cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7573641
title: MRI versus CT in the detection of brain lesions in patients with infective endocarditis before or after cardiac surgery

description: Vitali P, Savoldi F, Segati F, Melazzini L, Zanardo M, Fedeli MP, Benedek A, Di Leo G, Menicanti L, Sardanelli F. MRI versus CT in the detection of brain lesions in patients with infective endocarditis before or after cardiac surgery. Neuroradiology. 2022 May;64(5):905-913. doi: 10.1007/s00234-021-02810-y. Epub 2021 Oct 13. PMID: 34647143; PMCID: PMC9005423. Abstract <strong>Purpose: </strong>Imaging of brain involvement in infective endocarditis can drive the clinical management of this serious condition. MRI is very sensitive, but CT is more readily available. In this retrospective study, we compared the detection rates of CT and MRI. <strong>Methods: </strong>After Ethics Committee approval, we retrospectively reviewed a series of 20 patients (13 males, median age 64 years) who underwent both CT and MRI either before or after cardiac surgery for definite infective endocarditis. Plain CT and MRI were evaluated for acute ischemic lesions, both punctuate and large, intraparenchymal hemorrhages, cerebral microbleeds, subarachnoid hemorrhages, abscesses, microabscesses, and meningitis. Qualitative assessment and McNemar test were performed. The value of contrast-enhanced scans (MRI, n = 14; CT, n = 9) and cognitive status were also assessed. <strong>Results: </strong>A total of 166 lesions were identified on either technique: 137 (83%) on MRI only, 4 (2%) on CT only, and 25 (15%) on both techniques (p &lt; 0.001). For these last 25 lesions, concordance on lesion type was only 16/25 (64%). MRI detected more microbleeds and ischemic lesions, while the 4 CT-only findings were false positives. Contrast-enhanced scans identified 68 enhancing lesions, mainly abscesses and microabscesses, and allowed a better characterization for 61/117 lesions (52%) with MRI, and for 11/81 (14%) with CT. Follow-up identified mild cognitive impairment in 6/13 and dementia in 3/13 patients. <strong>Conclusion: </strong>While CT rapidly excludes large hemorrhages in patients with infective endocarditis, MRI accurately distinguishes the whole spectrum of brain lesions, including small ischemic lesions, microbleeds, and microabscesses.
DOI: 10.48420/16965292
title: Training, validation and test set for M1-M20

description: Training, validation and test set used on the work: Organic Reaction Mechanism Classification with Machine Learning It contains 5,000,000 samples of kinetic profiles for reaction mechanisms transforming a substrate S into a product P, catalyzed by a catalyst, cat. These were generated by solving the corresponding ordinary differential equations corresponding to 20 typical mechanisms. These mechanisms belong to four distinct categories: 1) the core mechanism (M1), is the simplest Michaelis-Menten-type mechanism; 2) mechanisms with bicatalytic steps (M2-M5), involve either catalyst dimerization (M2 and M3), or a reaction between two different catalytic species (M4 and M5); 3) mechanisms with catalyst activation steps based on the core mechanism, where a precatalyst requires activation unimolecularly (M6), via substrate coordination (M7) or via ligand dissociation (M8); 4) mechanisms with a variety of catalyst deactivation steps from either catalytic intermediate of the core mechanism (M9-M20). These kinetic samples can be used to train, validate and test a machine learning model.<br>
DOI: 10.48420/16965292.v2
title: Training, validation and test set for M1-M20

description: Training, validation and test set used on the work: Organic Reaction Mechanism Classification with Machine Learning It contains 5,000,000 samples of kinetic profiles for reaction mechanisms transforming a substrate S into a product P, catalyzed by a catalyst, cat. These were generated by solving the corresponding ordinary differential equations corresponding to 20 typical mechanisms. These mechanisms belong to four distinct categories: 1) the core mechanism (M1), is the simplest Michaelis-Menten-type mechanism; 2) mechanisms with bicatalytic steps (M2-M5), involve either catalyst dimerization (M2 and M3), or a reaction between two different catalytic species (M4 and M5); 3) mechanisms with catalyst activation steps based on the core mechanism, where a precatalyst requires activation unimolecularly (M6), via substrate coordination (M7) or via ligand dissociation (M8); 4) mechanisms with a variety of catalyst deactivation steps from either catalytic intermediate of the core mechanism (M9-M20). These kinetic samples can be used to train, validate and test a machine learning model.<br>
DOI: 10.48420/16965292.v1
title: Training, validation and test set for M1-M20

