cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30633173.v1
title: Supplementary Material for: Minimal clonal plasma cell contamination of peripheral stem cell grafts have an adverse prognostic impact in patients with multiple myeloma undergoing autologous transplantation

description: Introduction
The significance of autografts' contamination by clonal plasma cells on clinical outcome in newly diagnosed multiple myeloma remains controversial.
Methods
We retrospectively reviewed the clinical and laboratory data of newly diagnosed multiple myeloma (NDMM) patients who underwent autologous stem cell transplantation (ASCT) and had received graft minimal residual disease (gMRD) examination by multi-color flow cytometry. 
Results
From January 2011 to December 2022, 250 NDMM patients with complete cytogenetic information, gMRD information, and who received autologous stem cell transplantation (ASCT) as consolidation were enrolled. Multi-flow cytometry can achieve a median detection sensitivity of 0.004%, and gMRD positivity was 12.4% at a median level of 0.0160% (IQR, 0.0049%, 0.05394%). Its presence was correlated with response to induction treatment, with percentages of 2.65%, 12.94%, 28.89%, and 57.14% of patients achieving complete response, very good partial response, partial response, and minimal response/stable disease, respectively. gMRD (+) patients had a higher risk of not achieving bone marrow MRD negativity post-ASCT. After a median follow-up of 33.5 months for the whole cohort, patients in the gMRD (+) group had significantly worse PFS than those in the gMRD (-) group did (34.8 vs. 65.0 months, P = 0.001). Multivariable analysis revealed that gMRD (-) was independently predictive of better PFS (HR 0.464, 95%CI: 0.274-0.785, P = 0.004). We found the significance of gMRD on PFS was in high-risk subgroups and in patients who achieved ≤ partial response prior to ASCT.
Conclusions
In conclusion, gMRD (+) was an independent risk factor for inferior progression-free survival, with the impact primarily affecting high-risk groups and patients who achieved ≤ partial response before ASCT.
DOI: 10.15468/dl.q5gbkt
title: Occurrence Download

description: A dataset containing 8525 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.countrycode = 'CN'
 AND (occurrence.level0gid = 'CHN.2.1_1' OR occurrence.level1gid = 'CHN.2.1_1' OR occurrence.level2gid = 'CHN.2.1_1' OR occurrence.level3gid = 'CHN.2.1_1')
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (CAST(occurrence.taxonkey AS INTEGER) = 212 OR CAST(occurrence.acceptedtaxonkey AS INTEGER) = 212 OR CAST(occurrence.kingdomkey AS INTEGER) = 212 OR CAST(occurrence.phylumkey AS INTEGER) = 212 OR CAST(occurrence.classkey AS INTEGER) = 212 OR CAST(occurrence.orderkey AS INTEGER) = 212 OR CAST(occurrence.familykey AS INTEGER) = 212 OR CAST(occurrence.genuskey AS INTEGER) = 212 OR CAST(occurrence.specieskey AS INTEGER) = 212)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 8525 records were derived from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038398-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.23636/wkrk-hw15
title: Automatic Text Recognition (ATR) in Cultural Heritage Institutions Survey [version 1.2]

description: The dataset contains the answers to the survey on Automatic Text Recognition collected between 17/03/2025 and 07/04/2025. For more information on the survey background see the blog post published on the Digital Scholarship Blog: https://blogs.bl.uk/digital-scholarship/2025/03/help-us-explore-automatic-text-recognition-in-cultural-heritage-.html. It also contains a PDF copy of the original survey. The data has been anonymised. An analysis of the data will be published on the Digital Scholarship blog.
description: UK
DOI: 10.15468/dl.9hjkf9
title: Occurrence Download

description: A dataset containing 6074 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 (occurrence.level0gid = 'CHN.2.1_1' OR occurrence.level1gid = 'CHN.2.1_1' OR occurrence.level2gid = 'CHN.2.1_1' OR occurrence.level3gid = 'CHN.2.1_1')
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (CAST(occurrence.taxonkey AS INTEGER) = 216 OR CAST(occurrence.acceptedtaxonkey AS INTEGER) = 216 OR CAST(occurrence.kingdomkey AS INTEGER) = 216 OR CAST(occurrence.phylumkey AS INTEGER) = 216 OR CAST(occurrence.classkey AS INTEGER) = 216 OR CAST(occurrence.orderkey AS INTEGER) = 216 OR CAST(occurrence.familykey AS INTEGER) = 216 OR CAST(occurrence.genuskey AS INTEGER) = 216 OR CAST(occurrence.specieskey AS INTEGER) = 216)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 6074 records were derived from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038401-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30618955
title: ele_foodsp_nutrients_global.R from Should elephants graze or browse? The nutritional and functional consequences of dietary variation in a mixed-feeding megaherbivore

