they can use HyperCOG solutions in their daily work, and to produce training materials on cyber-physical systems based on the Hypercog experience, aimed at Master students .
DOI: 10.6084/m9.figshare.24417127
title: Moral Uncanny Valley Revisited

description: <br><br>Stripped down STATA version of the data set for Study 1 and some syntax for its analysis. Study 2 was collected in conjunction with other experiments prior to GDPR implementation and will be shared at a later date once it has been inspected for potentially identifying information that needs to be removed.<br>Almost all health related information and background questions and related identifying information has been removed from the shared data. Prolific IDs have also been removed -- for this reason there are also some test responses included inte data-set, that are filtred out by using the attention checks. Geolocation information was never stored.<br>Potential unreported exploratory variables will still be used for potential future publications and are therefore not published.<br>Variables reported in the manuscript are provided for replicating the anylsis.<br>Simplified analysis script has been provided, that should correspond with the manuscript with 99% accuracy.<br>Any potential errors and mismatch between the information available here and between the online manuscript are due to human error stemming from lack of mental resources, exhaustion, dyslexia or time or some combination of all of these.
DOI: 10.5281/zenodo.8396590
title: Data from: Chemotype-specific susceptibility to foliar spray treatment with 5-azacytidine and kaolin in Tanacetum vulgare

description: General information
This deposition contains the datasets that were used in the statistical analysis of the manuscript entitled Chemotype-specific susceptibility to foliar spray treatment with 5-azacytidine and kaolin in Tanacetum vulgar submitted to PeerJ.
Uploaded files

README file to explain the datasets
Glandular trichomes dataset called Jürgens_et_al_2023_PeerJ_RawData_GlandularTrichomes.txt
VOC dataset from 5-azacytidine foliar spray treatment called Jürgens_et_al_2023_PeerJ_RawData_VOC_5AZA.txt
VOC dataset from kaolin foliar spray treatment called JJürgens_et_al_2023_PeerJ_RawData_VOC_Kaolin.txt
description: This work was funded by the Deutsche Forschungsgemeinschaft (DFG) as part of the research unit FOR3000 (MU1829/29-1).
DOI: 10.5281/zenodo.8396589
title: Data from: Chemotype-specific susceptibility to foliar spray treatment with 5-azacytidine and kaolin in Tanacetum vulgare

description: General information
This deposition contains the datasets that were used in the statistical analysis of the manuscript entitled Chemotype-specific susceptibility to foliar spray treatment with 5-azacytidine and kaolin in Tanacetum vulgar submitted to PeerJ.
Uploaded files

README file to explain the datasets
Glandular trichomes dataset called Jürgens_et_al_2023_PeerJ_RawData_GlandularTrichomes.txt
VOC dataset from 5-azacytidine foliar spray treatment called Jürgens_et_al_2023_PeerJ_RawData_VOC_5AZA.txt
VOC dataset from kaolin foliar spray treatment called JJürgens_et_al_2023_PeerJ_RawData_VOC_Kaolin.txt
description: This work was funded by the Deutsche Forschungsgemeinschaft (DFG) as part of the research unit FOR3000 (MU1829/29-1).
DOI: 10.57967/hf/1268
title: SNLI_Dutch_translated_with_Marianmt
DOI: 10.34740/kaggle/dsv/6800773
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5281/zenodo.6352432
title: Time optimal low-thrust rendezvous from an asteroid belt

description: This regression dataset allows to train models representing the optimal time of flight of a constant acceleration low-thrust trajectory aimed at a randezvous with a target orbiting station (placed at 1.3 Astronomical Units). The attributes, X, represent the modified equinoctial parameters (p,f,g,h,k,L) of the spacecraft (SI units) at the start of the transfer. L is the true longitude. Different representations of the initial state are likely key to improving any model. The time of flight, Y, is given in units of TIME=5022642.890912783s The dataset was used for <strong>value function learning</strong> in our paper: Izzo, D. and Origer, S.: "Neural representation of a time optimal, constant acceleration rendezvous" where we were more interested on the effects/use of the data augmentation technique called "Backward Propagation of Optimal Examples" than on the accuracy of the resulting neural model. A MAE of ~22 days is obtained on the augmented dataset (~35 days on the non augmented one). Both these numbers can likely be improved considerably, constituting a nice challenge for the community. The python pickles can be opened as: with open("filename.pk", "rb") as f:<br> X, Y = pkl.load(f) else , we also provide the corresponding csv files. * <strong>training</strong>: contains 3000 items * <strong>test</strong>: contains 1000 items * <strong>training_augmented_32_0.0001</strong>: contains 96000 items (augmented from the 3000 of training)
DOI: 10.6084/m9.figshare.24441340
title: AI-CXR and Hematology Parameters for Prediction Mortality and Intubation Event In COVID-19 Patients from Second Referral HospitalItem

