cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.jh277k
title: Occurrence Download

description: A dataset containing 739011 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2025-03-11",
 "EventDate is less than or equal to 2025-04-10",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 }
 ]
}

 The dataset includes 739011 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018527-250426092105405/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.28464323
title: <b>Fig. 3 | Performance analysis of microrobot navigation in various environments using reinforcement learning algorithms.</b>

description: Reward trajectories for: <b>a.</b> Multi-output Tributary channel, <b>b.</b> Circuitous channel, <b>c.</b> Vascular channels with Dreamer V3 (blue) outperforming the hyperparameter-tuned state-of-the-art PPO (green) across simulation steps. <b>d.</b> Comparison of PPO and Dreamer algorithms in reaching targets across different channel types: Racetrack, tributary, SPA, Squares, Vascular, and Maze. <b>e.</b> Impact of different reward functions on the rate of target achievement. <b>f.</b> Effects of frame skipping on performance, presented in a logarithmic plot. <b>g.</b> Influence of training ratios on reward dynamics, highlighting consistent performance across various ratios in simulated environments.
DOI: 10.25934/pr00011347
title: ASTPredIndia

description: Antimicrobial Resistance in Indian Clinical Isolates: Understanding Patterns to Improve Patient Care
Background and Summary
By analyzing comprehensive datasets of antimicrobial susceptibility profiles and resistance mechanisms from clinical isolates across India, this research aims to create a detailed map of resistance patterns in key gram-negative pathogens over time and across different regions.
Gram-negative bacteria  are common causes of serious infections in healthcare settings and the community. These bacteria have shown alarming increases in resistance to multiple antibiotics, making infections increasingly difficult to treat. By understanding the specific resistance mechanisms and how they vary by region, time period, and patient population, we can develop more targeted approaches to combat these dangerous infections. Also, Genitourinary Tuberculosis (GUTB) can be misdiagnosed as a urinary tract infection (UTI), leading to delayed or incorrect treatment. UTIs and GUTB can share similar symptoms, making differentiation challenging. 
This research will directly impact patient care by:

Developing region-specific antibiotic treatment guidelines based on local resistance patterns
Enabling healthcare providers to make more informed initial antibiotic choices, increasing the likelihood of effective first-line treatment
Reducing delays in appropriate therapy by identifying predictors of resistance
Creating prediction tools that help clinicians quickly identify patients at higher risk for resistant infections

When physicians can better predict which antibiotics will be effective against a specific infection, patients receive appropriate treatment faster, experience fewer complications, and have shorter hospital stays.
Strengthening Antimicrobial Stewardship
This research will support antibiotic stewardship efforts by:

By mapping resistance trends to antibiotic usage patterns, hospitals and healthcare systems can develop more effective policies to preserve the effectiveness of critical antibiotics.
DOI: 10.6084/m9.figshare.28464416
title: <b>Fig. 4 | </b><b>Transition of autonomous microrobots from simulation to </b><b>physical</b><b> environment. </b>

description: <b>a. </b>A plot of MBRL demonstrates training of microrobots with 32 actions in an experimental racetrack square channel. The <i>y</i>-axis indicates the reward function, with positive values signalling successful navigation towards the target and negative values indicating crashes. The <i>x</i>-axis displays the number of training steps. The reward drops significantly at around 300k steps due to overfitting to a specific corner of the channel. <b>b.</b> (Top) Image sequences illustrate the experimental training of microrobots at 100k steps of graph <b>a</b>. At this early stage, microrobots frequently fail and reach the target only 20% of the time. (Bottom) The image sequence shows that by 350k training steps, the microrobots demonstrate improved efficiency, reaching the target faster (in seconds) 75% of the time. A red box marks the microrobot when it is far from the target, while green indicates proximity to the target. The target position is denoted by a circle with a black plus symbol. <b>c. </b>A plot of RL performance illustrates the transition of a pre-trained model from simulation to the physical environment. The score rapidly increases during the simulation phase (blue), drops after the transition to experiments (marked by a red vertical dashed line), and then increases as the model adapts to the physical environment (green). <b>d. </b>A micrograph with superimposed images of the microrobots' navigation steps within an artificial vasculature, with the start and target points marked by red hollow circles. Arrows in the top left corner indicate all possible movement directions of the microrobots. <b>e, f, g.</b> Image sequence demonstrating a microrobot navigating an artificial vasculature, starting from the initial position and sequentially reaching three predefined targets:,and (. <b>h. </b>The heatmap depicts the relationship between speed and position within an artificial vasculature channel. The color gradient indicates the speed at each position, with darker colors indicating higher speeds and lighter shades denoting slower velocities.
DOI: 10.6084/m9.figshare.28464416.v1
title: <b>Fig. 4 | </b><b>Transition of autonomous microrobots from simulation to </b><b>physical</b><b> environment. </b>

