cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/rjc-82g-9wk
title: Scrophularia lyrata Willd.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/rjh-j5g-s9l
title: Hieracium umbellatum subsp. umbellatum

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/zw1-h5q-1zg
title: Ardisia crenulata Vent.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/bvr-dgn-mdm
title: Asteraceae Dumort.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/pcg-p7t-fdm
title: Crepis nicaeensis Balb. ex Pers.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/qwr-vxg-6kd
title: Hieracium alpinum subsp. halleri (Vill.) Ces.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/bwq-nhx-zra
title: Psychilis atropurpurea (Willd.) Sauleda

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/mwz-k80-atg
title: Taraxacum obovatum (Willd.) DC.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/jvz-pwb-2r6
title: Hieracium alpinum L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/ncy-057-z4y
title: Hieracium pyrenaicum Jord.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/jf1-tb9-7c7
title: Hieracium villosum Jacq.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/w86-rwt-stt
title: Crepis sancta subsp. sancta

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/lbj-jme-830
title: Crepis leontodontoides All.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/508-3gh-hdc
title: Lactuca serriola L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/930-9bc-zlp
title: Mycelis muralis (L.) Dumort.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/4j7-pxv-ffq
title: Pteronia camphorata L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/jw8-0je-ynr
title: Hieracium villosum subsp. villosissimum (Nägeli) Nägeli & Peter

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/a8t-aj0-qrb
title: Helminthotheca echioides (L) Holub

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/46y-z4q-rpm
title: Tragopogon L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/95k-egt-09v
title: Sonchus oleraceus L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/7s0-jav-8dp
title: Quercus pubescens Willd.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/wjj-dje-s4k
title: Peperomia amplexicaulis (Sw.) A. Dietr.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.15468/dl.j786zs
title: Occurrence Download

description: A dataset containing 502946 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ipomoea L."
 ]
}

 The dataset includes 502946 records from 1632 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106222-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.82144/45674eb3
title: NOAA Ship Gordon Gunter Vessel Line-transect Survey 2021 (GU2103)

description: Original provider:
NOAA Southeast Fisheries Science Center (NEFSC)

Dataset credits:
NOAA Southeast Fisheries Science Center (SEFSC)

Abstract:
As part of the Atlantic Marine Assessment Program for Protected Species (AMAPPS), the Southeast Fisheries Science Center (SEFSC) conducted a shipboard survey of the U.S. waters of the Western North Atlantic (WNA) from approximately the 200m isobath out to the U.S. Exclusive Economic Zone (EEZ). The survey was conducted between June 12 and September 5, 2021 onboard the NOAA Ship Gordon Gunter, split into three survey legs.

Due to the COVID-19 pandemic, the scientific staff sheltered-in-place (SIP) before the start of the survey and before leg 3. Multiple days were lost due to mechanical problems with the ship and unfavorable weather conditions. The final portion of this survey transited through the Gulf of America (formerly Gulf of Mexico). The survey was designed for a two-team independent visual observer approach with distance sampling to estimate the detection probabilities for marine mammal sightings.

A sighting constituted a single or group of marine mammals (whales and dolphins) seen at the same location and time. This dataset includes marine mammal visual observation data and effort points with surveying conditions that can be used in abundance and density modeling per visual line-transect survey protocols.

This dataset also includes opportunistic marine mammal photographs and non-marine mammal sightings such as sea turtles. Sea bird observers conducted counts of all birds detected within a 300m strip transect from the ship during all legs of the survey (see additional references and data below). The scientific echosounder (EK) recorded data during the nighttime from the end of the survey day until commencement of acoustic effort, the following morning (CIMAS, NOAA Southeast Fisheries Science Center, 2024). Environmental data was collected at predetermined stations using a conductivity, temperature, and depth sensor (CTD) unit (Wanninkhof, Sullivan, and Pierrot, 2021).
DOI: 10.15468/dl.3duar6
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.800303 24.343606, 88.72323 24.338029, 88.64807500000001 24.321437, 88.576706 24.294242, 88.51089399999999 24.25712, 88.452268 24.210994, 88.402275 24.157009, 88.36214200000001 24.096501, 88.332849 24.03097, 88.315102 23.962034, 88.309321 23.891396, 88.315629 23.820795, 88.333851 23.751969, 88.36352100000001 23.686607, 88.403896 23.626315, 88.45397199999999 23.572567, 88.51251499999999 23.526679, 88.578085 23.489772, 88.64907700000001 23.462748, 88.72375700000001 23.446265, 88.800303 23.440726, 88.87684900000001 23.446265, 88.95152899999999 23.462748, 89.022521 23.489772, 89.08809100000001 23.526679, 89.14663400000001 23.572567, 89.19671 23.626315, 89.23708499999999 23.686607, 89.266755 23.751969, 89.284977 23.820795, 89.291285 23.891396, 89.285504 23.962034, 89.267757 24.03097, 89.23846399999999 24.096501, 89.198331 24.157009, 89.148338 24.210994, 89.08971200000001 24.25712, 89.0239 24.294242, 88.95253099999999 24.321437, 88.877376 24.338029, 88.800303 24.343606))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0080522-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fnqbmf
title: Occurrence Download

description: A dataset containing 25975 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eucalyptus tereticornis"
 ]
}