description: Training, validation and test set used on the work: Organic Reaction Mechanism Classification with Machine Learning It contains 5,000,000 samples of kinetic profiles for reaction mechanisms transforming a substrate S into a product P, catalyzed by a catalyst, cat. These were generated by solving the corresponding ordinary differential equations corresponding to 20 typical mechanisms. These mechanisms belong to four distinct categories: 1) the core mechanism (M1), is the simplest Michaelis-Menten-type mechanism; 2) mechanisms with bicatalytic steps (M2-M5), involve either catalyst dimerization (M2 and M3), or a reaction between two different catalytic species (M4 and M5); 3) mechanisms with catalyst activation steps based on the core mechanism, where a precatalyst requires activation unimolecularly (M6), via substrate coordination (M7) or via ligand dissociation (M8); 4) mechanisms with a variety of catalyst deactivation steps from either catalytic intermediate of the core mechanism (M9-M20). These kinetic samples can be used to train, validate and test a machine learning model.<br>
DOI: 10.15151/esrf-es-1020228581
title: The Netherlands BAG

description: The Netherlands BAG includes groups situated at five Dutch universities (Utrecht, Groningen, Amsterdam, Wageningen and Eindhoven) and at the Netherlands Cancer Institute in Amsterdam, with a total of twelve PIs. The structural biology research carried out in these groups is highly diverse, including prokaryotic and eukaryotic proteins with a strong focus on targets and processes of biomedical relevance. Central to many projects are protein receptors and their interactions with other proteins and with small molecule modulators. An additional major research area concerns structure-based protein engineering and design aiming at the characterization and engineering of new enzymes for green chemistry and new-to-nature reactions. Projects deal with membrane and soluble proteins, cell surface receptors, and protein-protein, protein-ribonucleotide, protein-drug and enzyme-ligand complexes.
DOI: 10.6073/pasta/16e8656adfabde3a833af235bdd5b9b9
title: Belowground Carbon Observatory Network (BeCON) at Harvard Forest since 2020

description: A central goal of the recent Harvard Forest C Synthesis activity was to identify high-priority needs for C-cycle research at the forest. While there were a number of areas needing follow-up research, belowground C pools and fluxes remain the single largest gap in the C budget and therefore our understanding of the C cycle of the forest. To this end, we developed the Belowground Carbon Observatory Network (BeCON) as a part of the LTER-VI grant proposal. The long-term goal of BeCON is to understand temporal and spatial variations in belowground C pools in response to the various environmental changes the forest now faces [e.g., rising CO2, changes in atmospheric N, S and O3 chemistry, invading insects, land-use change, immigrating (large) herbivores]. There are two great challenges in achieving this goal: (1) excessive spatial heterogeneity in belowground C pools; and (2) lack of automated systems for analysis. These characteristics make it very difficult to quantify changes because the work is intrinsically labor intensive with low replication and the noisy data lead to low statistical power. Here, we measured soil carbon content, root biomass and carbon content, and annual root productivity following the protocols for BeCON.
DOI: 10.5281/zenodo.7573693
title: Bacidia fuscoviridis, another overlooked sorediate crustose lichen widely distributed in temperate eastern North America

description: To evaluate the generic relationships of <em>Bacidia fuscovirdis </em>within Ramalinaceae we carried out BLASTn searches of the existing reference sequences of <em>B. fuscoviridis</em> in NCBI which recovered representatives of <em>Biatora </em>Ach., <em>Lecania </em>A.Massal. and <em>Mycobilimbia</em> Rhem, as the closest hits for ITS and the lone sequence of rpb2. Based on these results we used the published phylogeny of Ramalinaceae from Kistenich et al. (2018) as a guide and constructed a multi-locus dataset that mirrored their sampling of the clade containing <em>Bilimbia</em>, <em>Lecania</em> and <em>Mycobilimbia</em> with <em>Biatora</em> as an outgroup. We downloaded the mtSSU, ITS, nucLSU and RPB2 sequences used by those authors (see Table 1) and manually aligned each dataset in Mesquite 3.31 (Maddison & Maddison 2017. We then added the available reference sequences of<em> B. fuscoviridis </em>(three ITS sequences, one rpb2 sequence) to the relevant alignment, manually adjusted them, and defined all ambiguously aligned regions and gap-rich terminal regions in an exclusion set. The excluded regions were then manually deleted, terminal gaps transformed to missing data, and uncertainties and polymorphisms transformed to missing data. The alignments were then concatenated in Mesquite and exported a single PHYLIP file. The concatenated alignment was partitioned and RAxML v8.2x (Stamatakis 2006) was used to infer a maximum likelihood (ML) topology and bootstrapping was performed with 500 pseudoreplicates and implementing the model GTRGAMMA across all partitions. The results were visualized in FigTree 1.4.3 (Rambaut 2016). This data deposit includes the underlying files for the phylogeny presented in the published study (Curtis et al., Journal of the Torrey Botanical Society). It includes a translation table for GenBank accessions and terminal names used in the dataset, individual alignments for ITS, mtSSU, nucLSU and rpb2 all in NEXUS format, concatenated alignment in NEXUS and PHYLIP format as well as partitions file for RAxML, and the final tree figure presented in the publication.
DOI: 10.5281/zenodo.7573692
title: Bacidia fuscoviridis, another overlooked sorediate crustose lichen widely distributed in temperate eastern North America