description: <b>a. </b>A plot of MBRL demonstrates training of microrobots with 32 actions in an experimental racetrack square channel. The <i>y</i>-axis indicates the reward function, with positive values signalling successful navigation towards the target and negative values indicating crashes. The <i>x</i>-axis displays the number of training steps. The reward drops significantly at around 300k steps due to overfitting to a specific corner of the channel. <b>b.</b> (Top) Image sequences illustrate the experimental training of microrobots at 100k steps of graph <b>a</b>. At this early stage, microrobots frequently fail and reach the target only 20% of the time. (Bottom) The image sequence shows that by 350k training steps, the microrobots demonstrate improved efficiency, reaching the target faster (in seconds) 75% of the time. A red box marks the microrobot when it is far from the target, while green indicates proximity to the target. The target position is denoted by a circle with a black plus symbol. <b>c. </b>A plot of RL performance illustrates the transition of a pre-trained model from simulation to the physical environment. The score rapidly increases during the simulation phase (blue), drops after the transition to experiments (marked by a red vertical dashed line), and then increases as the model adapts to the physical environment (green). <b>d. </b>A micrograph with superimposed images of the microrobots' navigation steps within an artificial vasculature, with the start and target points marked by red hollow circles. Arrows in the top left corner indicate all possible movement directions of the microrobots. <b>e, f, g.</b> Image sequence demonstrating a microrobot navigating an artificial vasculature, starting from the initial position and sequentially reaching three predefined targets:,and (. <b>h. </b>The heatmap depicts the relationship between speed and position within an artificial vasculature channel. The color gradient indicates the speed at each position, with darker colors indicating higher speeds and lighter shades denoting slower velocities.
DOI: 10.15468/dl.q76q4u
title: Occurrence Download

description: A dataset containing 20537 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation)",
 {
 "or" : [
 "Geometry POLYGON((61.51417 35.29785,60.86181 34.5556,60.76862 32.95065,60.91507 32.27781,63.77746 31.97338,65.22863 31.52052,66.094 30.9856,66.62654 30.79137,68.31735 31.58859,69.46231 33.8288,70.4475 34.91665,70.0481 35.72051,69.7552 35.80693,67.3721 34.79648,65.16206 34.41294,62.84552 34.79648,61.51417 35.29785))",
 "Geometry POLYGON((50.8366 50.09919,53.23662 46.29256,56.55093 45.52386,63.21764 45.68378,65.99862 45.97577,68.85578 45.63052,71.97961 45.57722,75.37011 46.50274,80.09395 46.37147,80.93205 47.23199,82.2654 49.87876,80.47491 51.01881,78.60823 51.04277,77.31298 49.92783,77.9987 49.01188,77.31298 49.03686,74.5701 49.03686,72.58914 49.63264,68.13196 50.41582,63.5986 50.89883,60.13191 50.26994,58.79856 49.08679,58.22713 49.06183,55.21759 49.97685,53.04614 50.8267,50.8366 50.09919))",
 "Geometry POLYGON((80.1039 46.37102,80.98706 45.97161,81.94231 45.32907,83.16792 44.71758,84.35748 44.79437,85.27669 45.34174,85.56507 45.78339,85.94356 46.13422,87.20522 46.55724,87.22324 46.94008,85.69123 47.72187,85.20459 46.98928,84.17724 47.09982,83.72665 46.97699,82.35686 46.90315,80.96904 47.23461,80.1039 46.37102))",
 "Geometry POLYGON((146.11814 44.71637,145.3348 43.86289,145.45265 43.64759,146.46475 43.56728,147.04012 43.7328,147.35207 44.36061,147.74721 44.8148,148.48202 45.01607,148.90488 45.24593,148.9742 45.52345,148.73158 45.60596,147.8096 45.5817,146.74897 44.9278,146.11814 44.71637))",
 "Geometry POLYGON((139.28199 58.53735,136.7027 56.49577,134.60556 55.85652,131.75945 55.56114,130.63598 54.60374,130.81074 52.74059,130.66095 51.12448,130.86067 49.57945,131.53475 48.64801,134.73039 49.10775,136.8525 49.90213,136.67773 48.53242,135.27964 46.09564,135.35454 45.2232,135.77896 45.17042,139.9233 47.81653,140.62235 48.86199,141.46495 51.88398,141.62255 52.22747,141.59134 52.31245,141.47899 53.435,140.20573 54.43437,138.48308 54.99679,138.15853 55.8609,141.22933 58.30201,140.55525 58.55037,139.28199 58.53735))"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (April, May, June, July, August)",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves",
 {
 "or" : [
 "Year 1981-1999",
 "Year 2016-2025"
 ]
 }
 ]
}

