cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3713306
title: ZeroCostDL4Mic - Stardist example training and test dataset

description: <strong>Name</strong>: ZeroCostDL4Mic - Stardist example training and test dataset (see our Wiki for details) <strong>Data type</strong>: Paired microscopy images (fluorescence) and corresponding masks <strong>Microscopy data type</strong>: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist for details about the segmentation) <strong>Microscope</strong>: Spinning disk confocal microscope with a 20x 0.8 NA objective <strong>Cell type</strong>: DCIS.COM LifeAct-RFP cells <strong>File format</strong>: .tif (16-bit for fluorescence and 8 and 16-bit for the masks) <strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm) <strong>Author(s)</strong>: Johanna Jukkala<sup>1,2</sup> and Guillaume Jacquemet<sup>1,2</sup> <strong>Contact email</strong>: guillaume.jacquemet@abo.fi <strong>Affiliation</strong> : 1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland 2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland <strong>Associated publications</strong>: Unpublished <strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.
DOI: 10.5281/zenodo.3713307
title: ZeroCostDL4Mic - Stardist example training and test dataset

description: <strong>Name</strong>: ZeroCostDL4Mic - Stardist example training and test dataset (see our Wiki for details) <strong>Data type</strong>: Paired microscopy images (fluorescence) and corresponding masks <strong>Microscopy data type</strong>: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist for details about the segmentation) <strong>Microscope</strong>: Spinning disk confocal microscope with a 20x 0.8 NA objective <strong>Cell type</strong>: DCIS.COM LifeAct-RFP cells <strong>File format</strong>: .tif (16-bit for fluorescence and 8 and 16-bit for the masks) <strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm) <strong>Author(s)</strong>: Johanna Jukkala<sup>1,2</sup> and Guillaume Jacquemet<sup>1,2</sup> <strong>Contact email</strong>: guillaume.jacquemet@abo.fi <strong>Affiliation</strong> : 1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland 2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland <strong>Associated publications</strong>: Unpublished <strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.
DOI: 10.5281/zenodo.3713321
title: Dataset related to article "Regorafenib in patients with refractory metastatic pancreatic cancer: a Phase II study (RESOUND)."

description: <strong>Aim:</strong> Regorafenib may be active in different cancer types. This Phase II trial included patients with various refractory cancer types treated with regorafenib. Here, we report the results of the pancreatic adenocarcinoma cohort. <strong>Methods:</strong> The primary end point was progression-free survival (PFS) rate at 8 weeks; further investigation of regorafenib would be warranted with a PFS rate ≥50%. <strong>Results:</strong> A total of 20 patients were enrolled. The best response was stable disease in four patients (20%). The 8-week PFS rate was 25% with a median PFS of 1.7 months (95% CI: 1.5-2.0). A total of 13 patients (65%) experienced grade 3-4 treatment-related adverse events. <strong>Conclusion:</strong> The study did not meet its primary end point. Further investigation of regorafenib monotherapy in this setting is not recommended. <strong>Clinical Trial Registration:</strong> NCT02307500.
DOI: 10.5281/zenodo.3713322
title: Dataset related to article "Regorafenib in patients with refractory metastatic pancreatic cancer: a Phase II study (RESOUND)."

description: <strong>Aim:</strong> Regorafenib may be active in different cancer types. This Phase II trial included patients with various refractory cancer types treated with regorafenib. Here, we report the results of the pancreatic adenocarcinoma cohort. <strong>Methods:</strong> The primary end point was progression-free survival (PFS) rate at 8 weeks; further investigation of regorafenib would be warranted with a PFS rate ≥50%. <strong>Results:</strong> A total of 20 patients were enrolled. The best response was stable disease in four patients (20%). The 8-week PFS rate was 25% with a median PFS of 1.7 months (95% CI: 1.5-2.0). A total of 13 patients (65%) experienced grade 3-4 treatment-related adverse events. <strong>Conclusion:</strong> The study did not meet its primary end point. Further investigation of regorafenib monotherapy in this setting is not recommended. <strong>Clinical Trial Registration:</strong> NCT02307500.
DOI: 10.17632/8g2zhd4fc2.1
title: Activation regional brain changes associated with language treatment inData for Post-stroke Aphasia: an activation likelihood estimation meta-analysis

