cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.zva8hq
title: Occurrence Download

description: A dataset containing 8696 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Amorpha canescens Pursh)"
 ]
}

 The dataset includes 8696 records from 94 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007871-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3ghm55
title: Occurrence Download

description: A dataset containing 380 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "TaxonKey is Puya ferruginea (Ruiz & Pav.) L.B.Sm."
 ]
}

 The dataset includes 380 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007872-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5v4w7j
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Emilia flammea Cass.)"
 ]
}

 The dataset includes 8 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007873-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22002/0c391-eeh07
title: Dataset: Gene model for the ortholog Myc in Drosophila ananassae

description: GFF, FASTA, and PEP sequences for Myc in D ananassae
DOI: 10.15468/dl.ntq7dk
title: Occurrence Download

description: A dataset containing 9125 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Setaria faberi R.A.W.Herrm.)"
 ]
}

 The dataset includes 9125 records from 246 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007875-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mkyj7d
title: Occurrence Download

description: A dataset containing 173 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Brassica oleracea var. italica Plenck)"
 ]
}

 The dataset includes 173 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007876-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.87k372
title: Occurrence Download

description: A dataset containing 119 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Glycyrrhiza uralensis Fisch.)"
 ]
}

 The dataset includes 119 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007877-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d2mmgg
title: Occurrence Download

description: A dataset containing 173 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Glycyrrhiza pallidiflora Maxim.)"
 ]
}

 The dataset includes 173 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007878-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zxubt8
title: Occurrence Download

description: A dataset containing 315 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Citrus ×junos Siebold ex Yu.Tanaka"
 ]
}

 The dataset includes 315 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007880-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fyn7xn
title: Occurrence Download

description: A dataset containing 136424 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Symphyotrichum Nees",
 {
 "or" : [
 "Year 1960-1969",
 "Year 2016-2024",
 "Year 1996-1996"
 ]
 }
 ]
}

 The dataset includes 136424 records from 925 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007855-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17287991
title: Xinjiang Velocity Model

description: The dataset contains the  3-D Vp & Vs velocity model in the Xinjiang Area.
DOI: 10.5281/zenodo.17287992
title: Xinjiang Velocity Model

description: The dataset contains the  3-D Vp & Vs velocity model in the Xinjiang Area.
DOI: 10.15468/dl.tr5ch7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Apterygiformes)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((-90 30,-60 30,-60 60,-90 60,-90 30))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058415-250920141307145/datasets/export for details.
DOI: 10.5281/zenodo.17288039
title: Halamphora lukasiewiczii alignment files
DOI: 10.5281/zenodo.17288040
title: Halamphora lukasiewiczii alignment files
DOI: 10.6084/m9.figshare.25913917
title: Using the interactive software FossilSketch to teach micropaleontology to undergraduate students

description: Micropaleontology is a critical tool for determining the ages of geologic records, reconstructing ancient environments, and monitoring modern ecosystem health. However, most students are not exposed to micropaleontology in their college coursework. To enable non-expert instructors to integrate microfossil identification training in their undergraduate courses, we developed FossilSketch, an interactive web-based educational tool that introduces students to the basics of micropaleontology and guides students through a scaffolded learning experience that develops microfossil identification skills. Here we test the impact of FossilSketch on students’ ability to learn micropaleontology skills, such as identification of microfossils to genus level and basics of fossil data analysis, using data on students’ performance and survey responses collected in an undergraduate paleontology course for geology majors at a large public university. A total of 112 students took part in this study. Analysis of classroom assessments showed that junior and senior geology majors who used FossilSketch were better able to understand the process of microfossil identification, recognize morphological characteristics, and achieve a correct identification than those who did not use FossilSketch. Students who used FossilSketch needed to ask the teaching assistant fewer questions and felt better prepared for specimen-based work than students who did not use FossilSketch. These results suggest that FossilSketch improves students’ understanding of the microfossil identification process.
DOI: 10.6084/m9.figshare.25913917.v1
title: Using the interactive software FossilSketch to teach micropaleontology to undergraduate students