 The dataset includes 20537 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018543-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15482/usda.adc/25054979
title: Data From: Two haplotype-resolved genomes reveal important flower traits in bigleaf hydrangea (Hydrangea macrophylla) and insights into Asterid evolution

description: These files represent the final annotations for two hydrangea genomes derived from BRAKER2 using total RNAs extracted from seven tissue types, including sepals, buds, flowers, nodes, internodes, leaves, and roots, and subjected to library preparation according to the manufacturer’s instructions. The publicly available Iso-seq reads on NCBI (PRJDB9979) were used for genome annotation. Files include normal gff3 format and coding sequence (CDS) files.
DOI: 10.15482/usda.adc/25054979.v1
title: Data From: Two haplotype-resolved genomes reveal important flower traits in bigleaf hydrangea (Hydrangea macrophylla) and insights into Asterid evolution

description: These files represent the final annotations for two hydrangea genomes derived from BRAKER2 using total RNAs extracted from seven tissue types, including sepals, buds, flowers, nodes, internodes, leaves, and roots, and subjected to library preparation according to the manufacturer’s instructions. The publicly available Iso-seq reads on NCBI (PRJDB9979) were used for genome annotation. Files include normal gff3 format and coding sequence (CDS) files.
DOI: 10.6084/m9.figshare.28464422
title: <b>Fig. 5 |</b> <b>Performance of the generalized world model across various environments.</b>

description: <b>a.</b> The plot presents the reward progression over training steps across nine distinct environments, including Empty, 4 Squares, Racetrack, Vascular, and various Maze configurations. Each line on the graph denotes the average reward for each environment, effectively illustrating the model's learning trajectory. Simpler environments achieve convergence faster, whereas more complex ones require additional time, but all eventually reach convergence. The region beyond 4.5 million steps, demarcated by a dashed vertical red line, marks the phase of pretraining and subsequent adaptation to a new environment. This transition point highlights the shift from pretraining across ten simulation environments to adaptation within a new multi-output tributary channel. The model demonstrates significant improvement over the initial 4.5 million steps of pretraining, followed by rapid adaptation, achieving stable performance within just 50,000 steps (approximately 30 minutes) in the new environment. <b>b.</b> The success rate of targets reached across different environments is plotted against training steps. Box plots illustrate the variability and distribution of the MBRL algorithm's performance in successfully reaching targets. While simpler environments facilitate quicker convergence, our MBRL model consistently attains convergence across all scenarios.<br>
DOI: 10.6084/m9.figshare.28464422.v1
title: <b>Fig. 5 |</b> <b>Performance of the generalized world model across various environments.</b>

description: <b>a.</b> The plot presents the reward progression over training steps across nine distinct environments, including Empty, 4 Squares, Racetrack, Vascular, and various Maze configurations. Each line on the graph denotes the average reward for each environment, effectively illustrating the model's learning trajectory. Simpler environments achieve convergence faster, whereas more complex ones require additional time, but all eventually reach convergence. The region beyond 4.5 million steps, demarcated by a dashed vertical red line, marks the phase of pretraining and subsequent adaptation to a new environment. This transition point highlights the shift from pretraining across ten simulation environments to adaptation within a new multi-output tributary channel. The model demonstrates significant improvement over the initial 4.5 million steps of pretraining, followed by rapid adaptation, achieving stable performance within just 50,000 steps (approximately 30 minutes) in the new environment. <b>b.</b> The success rate of targets reached across different environments is plotted against training steps. Box plots illustrate the variability and distribution of the MBRL algorithm's performance in successfully reaching targets. While simpler environments facilitate quicker convergence, our MBRL model consistently attains convergence across all scenarios.<br>
DOI: 10.15468/dl.pzc4jr
title: Occurrence Download

description: A dataset containing 486 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ethiopia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Acacia Mill."
 ]
}

 The dataset includes 486 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018547-250426092105405/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.28464425
title: <b>Fig. 6 |</b> <b>Autonomous microrobot navigation upstream in a flow environment.</b>

description: <b>a.</b> Schematic of the reward function adjustment to promote microrobot navigation close to the wall, minimizing drag. <b>b.</b> Graph showing reward progression over time for microrobots in normal (blue line) and stronger (green line) flow conditions, highlighting differences in learning and adaptation. In normal flow, rewards steadily improve and stabilize around 200,000 steps. In stronger flow, initial difficulties lead to more negative rewards, but the algorithm shows significant improvement by 400,000 steps. <b>c.</b> A schematic illustrates the behavior of the microrobot attached to the wall to avoid drag and move against the flow. <b>d.</b> An image sequence shows a microrobot navigating within a microfluidic channel under flow conditions. Initially, the microrobot encounters maximum drag, i.e., when it is at the center of the channel (0.0 s to 6.7 s) before moving towards the wall (7.8 s to 31.4 s), where the drag is minimal. The reduced drag forces at the wall facilitate more stable and controlled navigation. The red arrows indicate the direction of the fluid flow.
DOI: 10.6084/m9.figshare.28464425.v1
title: <b>Fig. 6 |</b> <b>Autonomous microrobot navigation upstream in a flow environment.</b>