description: R script to analyze nutritional differences between grass and browse for the global dataset
DOI: 10.6084/m9.figshare.30618955.v1
title: ele_foodsp_nutrients_global.R from Should elephants graze or browse? The nutritional and functional consequences of dietary variation in a mixed-feeding megaherbivore

description: R script to analyze nutritional differences between grass and browse for the global dataset
DOI: 10.6084/m9.figshare.30618958
title: Supplementary Material from Should elephants graze or browse? The nutritional and functional consequences of dietary variation in a mixed-feeding megaherbivore

description: elephas paper data.csv
DOI: 10.6084/m9.figshare.30618958.v1
title: Supplementary Material from Should elephants graze or browse? The nutritional and functional consequences of dietary variation in a mixed-feeding megaherbivore

description: elephas paper data.csv
DOI: 10.6084/m9.figshare.30618961
title: ELEDUNG_dC13.csv from Should elephants graze or browse? The nutritional and functional consequences of dietary variation in a mixed-feeding megaherbivore

description: data file with carbon isotope values for fecal samples, used to analyze diet composition (grazing/browsing)
DOI: 10.6084/m9.figshare.30618961.v1
title: ELEDUNG_dC13.csv from Should elephants graze or browse? The nutritional and functional consequences of dietary variation in a mixed-feeding megaherbivore

description: data file with carbon isotope values for fecal samples, used to analyze diet composition (grazing/browsing)
DOI: 10.6084/m9.figshare.30618964
title: elephas paper data.csv from Should elephants graze or browse? The nutritional and functional consequences of dietary variation in a mixed-feeding megaherbivore

description: Raw data (on nutritional values of elephant food plants) used as input in ele_foodsp_nutrients_global.R and ele_foodsp_nutrients_onlyAsia.R scripts
DOI: 10.6084/m9.figshare.30618964.v1
title: elephas paper data.csv from Should elephants graze or browse? The nutritional and functional consequences of dietary variation in a mixed-feeding megaherbivore

description: Raw data (on nutritional values of elephant food plants) used as input in ele_foodsp_nutrients_global.R and ele_foodsp_nutrients_onlyAsia.R scripts
DOI: 10.6084/m9.figshare.30618970
title: Supplementary Material from Should elephants graze or browse? The nutritional and functional consequences of dietary variation in a mixed-feeding megaherbivore

description: Nutrients_eledung.R
DOI: 10.6084/m9.figshare.30618970.v1
title: Supplementary Material from Should elephants graze or browse? The nutritional and functional consequences of dietary variation in a mixed-feeding megaherbivore

description: Nutrients_eledung.R
DOI: 10.6084/m9.figshare.30618973
title: ele_foodsp_nutrients_onlyasia.R from Should elephants graze or browse? The nutritional and functional consequences of dietary variation in a mixed-feeding megaherbivore

description: R script to analyze nutritional differences between grass and browse for the Asia-limited dataset
DOI: 10.6084/m9.figshare.30618973.v1
title: ele_foodsp_nutrients_onlyasia.R from Should elephants graze or browse? The nutritional and functional consequences of dietary variation in a mixed-feeding megaherbivore

description: R script to analyze nutritional differences between grass and browse for the Asia-limited dataset
DOI: 10.6084/m9.figshare.26308873
title: untargeted metabolomic analysis.zip

description: Untargeted metabolomic analysis of fecal samples from MPTP-induced parkinson's disease mice.
DOI: 10.6084/m9.figshare.26308873.v1
title: untargeted metabolomic analysis.zip

description: Untargeted metabolomic analysis of fecal samples from MPTP-induced parkinson's disease mice.
DOI: 10.5281/zenodo.17602550
title: Underwater images collected by a Kite surf in Pointe-Esny, Mauritius - 2018-03-04

description: This dataset was collected by a Kite surf in Pointe-Esny, Mauritius - 2018-03-04. 

