cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6449127
title: IMC Segmentation Pipeline results of example IMC data

description: This repository hosts the results of processing example imaging mass cytometry (IMC) data hosted at 10.5281/zenodo.5949116 using the IMC Segmentation Pipeline available at https://github.com/BodenmillerGroup/ImcSegmentationPipeline (DOI: 10.5281/zenodo.6402666). Please refer to https://github.com/BodenmillerGroup/steinbock as alternative processing framework and 10.5281/zenodo.6043600 for the data generated by <em>steinbock</em>. The following files are part of the <strong>analysis.zip</strong> folder when running the IMC Segmentation Pipeline: <strong>cpinp</strong>: contains input files for the segmentation pipeline <strong>cpout</strong>: contains all final output files of the pipeline: <em>cell.csv</em> containing the single-cell features; <em>Experiment.csv</em> containing CellProfiler metadata; <em>Image.csv</em> containing acquisition metadata; <em>Object relationships.csv</em> containing an edge list indicating interacting cells; <em>panel.csv</em> containing channel information; <em>var_cell.csv</em> containing cell feature information; <em>var_Image.csv</em> containing acquisition feature information; <em>images </em>containing the hot pixel filtered multi-channel images and the channel order; <em>masks</em> containing the segmentation masks; <em>probabilities </em>containing the pixel probabilities. <strong>histocat</strong>: contains single channel .tiff files per acquisition for upload to histoCAT (https://bodenmillergroup.github.io/histoCAT/) <strong>crops</strong>: contains upscaled image crops in .h5 format for ilastik (https://www.ilastik.org/) training <strong>ometiff</strong>: contains .ome.tiff files per acquisition, .png files per panorama and additional metadata files per slide <strong>ilastik</strong>: multi channel images for ilastik pixel classification (<em>_ilastik.full</em>) and their channel order (<em>_ilastik.csv</em>); upscaled multi channel images for ilastik pixel prediction (<em>_ilastik_s2.h5</em>); upscaled 3 channel images containing ilastik pixel probabilities (<em>_ilastik_s2_Probabilities.tiff</em>). The remaining files are part of the root directory: <strong>docs.zip: </strong>Documentation of the pipeline in markdown format I<strong>MCWorkflow.ilp: </strong>Ilastik pixel classifier pre-trained on the example data <strong>resources.zip: </strong>The CellProfiler pipelines and CellProfiler plugins used for the analysis <strong>sample_metadata.xlsx: </strong>Metadata per sample including the cancer type <strong>scripts.zip: </strong>Python notebooks used for pre-processing and downloading the example data <strong>src.zip: </strong>Scripts for the imcsegpipe python package
DOI: 10.5281/zenodo.6043543
title: IMC Segmentation Pipeline results of example IMC data

description: If you are working with these files, please cite them as follows:Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). https://doi.org/10.1038/s41596-023-00881-0
This repository hosts the results of processing example imaging mass cytometry (IMC) data hosted at 10.5281/zenodo.5949116 using the IMC Segmentation Pipeline available at https://github.com/BodenmillerGroup/ImcSegmentationPipeline (DOI: 10.5281/zenodo.6402666) v3.6. Please refer to https://github.com/BodenmillerGroup/steinbock as alternative processing framework and 10.5281/zenodo.6043600 for the data generated by steinbock.
The following files are part of the analysis.zip folder when running the IMC Segmentation Pipeline:

cpinp: contains input files for the segmentation pipeline
cpout: contains all final output files of the pipeline: cell.csv containing the single-cell features; Experiment.csv containing CellProfiler metadata; Image.csv containing acquisition metadata; Object relationships.csv containing an edge list indicating interacting cells; panel.csv containing channel information; var_cell.csv containing cell feature information; var_Image.csv containing acquisition feature information; images containing the hot pixel filtered multi-channel images and the channel order; masks containing the segmentation masks; probabilities containing the pixel probabilities.
histocat: contains single channel .tiff files per acquisition for upload to histoCAT (https://bodenmillergroup.github.io/histoCAT/)
crops: contains upscaled image crops in .h5 format for ilastik (https://www.ilastik.org/) training
ometiff: contains .ome.tiff files per acquisition, .png files per panorama and additional metadata files per slide
ilastik: multi channel images for ilastik pixel classification (_ilastik.full) and their channel order (_ilastik.csv); upscaled multi channel images for ilastik pixel prediction (_ilastik_s2.h5); upscaled 3 channel images containing ilastik pixel probabilities (_ilastik_s2_Probabilities.tiff).
The remaining files are part of the root directory:

docs.zip: Documentation of the pipeline in markdown format
IMCWorkflow.ilp: Ilastik pixel classifier pre-trained on the example data
resources.zip: The CellProfiler pipelines and CellProfiler plugins used for the analysis
scripts.zip: Python notebooks used for pre-processing and downloading the example data
src.zip: Scripts for the imcsegpipe python package
DOI: 10.5281/zenodo.6402763
title: IMC Segmentation Pipeline results of example IMC data

description: This repository hosts the results of processing example imaging mass cytometry (IMC) data hosted at 10.5281/zenodo.5949116 using the IMC Segmentation Pipeline available at https://github.com/BodenmillerGroup/ImcSegmentationPipeline. Please refer to https://github.com/BodenmillerGroup/steinbock as alternative processing framework and 10.5281/zenodo.6043600 for the data generated by <em>steinbock</em>. The following files are part of the "analysis" folder when running the IMC Segmentation Pipeline: <strong>cpinp.zip</strong>: contains input files for the segmentation pipeline <strong>cpout.zip</strong>: contains all final output files of the pipeline: <em>cell.csv</em> containing the single-cell features; <em>Experiment.csv</em> containing CellProfiler metadata; <em>Image.csv</em> containing acquisition metadata; <em>Object relationships.csv</em> containing an edge list indicating interacting cells; <em>panel.csv</em> containing channel information; <em>var_cell.csv</em> containing cell feature information; <em>var_Image.csv</em> containing acquisition feature information <strong>histocat.zip</strong>: contains single channel .tiff files per acquisition for upload to histoCAT (https://bodenmillergroup.github.io/histoCAT/) <strong>ilastik.zip</strong>: contains upscaled image crops in .h5 format for ilastik (https://www.ilastik.org/) training <strong>ometiff.zip</strong>: contains .ome.tiff files per acquisition, .png files per panorama and additional metadata files per slide <strong>tiffs.zip</strong>: contains multi channel .tiff files of all channels for analysis (<em>_full.tiff</em>) and their channel order (<em>_full.csv</em>); multi channel images for ilastik pixel classification (<em>_ilastik.full</em>) and their channel order (<em>_ilastik.csv</em>); upscaled multi channel images for ilastik pixel prediction (<em>_ilastik_s2.h5</em>); 3 channel images containing ilastik pixel probabilities (<em>_ilastik_s2_Probabilities.tiff</em>) and segmentation masks (<em>_ilastik_s2_Probabilities_mask.tiff</em>). The remaining files are part of the root directory: <strong>config.zip</strong>: containing the antibody panel used for the experiment. <strong>cp4_pipelines.zip</strong>: containing the CellProfiler pipelines for analysis. <strong>documentation.zip</strong>: containing a description of the IMC Segmentation Pipeline <strong>scripts.zip</strong>: containing a jupyter notebook to perform image pre-processing <strong>setup.zip</strong>: containing a .yml describing the dependencies to be installed via conda
DOI: 10.5281/zenodo.6404960
title: IMC Segmentation Pipeline results of example IMC data

description: This repository hosts the results of processing example imaging mass cytometry (IMC) data hosted at 10.5281/zenodo.5949116 using the IMC Segmentation Pipeline available at https://github.com/BodenmillerGroup/ImcSegmentationPipeline (DOI: 10.5281/zenodo.6402666). Please refer to https://github.com/BodenmillerGroup/steinbock as alternative processing framework and 10.5281/zenodo.6043600 for the data generated by <em>steinbock</em>. The following files are part of the <strong>analysis.zip</strong> folder when running the IMC Segmentation Pipeline: <strong>cpinp</strong>: contains input files for the segmentation pipeline <strong>cpout</strong>: contains all final output files of the pipeline: <em>cell.csv</em> containing the single-cell features; <em>Experiment.csv</em> containing CellProfiler metadata; <em>Image.csv</em> containing acquisition metadata; <em>Object relationships.csv</em> containing an edge list indicating interacting cells; <em>panel.csv</em> containing channel information; <em>var_cell.csv</em> containing cell feature information; <em>var_Image.csv</em> containing acquisition feature information; <em>images </em>containing the hot pixel filtered multi-channel images and the channel order; <em>masks</em> containing the segmentation masks; <em>probabilities </em>containing the pixel probabilities. <strong>histocat</strong>: contains single channel .tiff files per acquisition for upload to histoCAT (https://bodenmillergroup.github.io/histoCAT/) <strong>crops</strong>: contains upscaled image crops in .h5 format for ilastik (https://www.ilastik.org/) training <strong>ometiff</strong>: contains .ome.tiff files per acquisition, .png files per panorama and additional metadata files per slide <strong>ilastik</strong>: multi channel images for ilastik pixel classification (<em>_ilastik.full</em>) and their channel order (<em>_ilastik.csv</em>); upscaled multi channel images for ilastik pixel prediction (<em>_ilastik_s2.h5</em>); upscaled 3 channel images containing ilastik pixel probabilities (<em>_ilastik_s2_Probabilities.tiff</em>). The remaining files are part of the root directory: <strong>docs.zip: </strong>Documentation of the pipeline in markdown format I<strong>MCWorkflow.ilp: </strong>Ilastik pixel classifier pre-trained on the example data <strong>resources.zip: </strong>The CellProfiler pipelines and CellProfiler plugins used for the analysis <strong>sample_metadata.xlsx: </strong>Metadata per sample including the cancer type <strong>scripts.zip: </strong>Python notebooks used for pre-processing and downloading the example data <strong>src.zip: </strong>Scripts for the imcsegpipe python package
DOI: 10.5281/zenodo.7997296
title: IMC Segmentation Pipeline results of example IMC data