description: There are an increasing number of neuroimaging studies that can better understand the efficacy and neural correlation of language therapy in post-stroke aphasia (PSA) patients to date. However, the results of these studies were difficult to compare as they included PSA patients at acute to chronic stages of the disease and employed different language therapy methods, so the statistical results of neuroimaging data showed a high degree of variability.
DOI: 10.5281/zenodo.3713325
title: ZeroCostDL4Mic - Noise2Void (3D) example training and test dataset

description: <strong>Name</strong>: ZeroCostDL4Mic - Noise2Void (3D) example training and test dataset (see our Wiki for details) <strong>Data type</strong>: 3D microscopy images (fluorescence) <strong>Microscopy data type</strong>: Fluorescence microscopy (Lifeact-RFP) <strong>Microscope</strong>: Spinning disk confocal microscope with a 63x 1.4 NA objective <strong>Cell type</strong>: A2780 ovarian carcinoma cells, transiently expressing Lifeact-RFP <strong>File format</strong>: .tif (16-bit) <strong>Image size</strong>: 512x512x13 (Pixel size: x,y: 195 nm , z: 500 nm) <strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2</sup> <strong>Contact email</strong>: guillaume.jacquemet@abo.fi <strong>Affiliation</strong>: 1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland 2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland <strong>Associated publications</strong>: Kaukonen <em>et al.</em> 2017, Nature Protocol DOI: 10.1038/nprot.2017.107 <strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.
DOI: 10.5281/zenodo.3713326
title: ZeroCostDL4Mic - Noise2Void (3D) example training and test dataset

description: <strong>Name</strong>: ZeroCostDL4Mic - Noise2Void (3D) example training and test dataset (see our Wiki for details) <strong>Data type</strong>: 3D microscopy images (fluorescence) <strong>Microscopy data type</strong>: Fluorescence microscopy (Lifeact-RFP) <strong>Microscope</strong>: Spinning disk confocal microscope with a 63x 1.4 NA objective <strong>Cell type</strong>: A2780 ovarian carcinoma cells, transiently expressing Lifeact-RFP <strong>File format</strong>: .tif (16-bit) <strong>Image size</strong>: 512x512x13 (Pixel size: x,y: 195 nm , z: 500 nm) <strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2</sup> <strong>Contact email</strong>: guillaume.jacquemet@abo.fi <strong>Affiliation</strong>: 1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland 2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland <strong>Associated publications</strong>: Kaukonen <em>et al.</em> 2017, Nature Protocol DOI: 10.1038/nprot.2017.107 <strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.
DOI: 10.15468/m2g26b
title: On the genera Ptiloneura Enderlein, Loneura Navás, and Loneuroides García Aldrete (Psocodea: ‘ Psocoptera’: Ptiloneuridae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article García Aldrete, Alfonso N., Obando, Ranulfo González, Gironza, Nancy Carrejo (2020): On the genera Ptiloneura Enderlein, Loneura Navás, and Loneuroides García Aldrete (Psocodea: ‘ Psocoptera’: Ptiloneuridae). Zootaxa 4751 (2): 291-309, DOI: https://doi.org/10.11646/zootaxa.4751.2.5
DOI: 10.15468/yuxmg2
title: The invertebrate host of salmonid fish parasites Ceratonova shasta and Parvicapsula minibicornis (Cnidaria: Myxozoa), is a novel fabriciid annelid, Manayunkia occidentalis sp. nov. (Sabellida: Fabriciidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Atkinson, Stephen D., Bartholomew, Jerri L., Rouse, Greg W. (2020): The invertebrate host of salmonid fish parasites Ceratonova shasta and Parvicapsula minibicornis (Cnidaria: Myxozoa), is a novel fabriciid annelid, Manayunkia occidentalis sp. nov. (Sabellida: Fabriciidae). Zootaxa 4751 (2): 310-320, DOI: https://doi.org/10.11646/zootaxa.4751.2.6
DOI: 10.15468/698bq9
title: Revision of the western Palaearctic species of Aleiodes Wesmael (Hymenoptera, Braconidae, Rogadinae). Part 2: Revision of the A. apicalis group