description: <b>a.</b> Schematic of the reward function adjustment to promote microrobot navigation close to the wall, minimizing drag. <b>b.</b> Graph showing reward progression over time for microrobots in normal (blue line) and stronger (green line) flow conditions, highlighting differences in learning and adaptation. In normal flow, rewards steadily improve and stabilize around 200,000 steps. In stronger flow, initial difficulties lead to more negative rewards, but the algorithm shows significant improvement by 400,000 steps. <b>c.</b> A schematic illustrates the behavior of the microrobot attached to the wall to avoid drag and move against the flow. <b>d.</b> An image sequence shows a microrobot navigating within a microfluidic channel under flow conditions. Initially, the microrobot encounters maximum drag, i.e., when it is at the center of the channel (0.0 s to 6.7 s) before moving towards the wall (7.8 s to 31.4 s), where the drag is minimal. The reduced drag forces at the wall facilitate more stable and controlled navigation. The red arrows indicate the direction of the fluid flow.
DOI: 10.7266/n7-b8x5-6006
title: Tagging information of red snapper in the northern Gulf of Mexico from 2019-04-02 to 2019-05-31
DOI: 10.15468/dl.wk9shf
title: Occurrence Download

description: A dataset containing 7365 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-103.55768 19.45885,-103.60735 19.44881,-103.68895 19.40755,-103.76818 19.40644,-103.84741 19.38524,-103.85096 19.33169,-103.86633 19.25579,-103.85924 19.17204,-103.82258 19.10165,-103.73743 19.05471,-103.59434 19.03236,-103.50802 19.03795,-103.41578 19.07819,-103.38976 19.124,-103.32827 19.20443,-103.30107 19.28147,-103.34009 19.3752,-103.40513 19.46108,-103.55768 19.45885))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 7365 records from 127 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018549-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h888yh
title: Occurrence Download

description: A dataset containing 0 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 Fulica caribaea Ridgway, 1884",
 "Year is greater than or equal to 1950",
 {
 "or" : [
 "GadmGid is USA",
 "GadmGid is CAN",
 "GadmGid is MEX"
 ]
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018550-250426092105405/datasets/export for details.
DOI: 10.15468/dl.mx8wzz
title: Occurrence Download

description: A dataset containing 234242 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Phyllostomidae"
 ]
}

 The dataset includes 234242 records from 196 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018544-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mfc4f8
title: Occurrence Download

description: A dataset containing 10976 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Osmia distincta Cresson, 1864, Osmia bucephala Cresson, 1864, Osmia megacephala Cresson, 1864, Osmia latitarsis Cresson, 1864, Osmia lignivora Packard, 1867, Osmia lignicola Provancher, 1882, Osmia subornata Cockerell, 1897, Osmia cornifrons (Radoszkowski, 1887), Megachile cornifrons (Radoszkowski, 1887), Osmia taurus Smith, 1873, Centrosmia bucephala (Cresson, 1864), Chalicodoma cornifrons Radoszkowski, 1887)",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Material sample)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than or equal to 2000",
 "CoordinateUncertaintyInMeters is null"
 ]
 },
 {
 "or" : [
 "Year is greater than or equal to 1960",
 "Year is null"
 ]
 },
 "HasCoordinate is true"
 ]
}

 The dataset includes 10976 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018552-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w5rwgz
title: Occurrence Download

description: A dataset containing 24924 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-75.7482 5.94113,-75.76334 5.92688,-75.77779 5.9222,-75.78375 5.90774,-75.76972 5.90009,-75.77992 5.88053,-75.75696 5.8699,-75.73102 5.83078,-75.6783 5.80825,-75.66937 5.80485,-75.66639 5.79634,-75.65831 5.79677,-75.65066 5.79166,-75.64471 5.76445,-75.62345 5.74872,-75.61579 5.77296,-75.61962 5.8074,-75.61027 5.82866,-75.62047 5.86055,-75.61409 5.87373,-75.61877 5.89074,-75.64683 5.94941,-75.62345 5.97833,-75.61469 5.97429,-75.60619 5.98662,-75.62319 5.98832,-75.63127 5.99895,-75.63893 5.9964,-75.65253 5.99172,-75.66784 6.00448,-75.70653 5.97982,-75.6857 5.97429,-75.70101 5.93942,-75.7482 5.94113))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 24924 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018551-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ufmpzs
title: Occurrence Download

description: A dataset containing 168924 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Veronica hederifolia",
 "Year 1956-2022"
 ]
}

 The dataset includes 168924 records from 1314 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035617-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.