description: If you are working with these files, please cite them as follows:Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). https://doi.org/10.1038/s41596-023-00881-0
This repository hosts the results of processing example imaging mass cytometry (IMC) data hosted at 10.5281/zenodo.5949116 using the IMC Segmentation Pipeline available at https://github.com/BodenmillerGroup/ImcSegmentationPipeline (DOI: 10.5281/zenodo.6402666) v3.6. Please refer to https://github.com/BodenmillerGroup/steinbock as alternative processing framework and 10.5281/zenodo.6043600 for the data generated by steinbock.
The following files are part of the analysis.zip folder when running the IMC Segmentation Pipeline:

cpinp: contains input files for the segmentation pipeline
cpout: contains all final output files of the pipeline: cell.csv containing the single-cell features; Experiment.csv containing CellProfiler metadata; Image.csv containing acquisition metadata; Object relationships.csv containing an edge list indicating interacting cells; panel.csv containing channel information; var_cell.csv containing cell feature information; var_Image.csv containing acquisition feature information; images containing the hot pixel filtered multi-channel images and the channel order; masks containing the segmentation masks; probabilities containing the pixel probabilities.
histocat: contains single channel .tiff files per acquisition for upload to histoCAT (https://bodenmillergroup.github.io/histoCAT/)
crops: contains upscaled image crops in .h5 format for ilastik (https://www.ilastik.org/) training
ometiff: contains .ome.tiff files per acquisition, .png files per panorama and additional metadata files per slide
ilastik: multi channel images for ilastik pixel classification (_ilastik.full) and their channel order (_ilastik.csv); upscaled multi channel images for ilastik pixel prediction (_ilastik_s2.h5); upscaled 3 channel images containing ilastik pixel probabilities (_ilastik_s2_Probabilities.tiff).
The remaining files are part of the root directory:

docs.zip: Documentation of the pipeline in markdown format
IMCWorkflow.ilp: Ilastik pixel classifier pre-trained on the example data
resources.zip: The CellProfiler pipelines and CellProfiler plugins used for the analysis
scripts.zip: Python notebooks used for pre-processing and downloading the example data
src.zip: Scripts for the imcsegpipe python package
DOI: 10.5281/zenodo.10210494
title: IODP Expedition 391 P-wave velocity caliper (section/discrete)

description: P-wave velocity data were measured on undisturbed section halves (JRSO-defined x-axis) and/or discrete cube and cylinder samples (x, y, or z-axis) using pairs of piezoelectric transducers mounted on a caliper system. Report includes P-wave velocity in x, y, and/or z-direction, caliper separation, traveltime between transucers, and first arrival picks.
DOI: 10.5281/zenodo.10210495
title: IODP Expedition 391 P-wave velocity caliper (section/discrete)

description: P-wave velocity data were measured on undisturbed section halves (JRSO-defined x-axis) and/or discrete cube and cylinder samples (x, y, or z-axis) using pairs of piezoelectric transducers mounted on a caliper system. Report includes P-wave velocity in x, y, and/or z-direction, caliper separation, traveltime between transucers, and first arrival picks.
DOI: 10.6084/m9.figshare.24639099
title: 20231128_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.24639099.v1
title: 20231128_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.5281/zenodo.10156134
title: Data for Kinetic Effect of In Situ Water Adsorption during CO2 Hydrogenation: An Experimental Investigation
DOI: 10.5281/zenodo.10156135
title: Data for Kinetic Effect of In Situ Water Adsorption during CO2 Hydrogenation: An Experimental Investigation
DOI: 10.6084/m9.figshare.24638670
title: [dataset]biochemical_assays