description: This dataset contains the digitized treatments in Plazi based on the original journal article van Achterberg, Cornelis, Shaw, Mark R., Quicke, Donald L. J. (2020): Revision of the western Palaearctic species of Aleiodes Wesmael (Hymenoptera, Braconidae, Rogadinae). Part 2: Revision of the A. apicalis group. ZooKeys 919: 1-259, DOI: http://dx.doi.org/10.3897/zookeys.919.39642, URL: http://dx.doi.org/10.3897/zookeys.919.39642
DOI: 10.17632/ys29nwvt75.1
title: HMG2D: A tool to identify miRNAs/Drugs/Genes associated with Diseases like Cancers

description: HMG2D-analyzer is a java based biological tool intended to find common links between two pathologically significant databases, and further use the derived results to predict correlation between two analytes that may otherwise be unrelated. The attached user guide outlines the procedure which needs to be followed to use this tool efficiently and extract desired results. No prior programming knowledge is required to use this tool. And hmg2d-analyzer works offline as a standalone desktop application.
DOI: 10.15468/ds8cxy
title: Redescription of Parotocinclus haroldoi Garavello, 1988 (Siluriformes: Loricariidae), Northeastern Brazil

description: This dataset contains the digitized treatments in Plazi based on the original journal article Ramos, Telton Pedro Anselmo, Neto, Luciano De Freitas Barros, Ferreira, Karina Carvalho Fernandes, Barbosa, José Etham De Lucena (2020): Redescription of Parotocinclus haroldoi Garavello, 1988 (Siluriformes: Loricariidae), Northeastern Brazil. Zootaxa 4751 (2): 321-332, DOI: https://doi.org/10.11646/zootaxa.4751.2.7
DOI: 10.17632/fw86ngty6c.1
title: Data on synthesis and characterization of new p-nitro stilbene Schiff basesderivatives as an electrochemical DNA potential spacer

description: Data for Figure 1, 2, 4, 6, 8 and 10 for a paper to be submitted in data in brief journal. These data describe the values in spectroscopic analysis which are Fourier transform infra red (FTIR), UV-Vis spectrophotometer, Nuclear magnetic resonance (NMR).
DOI: 10.15468/vsz4m9
title: New species of the genus Caloxiphus Saussure & Pictet, 1898 (Orthoptera: Tettigoniidae) from the Huasteca Region, Hidalgo, Mexico

description: This dataset contains the digitized treatments in Plazi based on the original journal article Barrientos-Lozano, Ludivina, Fernández-Azuara, Geovany J., Rocha-Sánchez, Aurora Y., Zaldívar-Riverón, Alejandro, Sánchez-Reyes, Uriel Jeshua, Almaguer-Sierra, Pedro (2020): New species of the genus Caloxiphus Saussure & Pictet, 1898 (Orthoptera: Tettigoniidae) from the Huasteca Region, Hidalgo, Mexico. Zootaxa 4751 (2): 333-344, DOI: https://doi.org/10.11646/zootaxa.4751.2.8
DOI: 10.15468/ae4hl8
title: Two new genera and two new species of Conopidae (Diptera) from China

description: This dataset contains the digitized treatments in Plazi based on the original journal article Stuke, Jens-Hermann (2019): Two new genera and two new species of Conopidae (Diptera) from China. Contributions to Entomology 69 (2): 301-317, DOI: 10.21248/contrib.entomol.69.2.301-317
DOI: 10.17632/brx6gb5887.1
title: Details of the specification of different MDR pattern (P1-6) according to the antibiotypes of the isolates

description: Details of the different MDR pattern and antibiotypes of E. coli of healthy ostrich and poultry with colibacillosis
DOI: 10.17632/zbn22gnn3c.2
title: Details of the data of the antibiotic sensibility testing of E. coli of healthy ostrich and poultry with colibacillosis

description: Details of the data of the antibiotic sensibility testing
DOI: 10.17632/zbn22gnn3c.1
title: Details of the data of the antibiotic sensibility testing of E. coli of healthy ostrich and poultry with colibacillosis