description: To evaluate the generic relationships of <em>Bacidia fuscovirdis </em>within Ramalinaceae we carried out BLASTn searches of the existing reference sequences of <em>B. fuscoviridis</em> in NCBI which recovered representatives of <em>Biatora </em>Ach., <em>Lecania </em>A.Massal. and <em>Mycobilimbia</em> Rhem, as the closest hits for ITS and the lone sequence of rpb2. Based on these results we used the published phylogeny of Ramalinaceae from Kistenich et al. (2018) as a guide and constructed a multi-locus dataset that mirrored their sampling of the clade containing <em>Bilimbia</em>, <em>Lecania</em> and <em>Mycobilimbia</em> with <em>Biatora</em> as an outgroup. We downloaded the mtSSU, ITS, nucLSU and RPB2 sequences used by those authors (see Table 1) and manually aligned each dataset in Mesquite 3.31 (Maddison & Maddison 2017. We then added the available reference sequences of<em> B. fuscoviridis </em>(three ITS sequences, one rpb2 sequence) to the relevant alignment, manually adjusted them, and defined all ambiguously aligned regions and gap-rich terminal regions in an exclusion set. The excluded regions were then manually deleted, terminal gaps transformed to missing data, and uncertainties and polymorphisms transformed to missing data. The alignments were then concatenated in Mesquite and exported a single PHYLIP file. The concatenated alignment was partitioned and RAxML v8.2x (Stamatakis 2006) was used to infer a maximum likelihood (ML) topology and bootstrapping was performed with 500 pseudoreplicates and implementing the model GTRGAMMA across all partitions. The results were visualized in FigTree 1.4.3 (Rambaut 2016). This data deposit includes the underlying files for the phylogeny presented in the published study (Curtis et al., Journal of the Torrey Botanical Society). It includes a translation table for GenBank accessions and terminal names used in the dataset, individual alignments for ITS, mtSSU, nucLSU and rpb2 all in NEXUS format, concatenated alignment in NEXUS and PHYLIP format as well as partitions file for RAxML, and the final tree figure presented in the publication.
DOI: 10.34740/kaggle/dsv/4901772
title: RIWA Dataset
DOI: 10.5281/zenodo.7573640
title: Gravity data of the Vargeão impact structure

description: This is a gravity dataset that was collected during one field campaign in the Vargeão impact structure, southern Brazil. Vargeão is a complex impact structure that has an overall diameter of approximately 12.4 km. The available file provides Free-air and Bouguer anomalies.
description: The production of this dataset was finantiated by grant #11/08014-5, São Paulo Research Foundation (FAPESP).
DOI: 10.5281/zenodo.7573639
title: Gravity data of the Vargeão impact structure

description: This is a gravity dataset that was collected during one field campaign in the Vargeão impact structure, southern Brazil. Vargeão is a complex impact structure that has an overall diameter of approximately 12.4 km. The available file provides Free-air and Bouguer anomalies.
description: The production of this dataset was finantiated by grant #11/08014-5, São Paulo Research Foundation (FAPESP).
DOI: 10.5281/zenodo.7573746
title: TotalSegmentator weights - Task 8 - liver vessels

description: nnU-Net weights for TotalSegmentator liver vessels model (Task 8). See https://github.com/wasserth/TotalSegmentator for more details.
DOI: 10.5281/zenodo.7573745
title: TotalSegmentator weights - Task 8 - liver vessels

description: nnU-Net weights for TotalSegmentator liver vessels model (Task 8). See https://github.com/wasserth/TotalSegmentator for more details.
DOI: 10.5285/a7e811fe11d34df5abac6f18c920bbeb
title: ESA Land Surface Temperature Climate Change Initiative (LST_cci): All-weather MicroWave Land Surface Temperature (MW-LST) global data record (1996-2020), v2.33