description: Micropaleontology is a critical tool for determining the ages of geologic records, reconstructing ancient environments, and monitoring modern ecosystem health. However, most students are not exposed to micropaleontology in their college coursework. To enable non-expert instructors to integrate microfossil identification training in their undergraduate courses, we developed FossilSketch, an interactive web-based educational tool that introduces students to the basics of micropaleontology and guides students through a scaffolded learning experience that develops microfossil identification skills. Here we test the impact of FossilSketch on students’ ability to learn micropaleontology skills, such as identification of microfossils to genus level and basics of fossil data analysis, using data on students’ performance and survey responses collected in an undergraduate paleontology course for geology majors at a large public university. A total of 112 students took part in this study. Analysis of classroom assessments showed that junior and senior geology majors who used FossilSketch were better able to understand the process of microfossil identification, recognize morphological characteristics, and achieve a correct identification than those who did not use FossilSketch. Students who used FossilSketch needed to ask the teaching assistant fewer questions and felt better prepared for specimen-based work than students who did not use FossilSketch. These results suggest that FossilSketch improves students’ understanding of the microfossil identification process.
DOI: 10.6084/m9.figshare.25175106
title: A review of the inquiry and utility of mineral and rock labs for use in introductory geology courses

description: Rock and mineral labs are fundamental in traditional introductory geology courses. Successful implementation of these lab activities provides students opportunities to apply content knowledge. Inquiry-based instruction may be one way to increase student success. Prior examination of published STEM labs indicates that geology labs, particularly rock and mineral activities, are often constructed at low levels of inquiry (i.e., students are provided context throughout each step of the scientific process rather than constructing their own knowledge). This could be related to instructor concerns about the time needed to implement inquiry-based labs. Here, 36 instructor-generated rock and mineral labs available through the Teach the Earth (TTE) collection are assessed using an inquiry rubric adapted from Ryker and McConnell and a newly developed utility rubric. For the activities within these labs (<i>n</i> = 55), inquiry levels ranged from confirmation (7%) to open (16%) with most identified as structured (58%). Lab activities from the TTE collection are generally more inquiry-based than previously published activities. Lab utility scores ranged from 18–29 on a 10–30 scale, where lower values indicate a greater difficulty of implementation. One particular challenge for these labs may be ease of grading, the category rated most often as having low utility. No significant correlations (<i>p</i> &gt; 0.05) were identified between the inquiry and utility scores, contradicting the idea that increasing inquiry comes at the expense of utility. The rubrics utilized in and developed for this study provide researchers with beneficial tools for exploration of laboratory activities on other topics, or in different disciplines.
DOI: 10.6084/m9.figshare.25175106.v1
title: A review of the inquiry and utility of mineral and rock labs for use in introductory geology courses

description: Rock and mineral labs are fundamental in traditional introductory geology courses. Successful implementation of these lab activities provides students opportunities to apply content knowledge. Inquiry-based instruction may be one way to increase student success. Prior examination of published STEM labs indicates that geology labs, particularly rock and mineral activities, are often constructed at low levels of inquiry (i.e., students are provided context throughout each step of the scientific process rather than constructing their own knowledge). This could be related to instructor concerns about the time needed to implement inquiry-based labs. Here, 36 instructor-generated rock and mineral labs available through the Teach the Earth (TTE) collection are assessed using an inquiry rubric adapted from Ryker and McConnell and a newly developed utility rubric. For the activities within these labs (<i>n</i> = 55), inquiry levels ranged from confirmation (7%) to open (16%) with most identified as structured (58%). Lab activities from the TTE collection are generally more inquiry-based than previously published activities. Lab utility scores ranged from 18–29 on a 10–30 scale, where lower values indicate a greater difficulty of implementation. One particular challenge for these labs may be ease of grading, the category rated most often as having low utility. No significant correlations (<i>p</i> &gt; 0.05) were identified between the inquiry and utility scores, contradicting the idea that increasing inquiry comes at the expense of utility. The rubrics utilized in and developed for this study provide researchers with beneficial tools for exploration of laboratory activities on other topics, or in different disciplines.
DOI: 10.5281/zenodo.11176588
title: Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-05-31

description: This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-05-31. 

            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.

            

            
 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!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.5281/zenodo.11176742
title: Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-05-31

description: This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-05-31. 

            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.

            

            
 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!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.5281/zenodo.11176876
title: Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-05-31

description: This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-05-31. 

            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.

            

            
 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!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.15468/dl.eg3pv4
title: Occurrence Download

description: A dataset containing 26572 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (iNaturalist Research-grade Observations, Observation.org, Nature data from around the World)",
 "Month is March",
 "OccurrenceStatus is Present",
 "TaxonKey is Mollusca",
 "Year 2025-2025"
 ]
}

 The dataset includes 26572 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020513-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15204935
title: Active transport enables protein condensation in cells

description: The repository contains data for the LAMMPS simulations in the manuscript titled: 

Active transport enables protein condensation in cells.
DOI: 10.5281/zenodo.15204936
title: Active transport enables protein condensation in cells

description: The repository contains data for the LAMMPS simulations in the manuscript titled: 

Active transport enables protein condensation in cells.