cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/tst-sqj-wj0
title: Illicium anisatum L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/b6w-ay9-rw3
title: Kadsura japonica (L. fil.) Dunal

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/qqt-ndl-3gt
title: Drimys insipida (R. Br.) Pilg.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/7qk-k2b-0c2
title: Drimys Forster & G. Forst.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/prf-8az-dfw
title: Magnolia virginiana L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/b86-sce-c8l
title: Magnolia obovata Thunb.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/cx6-yc9-qz5
title: Magnolia kobus DC.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/nd0-lkd-gvy
title: Magnolia × soulangeana Soul.-Bod.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/56d-dfz-sfm
title: Magnolia × soulangeana Soul.-Bod.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/vet-xdy-n6q
title: Magnolia × soulangeana Soul.-Bod.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/m6w-m31-8h9
title: Magnolia × soulangeana Soul.-Bod.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/7p3-ldp-cjx
title: Magnolia Plum. ex L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/gsa-p5f-3d9
title: Magnolia splendens Urb.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/6cw-xas-dnx
title: Gossypium barbadense L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/djx-p7c-k1v
title: Gossypium barbadense L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/vqv-bpy-bb5
title: Gossypium barbadense L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/cvz-ctm-cxx
title: Gossypium barbadense L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/r4e-584-lzy
title: Gossypium L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/ybd-9b3-m9j
title: Gossypium tomentosum Nutt. ex Seem.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/43m-5g4-xxl
title: Gossypium L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/s2f-9wh-syd
title: Gossypium L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/b19-dg8-c80
title: Gossypium L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/tba-9wz-gel
title: Gossypium L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/mp2-6sb-296
title: Gossypium L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/a63-r19-wyw
title: Abelmoschus moschatus (L.) Medik.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/lzf-2yt-xyt
title: Abelmoschus moschatus (L.) Medik.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/m7x-c98-fy1
title: Abelmoschus moschatus (L.) Medik.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/qdc-2mv-3t2
title: Abelmoschus esculentus (L.) Moench

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

description: A dataset containing 34596209 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is Mexico"
 ]
}

 The dataset includes 34596209 records from 3700 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0107269-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/t0c182pz
title: HW-NAS-Bench

description: The dataset used in the paper for Hardware-adaptive Efﬁcient Latency Prediction for NAS via Meta-Learning
DOI: 10.7910/dvn/q2j7qf
title: Replication Data for: When Cues Collide: Partisan Signals and the Dynamics of Ethnic Voting in Nonpartisan Local Elections

description: These files are to allow for the replication of the figures and tables presented in the forthcoming Urban Affairs Review article titled, "When Cues Collide: Partisan Signals and the Dynamics of Ethnic Voting in Nonpartisan Local Elections." 

That article's abstract is as follows:
Voters often use candidate ethnicity as a shortcut in low-information elections, but
partisan cues might shift the weight of ethnic signals. This paper examines how par-
tisan information moderates the relationship between co-ethnicity and vote choice in
103 multiethnic, nonpartisan mayoral elections in California (2010–2021). Although
party labels are absent from ballots, candidates sometimes publicly signal a partisan
affiliation. Using precinct-level election returns and voter registration data, I find that
Latino candidates receive greater support as the share of Latino registrants in a precinct increases—but this relationship changes when party information is revealed. When facing a known non-Latino Democrat, Latino candidates receive less support than their nonpartisan counterparts; when facing a known non-Latino Republican, they gain support. These effects are strongest in precincts with more Latino Democrats. The findings highlight how party cues reshape ethnic voting, even without formal partisan labels.
DOI: 10.57702/ks1drgwc
title: Bilingual Synchronization

description: The dataset used for bilingual synchronization task, which includes simulated interactive MT, translating with Translation Memory (TM) and TM cleaning.
DOI: 10.57702/mtqds7rn
title: SIGNet

description: A custom physically rendered dataset for intrinsic image decomposition of indoor scenes.
DOI: 10.57702/pisvm7oa
title: AIS and PlanetScope dataset for ship detection and transshipment

description: AIS data and PlanetScope satellite imagery dataset used for ship detection and transshipment event identification.
DOI: 10.57702/sxtse8kc
title: Commonsense dataset

description: A commonsense dataset, specifically chosen for its crowd-worker annotations, was utilized in this study.
DOI: 10.15468/dl.m5rqxs
title: Occurrence Download

description: A dataset containing 11804 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mytilus galloprovincialis",
 "HasCoordinate is true"
 ]
}