description: MW-LST is a data record of land surface temperature (LST) derived from the microwave instruments Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager / Sounder (SSMIS). Observations available at frequencies close to 18, 22, 26, and 85 GHz are used as an input to a retrieval algorithm that produces LST over all continental surfaces, twice per day (6 am/pm), at a spatial resolution of ~25 km, and over 25 years (1996-2020). The data record has been produced by the company Estellus working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci). Compared with the remaining infrared LST data records of the LST_cci, the spatial resolution of the MW-LST is coarser, and the associated retrieval errors are larger. However, it offers LST estimates for clear-sky and cloudy conditions, therefore complementing the IR LST data records, which can only provide LST for clear skies. The data record is temporally and spatially complete, although in rare occasions some data can be missing due to missing observations, e.g., due to satellite maintenance operations or anomalous behavior. The data record is provided on a regular grid of 0.25x0.25 degrees, saved as daily, monthly, and yearly netcdf files. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty. This version of the data is v2.33. It fixes an issue that was found with the variable 'lst_unc_time_correction' in the previous v2.23, but is otherwise identical.
DOI: 10.5281/zenodo.7361627
title: Air Quality - Genova Demo Area - Gavoglio Park

description: Conentration of PM10, PM2.5, NO<sub>2</sub>, and O3, temperature, relative humidity and pressure, measured at two different sites (inside and outside) of the DEMO area of UNaLab project in Genova
DOI: 10.5281/zenodo.7361626
title: Air Quality - Genova Demo Area - Gavoglio Park

description: Conentration of PM10, PM2.5, NO<sub>2</sub>, and O3, temperature, relative humidity and pressure, measured at two different sites (inside and outside) of the DEMO area of UNaLab project in Genova
DOI: 10.5281/zenodo.7361583
title: Genova - Rain monitoring network

description: This dataset is published into the Genova Open Data Portal and imported in the UNaLab Open Data to monitor weather data from Genova city
DOI: 10.5281/zenodo.7361582
title: Genova - Rain monitoring network

description: This dataset is published into the Genova Open Data Portal and imported in the UNaLab Open Data to monitor weather data from Genova city
DOI: 10.5281/zenodo.7361614
title: Meteo - Genova City- Various measurement points

description: Weathear condtions in several measurement points located in the area of the City of Genova Source: https://dati.comune.genova.it/Web_Service_meteo/handle_soap_request.php?mode=no_wsdl&action=read_json Column order: datetime;temperature;wind_chill;humidity;wind_speed;wind;wind_dir;pressure;rain;dew_point;rain_uh
DOI: 10.5281/zenodo.7361476
title: Genova KPIs (from City Performance Monitor)

description: This dataset contains all the GEnova KPIs monitored through the City Performance Monitor and exported in JSON format.
DOI: 10.5281/zenodo.7361613
title: Meteo - Genova City- Various measurement points

description: Weathear condtions in several measurement points located in the area of the City of Genova Source: https://dati.comune.genova.it/Web_Service_meteo/handle_soap_request.php?mode=no_wsdl&action=read_json Column order: datetime;temperature;wind_chill;humidity;wind_speed;wind;wind_dir;pressure;rain;dew_point;rain_uh
DOI: 10.5281/zenodo.7361475
title: Genova KPIs (from City Performance Monitor)

description: This dataset contains all the GEnova KPIs monitored through the City Performance Monitor and exported in JSON format.
DOI: 10.5281/zenodo.7569240
title: Simultaneous hydrogen bonds with different binding modes: the acceptor "rules" but the donor "chooses"

description: Supporting information for the paper: Simultaneous hydrogen bonds with different binding modes: the acceptor “rules” but the donor “chooses
DOI: 10.5281/zenodo.7274735
title: Simultaneous hydrogen bonds with different binding modes: the acceptor "rules" but the donor "chooses"

description: Supporting information for the paper: Simultaneous hydrogen bonds with different binding modes: the acceptor “rules” but the donor “chooses
DOI: 10.5281/zenodo.7361472
title: POIs from Open Nature Innovation Arena (Open311 standard)

description: This dataset has been generated from the Open Nature Innovation Arena according to the Open311 standard. It contains all the point of interests registered from the users into the Open Nature Innovation Arena to publish problems and challenges.
DOI: 10.5281/zenodo.7361471
title: POIs from Open Nature Innovation Arena (Open311 standard)

