cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30627386.v1
title: Supplementary Material for: Does oXiris Matter? A Comparative Outcome Study in CRRT-Treated Septic ICU Patients

description: Background/Objectives: Sepsis and septic shock frequently lead to acute kidney injury (AKI) and the need for continuous renal replacement therapy (CRRT). The oXiris hemofilter has cytokine- and endotoxin-adsorptive properties that may improve outcomes, but evidence remains inconclusive. This study compared the clinical outcomes of septic intensive care unit (ICU) patients treated with CRRT using oXiris versus standard filters. Methods: We conducted a retrospective observational study at a tertiary ICU in Istanbul between January 2019 and June 2024. Adult septic patients requiring CRRT within 24 hours of ICU admission were included. Patients were treated with either oXiris or standard CRRT filters (M-100 / M-150) for ≥ 16 hours. The primary endpoint was ICU mortality; secondary endpoints included ventilator-free days (VFD), ICU length of stay, and trends in inflammatory and biochemical markers. Results: A total of 360 patients were analyzed (133 oXiris, 227 standard filters). Despite higher baseline Sequential Organ Failure Assessment (SOFA) scores, C-reactive protein (CRP), procalcitonin (PCT), and lactate in the oXiris group, ICU survival was significantly higher (61.7% vs. 48.9%, p = 0.019). After adjustment, oXiris use remained independently associated with improved survival (OR 1.68, 95% CI 1.09–2.60, p = 0.020). Total CRRT duration was markedly shorter in the oXiris group (56.4 vs. 107.4 hours, p &lt; 0.001). VFD were similar between groups. Inflammatory markers (CRP, PCT) remained elevated in oXiris patients through day 5, while hemodynamic stabilization (lactate decline, norepinephrine reduction) was more pronounced. Platelet counts decreased in both groups without recovery. Conclusions: Our study demonstrated that patients with sepsis treated using the oXiris filter exhibited significantly higher ICU survival rates, despite higher mechanical ventilation duration and ICU length of stay. However, persistent elevations in CRP and PCT, along with thrombocytopenia, suggest that these markers may not fully reflect therapeutic response. While oXiris use was associated with reduced ICU mortality, further randomized trials are needed to confirm its efficacy and account for potential confounders.
DOI: 10.15468/dl.fhy625
title: Occurrence Download

description: A dataset containing 139 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-109.0188 24.89913, -108.8688 24.89913, -108.8688 25.04913, -109.0188 25.04913, -109.0188 24.89913))"
 ]
}

 The dataset includes 139 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035366-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zj53tb
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Notropis buccula",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035367-251025141854904/datasets/export for details.
DOI: 10.5281/zenodo.17616076
title: Underwater images collected by a Kite surf in One-Eye, Mauritius - 2019-08-03

description: This dataset was collected by a Kite surf in One-Eye, Mauritius - 2019-08-03. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            
                
                                       
                
                
 GPS information: 

                No GPS.
            
                    
                

                
            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
DOI: 10.5281/zenodo.17616428
title: Underwater images collected by a Kite surf in One-Eye, Mauritius - 2019-08-03

description: This dataset was collected by a Kite surf in One-Eye, Mauritius - 2019-08-03. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            
                
                                       
                
                
 GPS information: 

                No GPS.
            
                    
                

                
            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
DOI: 10.6084/m9.figshare.30627365
title: Pan-genome of Melon

description: We assembled a pan genome containing 9 melon species, and we have performed gene mapping on important agronomic traits. "Pan-genome analysis sheds light on structural variation-based dissection of agronomic traits in melon crops"
DOI: 10.6084/m9.figshare.30627365.v1
title: Pan-genome of Melon

description: We assembled a pan genome containing 9 melon species, and we have performed gene mapping on important agronomic traits. "Pan-genome analysis sheds light on structural variation-based dissection of agronomic traits in melon crops"
DOI: 10.15468/dl.gvxc93
title: Occurrence Download

description: A dataset containing 101 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-108.8688 24.89913, -108.7188 24.89913, -108.7188 25.04913, -108.8688 25.04913, -108.8688 24.89913))"
 ]
}