            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!
DOI: 10.5281/zenodo.17626795
title: Underwater images collected by a Kite surf in Pointe-Esny, Mauritius - 2018-03-04

description: This dataset was collected by a Kite surf in Pointe-Esny, Mauritius - 2018-03-04. 

            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!
DOI: 10.15468/dl.pkeg5p
title: Occurrence Download

description: A dataset containing 3708 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 (occurrence.level0gid = 'CHN.27.1_1' OR occurrence.level1gid = 'CHN.27.1_1' OR occurrence.level2gid = 'CHN.27.1_1' OR occurrence.level3gid = 'CHN.27.1_1')
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (CAST(occurrence.taxonkey AS INTEGER) = 212 OR CAST(occurrence.acceptedtaxonkey AS INTEGER) = 212 OR CAST(occurrence.kingdomkey AS INTEGER) = 212 OR CAST(occurrence.phylumkey AS INTEGER) = 212 OR CAST(occurrence.classkey AS INTEGER) = 212 OR CAST(occurrence.orderkey AS INTEGER) = 212 OR CAST(occurrence.familykey AS INTEGER) = 212 OR CAST(occurrence.genuskey AS INTEGER) = 212 OR CAST(occurrence.specieskey AS INTEGER) = 212)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 3708 records were derived from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038405-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30627469
title: Stability assessment of a novel 3D-printed oral dosage form of Mexiletine Hydrochloride: a six-month shelf-life study

description: This study presents the first evaluation of the shelf life of a novel three-dimensional (3D) printed formulation of Mexiletine Hydrochloride (HCl) over a six-month storage period. Mexiletine HCl was incorporated into a patient-personalized solid oral dosage form using a Semi-Solid Extrusion (SSE) 3D printing technique, and was then subjected to a preliminary stability assessment. We evaluated physical and chemical stability under ICH-recommended storage conditions, taking an important first step toward future full ICH-compliant studies. 3D-printed tablets were stored in a climatic cabinet, at 25 ± 2 °C and relative humidity 60 ± 5% (RH). The tablets were then evaluated for drug content analysis and impurities at 0 and 6 months of storage. Throughout the evaluation period, the printed tablets retained their physical characteristics, including shape and color, and exhibited no significant deviation in drug content and weight. Moreover, no detectable levels of degradation products or impurities were observed, as confirmed by validated analytical methods. These findings collectively indicate that the 3D-printed Mexiletine HCl formulation demonstrates excellent stability under room temperature storage conditions—information that is essential for advancing the formulation along regulatory and commercial pathways. Our data address the common trade-off between printability and stability, demonstrating that a formulation optimized for SSE printing can maintain stability under standard storage conditions.
DOI: 10.6084/m9.figshare.30627469.v2
title: Stability assessment of a novel 3D-printed oral dosage form of Mexiletine Hydrochloride: a six-month shelf-life study

description: This study presents the first evaluation of the shelf life of a novel three-dimensional (3D) printed formulation of Mexiletine Hydrochloride (HCl) over a six-month storage period. Mexiletine HCl was incorporated into a patient-personalized solid oral dosage form using a Semi-Solid Extrusion (SSE) 3D printing technique, and was then subjected to a preliminary stability assessment. We evaluated physical and chemical stability under ICH-recommended storage conditions, taking an important first step toward future full ICH-compliant studies. 3D-printed tablets were stored in a climatic cabinet, at 25 ± 2 °C and relative humidity 60 ± 5% (RH). The tablets were then evaluated for drug content analysis and impurities at 0 and 6 months of storage. Throughout the evaluation period, the printed tablets retained their physical characteristics, including shape and color, and exhibited no significant deviation in drug content and weight. Moreover, no detectable levels of degradation products or impurities were observed, as confirmed by validated analytical methods. These findings collectively indicate that the 3D-printed Mexiletine HCl formulation demonstrates excellent stability under room temperature storage conditions—information that is essential for advancing the formulation along regulatory and commercial pathways. Our data address the common trade-off between printability and stability, demonstrating that a formulation optimized for SSE printing can maintain stability under standard storage conditions.