description: This dataset has been generated from the Open Nature Innovation Arena according to the Open311 standard. It contains all the point of interests registered from the users into the Open Nature Innovation Arena to publish problems and challenges.
DOI: 10.5281/zenodo.7361738
title: Water quality in Koukkuoja and Virolaistenoja

description: Water quality in Koukkuoja and Virolaistenoja
DOI: 10.5281/zenodo.7361737
title: Water quality in Koukkuoja and Virolaistenoja

description: Water quality in Koukkuoja and Virolaistenoja
DOI: 10.5281/zenodo.7361669
title: Air Quality - Genova City - Various measurement points

description: Concentration of various pollutants in several measurement points located in the area of the City of Genova. Source: http://www.cartografiarl.regione.liguria.it/SiraQualAria/script/Pub2AccessoDatiAria.asp
DOI: 10.5281/zenodo.7361668
title: Air Quality - Genova City - Various measurement points

description: Concentration of various pollutants in several measurement points located in the area of the City of Genova. Source: http://www.cartografiarl.regione.liguria.it/SiraQualAria/script/Pub2AccessoDatiAria.asp
DOI: 10.34740/kaggle/ds/652083
title: Tehran Stock Exchange Market Data
DOI: 10.5281/zenodo.7361413
title: Published needs from Open Nature Innovation Arena (Open311 standard)

description: This dataset has been generated from the Open Nature Innovation Arena according to the Open311 standard. It contains all the problems published from the users into the Open Nature Innovation Arena.
DOI: 10.5281/zenodo.7361412
title: Published needs from Open Nature Innovation Arena (Open311 standard)

description: This dataset has been generated from the Open Nature Innovation Arena according to the Open311 standard. It contains all the problems published from the users into the Open Nature Innovation Arena.
DOI: 10.5281/zenodo.7572379
title: Anonymous dataset for submission.

description: Anonymous dataset for submission.
DOI: 10.5281/zenodo.7572378
title: Anonymous dataset for submission.

description: Anonymous dataset for submission.
DOI: 10.5281/zenodo.7361741
title: Green Space Management - Urban Green as perceived by citizens

description: This dataset contains the results, as csv file, of the monitoring surveys of green space management as perceived by citizens. The resources will be 1) results from first survey (summer 2021) and 2) results from the 2nd survey (TBD). Some of the most interesting columns in the results Column 9 ("Quartiere") is the residential area of the citizen. Column 10 ("Indirizzo"), when available, is the street where the citizen lives Column 11, ("Freq_parco"), when available, is the frequency with which the citizen visit a green area/park in the town Column 18, ("Distanza_parco"), when available, is the distance in minutes to reach a park
DOI: 10.5281/zenodo.7361740
title: Green Space Management - Urban Green as perceived by citizens

description: This dataset contains the results, as csv file, of the monitoring surveys of green space management as perceived by citizens. The resources will be 1) results from first survey (summer 2021) and 2) results from the 2nd survey (TBD). Some of the most interesting columns in the results Column 9 ("Quartiere") is the residential area of the citizen. Column 10 ("Indirizzo"), when available, is the street where the citizen lives Column 11, ("Freq_parco"), when available, is the frequency with which the citizen visit a green area/park in the town Column 18, ("Distanza_parco"), when available, is the distance in minutes to reach a park
DOI: 10.5281/zenodo.7361509
title: Eindhoven KPIs (from City Performance Monitor)

description: This dataset contains all the Eindhoven KPIs monitored through the CIty Performance Monitor and exported in JSON format.
DOI: 10.5281/zenodo.7361508
title: Eindhoven KPIs (from City Performance Monitor)

description: This dataset contains all the Eindhoven KPIs monitored through the CIty Performance Monitor and exported in JSON format.
DOI: 10.5281/zenodo.7361501
title: Tampere KPIs (from City Performance Monitor)

description: This dataset contains all the Tampere KPIs monitored through the Tampere Performance Monitor and exported in JSON format.
DOI: 10.5281/zenodo.7361500
title: Tampere KPIs (from City Performance Monitor)

description: This dataset contains all the Tampere KPIs monitored through the Tampere Performance Monitor and exported in JSON format.
DOI: 10.5281/zenodo.7573777
title: GO-term results Epichloe

description: Significantly enriched GO-terms (<em>p</em> &lt; 0.01) for all sweeps regions and grouped by genomic compartment from the study: "<strong>Contrasting genome-wide signatures of selection in two closely related </strong><em>Epichloe </em><strong>plant pathogen species"</strong>