 The dataset includes 25975 records from 276 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0080519-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/o4izi7ts
title: BTSR

description: BTSR contains 62 classes of high-resolution images typically resized to 224 × 224 × 3.
DOI: 10.57702/u0n4uaud
title: MPI3D dataset

description: The dataset used in the paper is a MPI3D dataset, which contains 3D images of objects with varying sizes and colors.
DOI: 10.57702/f3ikes41
title: Return-freight insurance fraud detection

description: Return-freight insurance dataset, containing accounts that have filed a claim over the past months, with device UMIDs used by these accounts in the past 40 days as graph nodes.
DOI: 10.57702/67cr47k0
title: MotionFollower

description: The dataset used in the paper is not explicitly described, but it is mentioned that the authors collect 3K videos (60-90 seconds long) from the internet to train their model.
DOI: 10.57702/a7olrvc8
title: BBOB suite

description: The BBOB suite is a collection of 24 noise-free real-parameter single objective optimization problems of different landscape complexity.
DOI: 10.57702/oj47wuh6
title: Smart card dataset of bus ridership

description: Three-month smart card dataset of bus ridership, containing over 10 million observations allied with detailed weather measurements, trip length, calendar events, and built environment features.
DOI: 10.57702/wnxl1g4w
title: HES dataset

description: The dataset used in this study is the Hospital Episode Statistics (HES) dataset, which is a part of the CPRD dataset. It contains data on hospitalizations, outpatient visits, accident and emergency for all admissions to National Health Service (NHS) hospitals in England.
DOI: 10.57702/gachz091
title: MFCC Features

description: MFCC features extracted from speech signal, with 39 features (13 MFCC features and 26 delta and delta-delta features).
DOI: 10.57702/oac1j4ff
title: ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents

description: The ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents dataset consists of 20,000 document images representing about 10,000 writers, divided into three types: manuscripts, letters, and charters.
DOI: 10.57702/pxzrlvti
title: How2Sign

description: How2Sign is a large-scale continuous American Sign Language (ASL) dataset. After removing invalid text-video pairs, we retain 31019, 1738, and 2348 available pairs in the training, validation, and testing sets.
DOI: 10.57702/151vw59h
title: Augmenting Interpretable Models with LLMs during Training

description: Aug-GAM and Aug-Tree are two instantiations of Aug-imodels, a framework for leveraging the knowledge learned by LLMs to build extremely eﬃcient and interpretable models.
DOI: 10.5281/zenodo.18098714
title: BikeZ-ETH - A Mass-Cycling Trajectory Dataset from a Controlled Experiment

description: This dataset presents aerial video recordings and trajectory data collected during a controlled mass-cycling experiment during the Cycling Research Board Annual Meeting in Zürich on 06th September 2024 at Hönggerberg Campus of ETH Zürich. 28 cyclists were recorded for a total duration of 30 minutes with a drone from above, and bicycle trajectories were extracted using computer vision and Kalman filtering methodology. As part of the controlled experiment, the number of simultaneous cyclists on the circular track varied between 6 and 22, two lane widths were tested (2.5 and 3.75m), and the flow was disrupted several times to observe acceleration and deacceleration manoeuvres.

The folder VIDEOS contains nine video recordings covering a duration of around half an hour (30:07) and 45,175 frames in total, at a framerate of 25 frames per second, and with a resolution of 3840 x 2160 pixels. The videos show more than 30 bicycles riding on the circular track of the Albert Einstein Garage at ETH Zürich Campus Hönggerberg (Zürich, Switzerland) during various experiments. The videos are provided in MP4 format.

The folder TRAJECTORIES contains the trajectories for each video, sequence, bicycle, and frame an exact bicycle position. The bicycle trajectories are provided as zipped CSV files, seperated by the comma symbol.Each row consists of 12 columns:



(1) Vehicle_ID

(2) Frame_ID

(3) GlobalTime (in seconds)

(4) Cartesian_X (in meter)

(5) Cartesian_Y (in meter)

(6) Polar_X (in radians)

(7) Polar_Y (in meter)

(8) v_Length (in meter)

(9) v_Width (in meter)

(10) v_Vel (in m/s)

(11) v_Angle (in radians)

(12) v_AngleVel (in rad/s)


 

Source Code Repository

The code to extract and process the bicycle trajectories from the videos is available on GitHub: https://github.com/DerKevinRiehl/mass_cycling_experiment

 

Demonstrations



https://youtu.be/-UbFwMcpJNk

https://youtu.be/b4SxqPf-qpQ

https://youtu.be/mCQmYtJ3D8w


 

AcknowledgementsWe would like to thank Catherine Elliot and the other organizers of the Cycling Research Board Annual Meeting (CRB2024), and the initiative Pro Velo Kanton Zurich for promoting the mass-cycling experiment amongst all the experiment participants. Additionally, we would like to thank Judith van den Hoeven for facilitating the coordination with participants, and Veloplan GmbH for the provision of bikes. Moreover, we thank following supporters that facilitated the experiment: Linghang Sun, Kimia Chavoshi, Yifan Zhang, Qishen Zhou.