 The dataset includes 11804 records from 211 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0080029-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/cc46ejwl
title: MAWI-Lab

description: The MAWI-Lab dataset contains labeled network activity data for benign behavior.
DOI: 10.57702/03gwyp5u
title: Navigating Assistance System for Quadcopter with Deep Reinforcement Learning

description: A deep reinforcement learning method for quadcopter to bypass obstacles in 3D environment.
DOI: 10.57702/mhx97qjd
title: Real-Time All-Purpose Segment Anything Model

description: Advanced by transformer architecture, vision foundation models (VFMs) achieve remarkable progress in performance and generalization ability. Segment Anything Model (SAM) is one remarkable model that can achieve generalized segmentation. However, most VFMs cannot run in real-time, which makes it difficult to transfer them into several products.
DOI: 10.57702/i71z2vh9
title: Shuttle Dataset

description: The shuttle dataset is used for anomaly clustering and visualization. It contains 9 numerical attributes and 7 classes.
DOI: 10.57702/proza1an
title: TrackML dataset

description: The TrackML dataset is a simulated set of proton-proton collision events originally developed for the TrackML Particle Tracking Challenge. TrackML events are generated with 200 pileup interactions on average, simulating the high-pileup conditions expected at the HL-LHC.
DOI: 10.57702/nkwmzleo
title: CausalFairML via RPID

description: A decision can be defined as fair if equal individuals are treated equally and unequals are treated unequally. Adopting this definition, the task of designing machine learning (ML) models that mitigate unfairness in automated decision-making systems must include causal thinking when introducing protected attributes: Following a recent proposal, we define individuals as being normatively equal if they are equal in a fictitious, normatively desired (FiND) world, where the protected attributes have no (direct or indirect) causal effect on the target.
DOI: 10.57702/ewo50z5h
title: PH2 dataset

description: The PH2 dataset used for evaluation of the proposed deep learning model for automatic skin lesion segmentation on dermoscopic images.
DOI: 10.57702/6p04ad3k
title: Power Systems Data Fusion

description: The dataset used in this paper is a combination of three data sources: state estimation, smart meters, and machine learning forecasts. The data sources are related to power systems and include measurements of active power injection, energy demand, solar generation, and voltage magnitudes.
DOI: 10.57702/zaxc34o3
title: Task-Aware Low-Rank Adaptation of Segment Anything Model

description: The Segment Anything Model (SAM) has been proven to be a powerful foundation model for image segmentation tasks, which is an important task in computer vision. However, the transfer of its rich semantic information to multiple different downstream tasks remains unexplored. In this paper, we propose the Task-Aware Low-Rank Adaptation (TA-LoRA) method, which enables SAM to work as a foundation model for multi-task learning.
DOI: 10.57702/2dssafem
title: NoW Benchmark

description: The NoW Benchmark consists of a validation set (20 subjects) and a test set (80 subjects). In the validation set, each subject has a 3D face scan corresponding to a neutral expression.
DOI: 10.57702/31j5cdt7
title: European Air Quality Portal

description: European Air Quality Portal dataset containing pollutant concentration data from EU member states
DOI: 10.5281/zenodo.10878354
title: TopTagXL: A top quark tagging dataset for large scale studies in jet tagging

description: A large set of Monte Carlo simulated LHC events for the development of deep learning tools in high-energy physics. The dataset contains 135M events divided into 100M events for training, 25M events for testing, and 10M events for validation. We evenly simulate QCD jets, originating from light hadrons, and jets originating from top quarks. The training files 1 to 5 include QCD jets, while the files 6 to 10 are top jets. 

Events are generated with Pythia8.310, and the detector simulation is done with Delphes3.5.0 using the CMS configuration. The cards used for the generation of the scattering process and the detector simulation are included in this repository:



"qcd_jets_pythia_TopTagXL.cmnd" and "top_jets_pythia_TopTagXL.cmnd" are the Pythia cards used for the generation of the scattering events. They contain a parton-level cut at 400 GeV, and top quarks are only allowed to decay hadronically;

"delphes_card_CMS_TopTagXL.tcl" is the default CMS card in Delphes3.5.0 to which we added the smearing of the charged tracks impact parameter as parametrized in "trackResolutionCMS.tcl". The parametrization is already available in Delphes3.5.0 and included here for completeness.


We only include fully reconstructed jets, i.e. we require $\Delta R < 0.8$ between the clustered jet kinematics and the parton-level kinematics of the top decays. We further require all jets to have $500 < p_T < 1000$ GeV and $|\eta| < 2$. Jets are clustered using FastJet with the anti-$k_T$ algorithm and a radius of $R=0.8$.