description: Sheets and graphics of biochemical analysis (proteins, enzymes and oxidative stress) from liver of fish <i>Poecilia reticulata</i> exposed to maghemite iron oxide nanoparticles and associations of IONP and glyphosate.
DOI: 10.6084/m9.figshare.24638670.v2
title: [dataset]biochemical_assays

description: Sheets and graphics of biochemical analysis (proteins, enzymes and oxidative stress) from liver of fish <i>Poecilia reticulata</i> exposed to maghemite iron oxide nanoparticles and associations of IONP and glyphosate.
DOI: 10.6084/m9.figshare.24638670.v1
title: [dataset]biochemical_assays

description: Sheets and graphics of biochemical analysis (proteins, enzymes and oxidative stress) from liver of fish <i>Poecilia reticulata</i> exposed to maghemite iron oxide nanoparticles and associations of IONP and glyphosate.
DOI: 10.5281/zenodo.10116376
title: co creation process Answers questionnaire
DOI: 10.5281/zenodo.10116375
title: co creation process Answers questionnaire
DOI: 10.6084/m9.figshare.24461242.v1
title: Supplementary Material for: Hepatocellular Carcinoma Patients with Hepatitis B Virus Infection Exhibited Favorable Survival from Immune Checkpoint Inhibitors: A Systematic Review and Meta-analysis

description: Background Immune checkpoint inhibitors (ICIs) have demonstrated effectiveness for advanced hepatocellular carcinoma (HCC). However, the discrepancy in the efficacy of ICIs in HCC patients with distinct etiologies has not been systematically validated. Methods PubMed, MEDLINE, EMBASE, clinicaltrials.gov and abstracts from ASCO and ESMO conferences were searched for eligible trials that explored the impact of etiology factor on the ICI treatment in HCC patients. The pooled hazard ratio (HR) of overall survival (OS) and progression-free survival (PFS), as well as the pooled odds ratio (OR) of objective response rate (ORR) were calculated with stratification of hepatitis B virus (HBV), hepatitis C virus (HCV) and nonviral subgroup, and the heterogeneity between different etiological subgroup were assessed by using an interaction test. Results Eight eligible studies with a total of 5646 patients were identified from searching published articles and conference abstracts. ICI therapies were associated with significantly prolonged OS with the pooled HRs of 0.78 (95%CI 0.73-0.84, p &lt; 0.001), 0.71 (95%CI 0.65-0.79, p &lt; 0.001), 0.80 (95%CI 0.69-0.93, p = 0.003) and 0.87 (95%CI 0.77-0.97, p = 0.011) for the whole population, HBV subgroup, HCV subgroup and non-viral subgroup, respectively. In addition, this analysis reported a significant PFS improvement with ICI therapies with HRs of 0.78 (p = 0.004), 0.53 (p &lt; 0.001), 0.65 (p = 0.011) and 0.81 (p = 0.107) for whole population, HBV, HCV and nonviral subgroup, respectively. The HBV-related HCC patients showed the more distinctive HRs for OS and PFS than other etiology subgroups, and this difference was significant in PFS (p for heterogeneity = 0.001) and there was a tendency of significance in OS (p for heterogeneity = 0.079). Furthermore, the ORR advantages of ICI therapies over control were also confirmed with the pooled ORs of 3.62 (p &lt; 0.001), 3.84 (p &lt; 0.001), 3.05 (p &lt; 0.001) and 2.99 (p &lt; 0.001) for whole population, HBV, HCV and nonviral subgroup, respectively (p for heterogeneity = 0.743). Conclusions ICI therapies significantly improve OS, PFS and ORR for HCC patients with different etiologies. HBV-related HCC patients could be the highlighted population to benefit from ICI treatment.
DOI: 10.6084/m9.figshare.24609027
title: Supplementary Material for: Machine Learning Facial Emotion Classifiers in Psychotherapy Research. A proof-of-concept study.