description: Researchers aim to predict the mortality of COVID-19 patients considering both the AI-CXR and hematology parameters.
DOI: 10.5281/zenodo.10043927
title: MDE language translation dataset

description: This is the dataset for our SNCS paper "Improving the usability of MDE for software language processing"
DOI: 10.5281/zenodo.10043926
title: MDE language translation dataset

description: This is the dataset for our SNCS paper "Improving the usability of MDE for software language processing"
DOI: 10.5281/zenodo.10043947
title: ChemM_APC_untarg_raw and mzml

description: Q Exactive HF
DOI: 10.5281/zenodo.10043946
title: ChemM_APC_untarg_raw and mzml

description: Q Exactive HF
DOI: 10.5281/zenodo.10043904
title: code, scripts and data for "Energy transfers in surface wave-averaged equations"  by L. Czeschel and C. Eden
DOI: 10.5281/zenodo.10043903
title: code, scripts and data for "Energy transfers in surface wave-averaged equations"  by L. Czeschel and C. Eden
DOI: 10.5258/soton/d2776
title: Dataset supporting the Southampton University Doctoral Thesis "German industrialization and bank credit with particular consideration of the disaggregated credit model: three essays"

description: This dataset contains all the relevant data to reproduce the results from the PhD thesis titled "German Industrialization and Bank Credit with particular consideration of the Disaggregated Credit Model: Three Essays". It geographically covers German macroeconomic national income data as well as financial statistics such as banking data during the period from 1860 to 1913. It was collected both from different authors and by means of aggregating primary data sources such as individual bank's ledgers or single company's security issuances. Economists are encouraged to either reproduce the results presented in the PhD thesis or use this dataset openly for further future research. In order to view the data, one should use MS Excel or similar software to open the xlsx file. Licence: CC BY
DOI: 10.5281/zenodo.6352431
title: Time optimal low-thrust rendezvous from an asteroid belt

description: This dataset allows to train regression models representing the optimal time of flight of a constant acceleration low-thrust trajectory aimed at a randezvous with a target orbiting station (placed at 1.3 Astronomical Units).
The version 2 of the dataset should be used, correcting a data bias towards simpler transfers present in the previous version.
The attributes, X, represent the modified equinoctial parameters (p,f,g,h,k,sin(L), cos(L)) of the spacecraft (SI units) at the start of the transfer. L is the true longitude. Different representations of the initial state are likely key to improving any model.
The time of flight, Y,  is given in units of TIME=5022642.890912783s
The dataset was used for value function learning in our paper:
Izzo, D. and Origer, S.: "Neural representation of a time optimal, constant acceleration rendezvous" - https://arxiv.org/abs/2203.15490
where we were more interested on the effects/use of the data augmentation technique called "Backward Generation of Optimal Examples" than on the accuracy of the resulting neural model. A MAE of ~25 days is obtained on the test set, when training from the augmented dataset (vs. ~34.08 days from the non augmented one). Both these numbers can likely be improved considerably, constituting a nice challenge for the community.
The python pickles can be opened as:

with open("filename.pk", "rb") as f:    X, Y = pkl.load(f)
else , we also provide the corresponding csv files.
* training: contains 3000 items
* test: contains ~1000 items
* training_augmented_32_0.0001: contains 96000 items (augmented from the 3000 of training)
DOI: 10.5281/zenodo.6365326
title: Time optimal low-thrust rendezvous from an asteroid belt

description: This dataset allows to train regression models representing the optimal time of flight of a constant acceleration low-thrust trajectory aimed at a randezvous with a target orbiting station (placed at 1.3 Astronomical Units).
The version 2 of the dataset should be used, correcting a data bias towards simpler transfers present in the previous version.
The attributes, X, represent the modified equinoctial parameters (p,f,g,h,k,sin(L), cos(L)) of the spacecraft (SI units) at the start of the transfer. L is the true longitude. Different representations of the initial state are likely key to improving any model.
The time of flight, Y,  is given in units of TIME=5022642.890912783s
The dataset was used for value function learning in our paper:
Izzo, D. and Origer, S.: "Neural representation of a time optimal, constant acceleration rendezvous" - https://arxiv.org/abs/2203.15490
where we were more interested on the effects/use of the data augmentation technique called "Backward Generation of Optimal Examples" than on the accuracy of the resulting neural model. A MAE of ~25 days is obtained on the test set, when training from the augmented dataset (vs. ~34.08 days from the non augmented one). Both these numbers can likely be improved considerably, constituting a nice challenge for the community.
The python pickles can be opened as:

with open("filename.pk", "rb") as f:    X, Y = pkl.load(f)
else , we also provide the corresponding csv files.
* training: contains 3000 items
* test: contains ~1000 items
* training_augmented_32_0.0001: contains 96000 items (augmented from the 3000 of training)