 The dataset includes 101 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035368-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aevmkj
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Notropis buchanani",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035369-251025141854904/datasets/export for details.
DOI: 10.15468/dl.kym6kr
title: Occurrence Download

description: A dataset containing 88 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Spain",
 "OccurrenceStatus is Present",
 "TaxonKey is Xylocopa violacea"
 ]
}

 The dataset includes 88 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035370-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3ey242
title: Occurrence Download

description: A dataset containing 337 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-108.7188 24.89913, -108.5688 24.89913, -108.5688 25.04913, -108.7188 25.04913, -108.7188 24.89913))"
 ]
}

 The dataset includes 337 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035372-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.94etp7
title: Occurrence Download

description: A dataset containing 25959 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "License is one of (CC-BY-NC 4.0, CC-BY 4.0)",
 "TaxonKey is one of (Lolium arundinaceum, Echinochloa crus-galli, Eleusine indica)"
 ]
}

 The dataset includes 25959 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035373-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a753dy
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Notropis chiliticus",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035374-251025141854904/datasets/export for details.
DOI: 10.6084/m9.figshare.30621400
title: Prognostic value of cardiac troponin I in acute ischemic stroke patients treated with reperfusion therapy: a multicenter retrospective cohort study

description: We aimed to evaluate the association between elevated cardiac troponin I (cTnI) and clinical outcomes in AIS patients receiving reperfusion therapy. We conducted a multicenter retrospective cohort study of AIS patients treated with thrombolytics at Southern Illinois Healthcare and the University of Oklahoma (2017–2024). Demographic, clinical, laboratory, and radiographic data were collected. Elevation of cTnI was defined as levels above 0.028 ng/mL. For high sensitivity troponins, the cutoff was 15 ng/L for women and 20 ng/L for men. Outcomes included symptomatic intracranial hemorrhage (sICH), in-hospital mortality, and functional status at 30 days (good outcome defined as mRS ≤2). Overlap-weighted multivariable logistic regression was performed, adjusting for demographics, comorbidities, and stroke severity. Among 496 patients, 143 (29%) had elevated cTnI. Compared with patients with normal cTnI, those with elevated levels were older, had more vascular risk factors, and higher baseline NIHSS. Elevated cTnI was not significantly associated with sICH (OR:3.75, 95%CI 0.52–27.21, <i>p</i> = 0.19), but was independently associated with increased in-hospital mortality (OR:1.96, 95%CI 1.62–6.20, <i>p</i> = 0.025) and decreased odds of good functional outcome at 30 days (OR:0.40, 95%CI 0.19–0.84, <i>p</i> = 0.016). Subgroup analyses showed these associations were most pronounced in older patients and in those with moderate (NIHSS 6–15) to severe (NIHSS 16–20 and &gt; 20) strokes, whereas no significant relationships were observed in younger patients or mild strokes. Normalization of cTnI during hospitalization did not improve survival (<i>p</i> &gt; 0.05). In our study, elevated cTnI on admission is an independent predictor of poor short-term outcomes in AIS patients treated with reperfusion therapy. Troponin measurement may provide important prognostic information, particularly in older patients and those with more severe strokes.
DOI: 10.6084/m9.figshare.30621400.v2
title: Prognostic value of cardiac troponin I in acute ischemic stroke patients treated with reperfusion therapy: a multicenter retrospective cohort study