description: Background: New advances in the field of machine learning make it possible to track facial emotional expression with high resolution, including micro-expressions. These advances have promising applications for psychotherapy research, since manual coding (e.g. the Facial Action Coding System [FACS]), is time-consuming. Purpose: We tested whether this technology can reliably identify in-session emotional expression in a naturalistic treatment setting, and how these measures relate to the outcome of psychotherapy. Method: We applied a machine learning emotion classifier to video material from 389 psychotherapy sessions of 23 patients with borderline personality pathology. We validated the findings with human ratings according to the Clients Emotional Arousal Scale (CEAS) and explored associations with treatment outcomes. Results: Overall, machine learning ratings showed significant agreement with human ratings. Machine learning emotion classifiers, particularly the display of positive emotions (smiling and happiness), showed medium effect size on median-split treatment outcome (d=0.3) as well as continuous improvement (r=0.49, p&lt;.05). Patients who dropped out form psychotherapy, showed significantly more neutral expressions, and generally less social smiling, particularly at the beginning of psychotherapeutic sessions. Conclusions: Machine learning classifiers are a highly promising resource for research in psychotherapy. The results highlight differential associations of displayed positive and negative feelings with treatment outcomes. Machine learning emotion recognition may be used for the early identification of drop-out risks and clinically relevant interactions in psychotherapy.
DOI: 10.21227/5jbx-qh37
title: DoE and regression analysis input and output

description: This dataset contains the input and output data from an industrial case study aiming to detect undesired non-intuitive behavior in an engineered complex system (an Autonomous Surface Vessel (ASV) on a Search and Rescue (SAR) mission). We used the Taguchi method to set up experiments, conducted the experiments in a case company specific test arena, and performed different forms of regression analysis. The paper titled A Novel Approach to Detect Detrimental Weak Emergent Behavior Considering Noise Factors: an Autonomous Search and Rescue Case Study uses this data, but does not show the complete set. The data is partially obtained from the case company's subject matter experts and partially generated from the statistical tool Minitab.
DOI: 10.6084/m9.figshare.24611907
title: Supplementary Material for: Non-Pathogenic Aspergillus Oryzae Acute Exogenous Endophthalmitis after Penetrating Keratoplasty: The first case report in the literature.

description: The authors report a singular case of post-operative exogenous fungal endophthalmitis caused by a non-pathogenic fungal agent: the Aspergillus Oryzae. A 75-year-old Caucasian woman with post-penetrating keratoplasty fungal endophthalmitis due to a non-pathogenic Aspergillus Oryzae, resistant to the current azoles anti-fungal agents, was treated with subtotal vitrectomy, intravitreal injection and systemic Voriconazole therapy. Complete resolution of the endophthalmitis occurred after two subsequent intravitreal injections and 2 months long systemic delivery of Voriconazole. The quick identification of the fungal agent allowed immediate and targeted therapy. In the article, the safety and efficacy of both systemic and intravitreal Voriconazole treatment are discussed.
DOI: 10.6084/m9.figshare.24611907.v1
title: Supplementary Material for: Non-Pathogenic Aspergillus Oryzae Acute Exogenous Endophthalmitis after Penetrating Keratoplasty: The first case report in the literature.

description: The authors report a singular case of post-operative exogenous fungal endophthalmitis caused by a non-pathogenic fungal agent: the Aspergillus Oryzae. A 75-year-old Caucasian woman with post-penetrating keratoplasty fungal endophthalmitis due to a non-pathogenic Aspergillus Oryzae, resistant to the current azoles anti-fungal agents, was treated with subtotal vitrectomy, intravitreal injection and systemic Voriconazole therapy. Complete resolution of the endophthalmitis occurred after two subsequent intravitreal injections and 2 months long systemic delivery of Voriconazole. The quick identification of the fungal agent allowed immediate and targeted therapy. In the article, the safety and efficacy of both systemic and intravitreal Voriconazole treatment are discussed.
DOI: 10.6084/m9.figshare.24639207
title: Table S2: Constraining the exhumation history of the Greater Himalayan sequence, Kali Gandaki, Central Nepal

description: Table S2.
DOI: 10.6084/m9.figshare.24639207.v1
title: Table S2: Constraining the exhumation history of the Greater Himalayan sequence, Kali Gandaki, Central Nepal

description: Table S2.
DOI: 10.6084/m9.figshare.24639210
title: Table S4: Constraining the exhumation history of the Greater Himalayan sequence, Kali Gandaki, Central Nepal

description: Table S4.
DOI: 10.6084/m9.figshare.24639210.v1
title: Table S4: Constraining the exhumation history of the Greater Himalayan sequence, Kali Gandaki, Central Nepal

description: Table S4.
DOI: 10.6084/m9.figshare.24639213
title: Table S3: Constraining the exhumation history of the Greater Himalayan sequence, Kali Gandaki, Central Nepal

description: Table S3.
DOI: 10.6084/m9.figshare.24639213.v1
title: Table S3: Constraining the exhumation history of the Greater Himalayan sequence, Kali Gandaki, Central Nepal

description: Table S3.