 

Publications



"BikeZ-ETH-MCE -- A Longitudinal Trajectory Dataset from an Controlled Mass-Cycling Experiment" (In Submission in "Scientific Data)

"The Bicycle Fundamental Diagram: Empirical Insights into Bicycle Flow for Sustainable Urban Mobility" (In Submisison in "Nature Scientific Communications")
DOI: 10.57702/xd20swb5
title: MAGIC

description: The MAGIC dataset is a multiclass classification dataset containing 19020 samples, each described by 10 features.
DOI: 10.5281/zenodo.18098713
title: BikeZ-ETH - A Mass-Cycling Trajectory Dataset from a Controlled Experiment

description: This dataset presents aerial video recordings and trajectory data collected during a controlled mass-cycling experiment during the Cycling Research Board Annual Meeting in Zürich on 06th September 2024 at Hönggerberg Campus of ETH Zürich. 28 cyclists were recorded for a total duration of 30 minutes with a drone from above, and bicycle trajectories were extracted using computer vision and Kalman filtering methodology. As part of the controlled experiment, the number of simultaneous cyclists on the circular track varied between 6 and 22, two lane widths were tested (2.5 and 3.75m), and the flow was disrupted several times to observe acceleration and deacceleration manoeuvres.

The folder VIDEOS contains nine video recordings covering a duration of around half an hour (30:07) and 45,175 frames in total, at a framerate of 25 frames per second, and with a resolution of 3840 x 2160 pixels. The videos show more than 30 bicycles riding on the circular track of the Albert Einstein Garage at ETH Zürich Campus Hönggerberg (Zürich, Switzerland) during various experiments. The videos are provided in MP4 format.

The folder TRAJECTORIES contains the trajectories for each video, sequence, bicycle, and frame an exact bicycle position. The bicycle trajectories are provided as zipped CSV files, seperated by the comma symbol.Each row consists of 12 columns:



(1) Vehicle_ID

(2) Frame_ID

(3) GlobalTime (in seconds)

(4) Cartesian_X (in meter)

(5) Cartesian_Y (in meter)

(6) Polar_X (in radians)

(7) Polar_Y (in meter)

(8) v_Length (in meter)

(9) v_Width (in meter)

(10) v_Vel (in m/s)

(11) v_Angle (in radians)

(12) v_AngleVel (in rad/s)


 

Source Code Repository

The code to extract and process the bicycle trajectories from the videos is available on GitHub: https://github.com/DerKevinRiehl/mass_cycling_experiment

 

Demonstrations



https://youtu.be/-UbFwMcpJNk

https://youtu.be/b4SxqPf-qpQ

https://youtu.be/mCQmYtJ3D8w


 

AcknowledgementsWe would like to thank Catherine Elliot and the other organizers of the Cycling Research Board Annual Meeting (CRB2024), and the initiative Pro Velo Kanton Zurich for promoting the mass-cycling experiment amongst all the experiment participants. Additionally, we would like to thank Judith van den Hoeven for facilitating the coordination with participants, and Veloplan GmbH for the provision of bikes. Moreover, we thank following supporters that facilitated the experiment: Linghang Sun, Kimia Chavoshi, Yifan Zhang, Qishen Zhou.

 

Publications



"BikeZ-ETH-MCE -- A Longitudinal Trajectory Dataset from an Controlled Mass-Cycling Experiment" (In Submission in "Scientific Data)

"The Bicycle Fundamental Diagram: Empirical Insights into Bicycle Flow for Sustainable Urban Mobility" (In Submisison in "Nature Scientific Communications")
DOI: 10.57702/yb4zm3p0
title: ARFI dataset

description: The dataset used in the paper for Multiple Track Location Shear Wave Elasticity Imaging (MTL-SWEI) using acoustic radiation force impulse (ARFI) method.
DOI: 10.57702/z4lsgehz
title: VLMCritic

description: The dataset and codebase for the paper Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviors
DOI: 10.57702/ukqxaz0e
title: SARS-COV-2 CT

description: Four COVID-19 CT and X-ray image datasets used for COVID-19 detection
DOI: 10.15156/bio/sh0153752.10fu
title: SH0153752.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0153753.10fu
title: SH0153753.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0153754.10fu
title: SH0153754.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0153755.10fu
title: SH0153755.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0153756.10fu
title: SH0153756.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.