description: We aimed to evaluate the association between elevated cardiac troponin I (cTnI) and clinical outcomes in AIS patients receiving reperfusion therapy. We conducted a multicenter retrospective cohort study of AIS patients treated with thrombolytics at Southern Illinois Healthcare and the University of Oklahoma (2017–2024). Demographic, clinical, laboratory, and radiographic data were collected. Elevation of cTnI was defined as levels above 0.028 ng/mL. For high sensitivity troponins, the cutoff was 15 ng/L for women and 20 ng/L for men. Outcomes included symptomatic intracranial hemorrhage (sICH), in-hospital mortality, and functional status at 30 days (good outcome defined as mRS ≤2). Overlap-weighted multivariable logistic regression was performed, adjusting for demographics, comorbidities, and stroke severity. Among 496 patients, 143 (29%) had elevated cTnI. Compared with patients with normal cTnI, those with elevated levels were older, had more vascular risk factors, and higher baseline NIHSS. Elevated cTnI was not significantly associated with sICH (OR:3.75, 95%CI 0.52–27.21, <i>p</i> = 0.19), but was independently associated with increased in-hospital mortality (OR:1.96, 95%CI 1.62–6.20, <i>p</i> = 0.025) and decreased odds of good functional outcome at 30 days (OR:0.40, 95%CI 0.19–0.84, <i>p</i> = 0.016). Subgroup analyses showed these associations were most pronounced in older patients and in those with moderate (NIHSS 6–15) to severe (NIHSS 16–20 and &gt; 20) strokes, whereas no significant relationships were observed in younger patients or mild strokes. Normalization of cTnI during hospitalization did not improve survival (<i>p</i> &gt; 0.05). In our study, elevated cTnI on admission is an independent predictor of poor short-term outcomes in AIS patients treated with reperfusion therapy. Troponin measurement may provide important prognostic information, particularly in older patients and those with more severe strokes.
DOI: 10.6084/m9.figshare.30621400.v1
title: Prognostic Value of Cardiac Troponin I in Acute Ischemic Stroke Patients Treated with Reperfusion Therapy: A Multicenter Retrospective Cohort Study.

description: Background We aimed to evaluate the association between elevated cardiac troponin I (cTnI) and clinical outcomes in AIS patients receiving reperfusion therapy. Methods We conducted a multicenter retrospective cohort study of AIS patients treated with thrombolytics at Southern Illinois Healthcare and the University of Oklahoma (2017–2024). Demographic, clinical, laboratory, and radiographic data were collected. Elevation of cTnI was defined as levels above 0.028 ng/mL. For high sensitivity troponins, the cutoff was 15 ng/L for women and 20 ng/L for men. Outcomes included symptomatic intracranial hemorrhage (sICH), in-hospital mortality, and functional status at 30 days (good outcome defined as mRS ≤2). Overlap-weighted multivariable logistic regression was performed, adjusting for demographics, comorbidities, and stroke severity. Statistical significance was set at p &lt; 0.05. Results Among 496 patients, 143 (29%) had elevated cTnI. Compared with patients with normal cTnI, those with elevated levels were older, had more vascular risk factors, and higher baseline NIHSS. Elevated cTnI was not significantly associated with sICH (OR:3.75, 95%CI 0.52–27.21, p = 0.19), but was independently associated with increased in-hospital mortality (OR:1.96, 95%CI 1.62–6.20, p = 0.025) and decreased odds of good functional outcome at 30 days (OR:0.40, 95%CI 0.19–0.84, p = 0.016). Subgroup analyses showed these associations were most pronounced in older patients and in those with moderate (NIHSS 6-15) to severe (NIHSS 16-20 and &gt; 20) strokes, whereas no significant relationships were observed in younger patients or mild strokes. Normalization of cTnI during hospitalization did not improve survival (p &gt; 0.05). Conclusions In our study, elevated cTnI on admission is an independent predictor of poor short-term outcomes in AIS patients treated with reperfusion therapy. Troponin measurement may provide important prognostic information, particularly in older patients and those with more severe strokes.
DOI: 10.5281/zenodo.17616919
title: Underwater images collected by  Scuba diving in Nosy-Velomitahy, Madagascar - 2023-05-03

description: This dataset was collected by  Scuba diving in Nosy-Velomitahy, Madagascar - 2023-05-03. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            
                
                                       
                
                
 GPS information: 

                GPX file from Garmin watch.
            
                    
                

                
            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!