description: Details of the data of the antibiotic sensibility testing
DOI: 10.5281/zenodo.3693041
title: MOTIVE - tiMe-OpTimized contextual Information flow on unmanned VEhicles project experimental results

description: The experimentation data were collected during the 6th Fed4FIRE+ Open Call (https://www.fed4fire.eu/) using the mobile nodes of the w-iLab.t (link) testbed. During this Open Call we proposed to evaluate the performance of an optimization model for temporal control of the transmission of messages from an IoT mobile device. This mechanism is based on a network condition model that transits from favourable to adverse conditions and vice versa. All these transitions are monitored and validated through our system (change detection and optimal stopping). If a network is performing properly then the transmission control can be relaxed to exploit available resources.The main contribution of MOTIVE is to apply a sequential decision-making process (DMP) on IoT devices that leverages the on-line derived network statistics to efficiently control their telemetry measurements transmission. Our data collected from the experimentation using the testbed's devices consists of network related information, packet error rate and latency. We collected data from a completely functional mobile node that operates in a saturated network and five mobile nodes in the same circumstances. Saturated conditions were generated by data produced by twenty static sensors for the duration of the each experiment. On each run we collected more than 2 * 105 samples. The comparative assessment we did was based on five different policies of decision making: i) no-policy model, ii) the heuristic threshold based model in which the transmission of the messages is paused when a specific threshold of quality network is below a threshold, iii) TOCP model which overviews the quality of network in normal mode and gets in pausing mode when a change is detected; the pausing period lasts for a specific threshold and then it is activated again, iv) TOCP-DRP and the v) fair TOCP-DRP which enters in pausing mode if a change is indicated by TOCP decision making model. The datasets include the packet error rate and the latency of each device, for each experiment, and the critical areas found during the experiments (most saturated areas). The datatset includes for each scenario(experiment) the participating devices and the Latency and Packet Error Rate(PER) per policy( NoPolicy, Heuristic, TOCP, TOCPDRP, TOCPDRPFAIR), apart from scenarioA(single node) where only four policies are present, since the TOCPDRPFAIR applies with more that one participating nodes.
DOI: 10.5281/zenodo.3693042
title: MOTIVE - tiMe-OpTimized contextual Information flow on unmanned VEhicles project experimental results

description: The experimentation data were collected during the 6th Fed4FIRE+ Open Call (https://www.fed4fire.eu/) using the mobile nodes of the w-iLab.t (link) testbed. During this Open Call we proposed to evaluate the performance of an optimization model for temporal control of the transmission of messages from an IoT mobile device. This mechanism is based on a network condition model that transits from favourable to adverse conditions and vice versa. All these transitions are monitored and validated through our system (change detection and optimal stopping). If a network is performing properly then the transmission control can be relaxed to exploit available resources.The main contribution of MOTIVE is to apply a sequential decision-making process (DMP) on IoT devices that leverages the on-line derived network statistics to efficiently control their telemetry measurements transmission. Our data collected from the experimentation using the testbed's devices consists of network related information, packet error rate and latency. We collected data from a completely functional mobile node that operates in a saturated network and five mobile nodes in the same circumstances. Saturated conditions were generated by data produced by twenty static sensors for the duration of the each experiment. On each run we collected more than 2 * 105 samples. The comparative assessment we did was based on five different policies of decision making: i) no-policy model, ii) the heuristic threshold based model in which the transmission of the messages is paused when a specific threshold of quality network is below a threshold, iii) TOCP model which overviews the quality of network in normal mode and gets in pausing mode when a change is detected; the pausing period lasts for a specific threshold and then it is activated again, iv) TOCP-DRP and the v) fair TOCP-DRP which enters in pausing mode if a change is indicated by TOCP decision making model. The datasets include the packet error rate and the latency of each device, for each experiment, and the critical areas found during the experiments (most saturated areas). The datatset includes for each scenario(experiment) the participating devices and the Latency and Packet Error Rate(PER) per policy( NoPolicy, Heuristic, TOCP, TOCPDRP, TOCPDRPFAIR), apart from scenarioA(single node) where only four policies are present, since the TOCPDRPFAIR applies with more that one participating nodes.
DOI: 10.15468/xsj2nb
title: A new stiletto fly genus from South America (Diptera: Therevidae: Agapophytinae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Irwin, Michael E., Winterton, Shaun L. (2020): A new stiletto fly genus from South America (Diptera: Therevidae: Agapophytinae). Zootaxa 4751 (2): 276-290, DOI: https://doi.org/10.11646/zootaxa.4751.2.4
DOI: 10.5281/zenodo.3713186
title: Data for stats.stackexchange question

description: Data for stats.stackexchange question: https://stats.stackexchange.com/q/454499/101464
DOI: 10.5281/zenodo.3713187
title: Data for stats.stackexchange question

description: Data for stats.stackexchange question: https://stats.stackexchange.com/q/454499/101464
DOI: 10.5281/zenodo.3713330
title: ZeroCostDL4Mic - CARE (2D) example training and test dataset

description: <strong>Name</strong>: ZeroCostDL4Mic - CARE (2D) example training and test dataset (see our Wiki for details) <strong>Data type</strong>: Paired microscopy images (fluorescence) of low and high signal-to-noise ratio <strong>Microscopy data type</strong>: Fluorescence microscopy (Lifeact-RFP) <strong>Microscope</strong>: Structured Illumination Microscopy (SIM) with a 60x 1.42 NA objective <strong>Cell type</strong>: DCIS.COM Lifeact-RFP <strong>File format</strong>: .tif (32-bit) <strong>Image size</strong>: 1024x1024 (Pixel size: 40 nm) <strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2</sup> <strong>Contact email</strong>: guillaume.jacquemet@abo.fi <strong>Affiliation</strong>: 1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland 2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland <strong>Associated publications</strong>: Unpublished <strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.
DOI: 10.5281/zenodo.3713329
title: ZeroCostDL4Mic - CARE (2D) example training and test dataset

description: <strong>Name</strong>: ZeroCostDL4Mic - CARE (2D) example training and test dataset (see our Wiki for details) <strong>Data type</strong>: Paired microscopy images (fluorescence) of low and high signal-to-noise ratio <strong>Microscopy data type</strong>: Fluorescence microscopy (Lifeact-RFP) <strong>Microscope</strong>: Structured Illumination Microscopy (SIM) with a 60x 1.42 NA objective <strong>Cell type</strong>: DCIS.COM Lifeact-RFP <strong>File format</strong>: .tif (32-bit) <strong>Image size</strong>: 1024x1024 (Pixel size: 40 nm) <strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2</sup> <strong>Contact email</strong>: guillaume.jacquemet@abo.fi <strong>Affiliation</strong>: 1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland 2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland <strong>Associated publications</strong>: Unpublished <strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.
DOI: 10.5281/zenodo.3689193
title: Monitoring clearcutting and subsequent rapid recovery in Mediterranean coppice forests with Landsat time series

description: This dataset considers information from clear-cut coppice forest occurred between 1999 and 2015 in three different area of Tuscany Region (Italy). The dataset consists in one shape file and two csv files. The shape file contains in 2371 spatial polygons of clear-cut occurred in the three area between 1999 and 2015. The polygons were generated using visual interpretation of the Landsat Time Series and high resolution regional ortomosaic. Photo interpreters delineated the spatial extent of each clear-cut and recorded the year of harvest, adopting a minimum mapping unit of 0.1 hectares. The database associated with shape file report information related with area extent of clear-cut, year of clear-cut and the forest types that is associated with the clear-cut derived by the Tucant Regional Forest Inventory (RFI) data that give information on dominant species. The .csv files reported the temporal characteristic of clear-cut based on Landsat Time Series data and based on Airborne Laser Scanner (ALS) Canopy Height Model (CHM), in order to describe the temporal trend of clear-cut based on remote sensing data.
description: {"references": ["Chirici G., Giannetti F., Mazza E., Francini S., Travaglini D., Pegna R., White J.C. (2020). Monitoring clearcutting and subsequent rapid recovery in Mediterranean coppice forests with Landsat time series, Annals of Forest Science, doi: 10.1007/s13595-020-00936-2"]}