The dataset contains up to 128 constituents with the following information:



"part_px", "part_py", "part_pz", "part_energy": four-momenta of the constituent;

"part_charge": an integer in (-1, 0, 1) indicating the charge of the constituent;

a one-hot encoded particle identification number indicated with the labels "part_"+"isChargedHadron", "isNeutralHadron", "isPhoton", "isElectron", "isMuon";

four trajectory displacement variable: "part_D0", "part_ErrD0", "part_DZ", and "part_ErrDZ".


The dataset format is fully compatible with the weaver package.

This is an extension of the "Top Quark Tagging Reference Dataset". The generation cuts are unchanged (see Pythia cards) but several simulation improvements are included in the generation pipeline. The updated detector card provides more accurate simulations and a larger set of jet features.
DOI: 10.5281/zenodo.10878355
title: TopTagXL: A top quark tagging dataset for large scale studies in jet tagging

description: A large set of Monte Carlo simulated LHC events for the development of deep learning tools in high-energy physics. The dataset contains 135M events divided into 100M events for training, 25M events for testing, and 10M events for validation. We evenly simulate QCD jets, originating from light hadrons, and jets originating from top quarks. The training files 1 to 5 include QCD jets, while the files 6 to 10 are top jets. 

Events are generated with Pythia8.310, and the detector simulation is done with Delphes3.5.0 using the CMS configuration. The cards used for the generation of the scattering process and the detector simulation are included in this repository:



"qcd_jets_pythia_TopTagXL.cmnd" and "top_jets_pythia_TopTagXL.cmnd" are the Pythia cards used for the generation of the scattering events. They contain a parton-level cut at 400 GeV, and top quarks are only allowed to decay hadronically;

"delphes_card_CMS_TopTagXL.tcl" is the default CMS card in Delphes3.5.0 to which we added the smearing of the charged tracks impact parameter as parametrized in "trackResolutionCMS.tcl". The parametrization is already available in Delphes3.5.0 and included here for completeness.


We only include fully reconstructed jets, i.e. we require $\Delta R < 0.8$ between the clustered jet kinematics and the parton-level kinematics of the top decays. We further require all jets to have $500 < p_T < 1000$ GeV and $|\eta| < 2$. Jets are clustered using FastJet with the anti-$k_T$ algorithm and a radius of $R=0.8$.

The dataset contains up to 128 constituents with the following information:



"part_px", "part_py", "part_pz", "part_energy": four-momenta of the constituent;

"part_charge": an integer in (-1, 0, 1) indicating the charge of the constituent;

a one-hot encoded particle identification number indicated with the labels "part_"+"isChargedHadron", "isNeutralHadron", "isPhoton", "isElectron", "isMuon";

four trajectory displacement variable: "part_D0", "part_ErrD0", "part_DZ", and "part_ErrDZ".


The dataset format is fully compatible with the weaver package.

This is an extension of the "Top Quark Tagging Reference Dataset". The generation cuts are unchanged (see Pythia cards) but several simulation improvements are included in the generation pipeline. The updated detector card provides more accurate simulations and a larger set of jet features.
DOI: 10.57702/5dfgsoa0
title: Set

description: The Set dataset is a card game that forms a simple-to-describe but challenging relational task.
DOI: 10.57702/9012endb
title: Search Engine IQ Test

description: The dataset used in the paper to evaluate the intelligence of search engines and human subjects.
DOI: 10.15468/dl.8k7j8u
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.777821 23.78953, 88.70107899999999 23.783953, 88.626245 23.767361, 88.55517999999999 23.740166, 88.48964700000001 23.703045, 88.431268 23.656919, 88.381484 23.602933, 88.341516 23.542424, 88.312341 23.47689, 88.294663 23.407951, 88.288899 23.337308, 88.29517300000001 23.266701, 88.31331299999999 23.197869, 88.342854 23.132501, 88.383056 23.072201, 88.43292099999999 23.018446, 88.491219 22.972552, 88.556517 22.93564, 88.627217 22.908611, 88.701589 22.892126, 88.777821 22.886585, 88.854052 22.892126, 88.928425 22.908611, 88.99912399999999 22.93564, 89.06442300000001 22.972552, 89.122721 23.018446, 89.172586 23.072201, 89.212788 23.132501, 89.242329 23.197869, 89.260468 23.266701, 89.26674300000001 23.337308, 89.26097900000001 23.407951, 89.2433 23.47689, 89.214125 23.542424, 89.17415800000001 23.602933, 89.12437300000001 23.656919, 89.065995 23.703045, 89.000462 23.740166, 88.92939699999999 23.767361, 88.854563 23.783953, 88.777821 23.78953))"},{"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 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0080030-251120083545085/datasets/export for details.
DOI: 10.15468/dl.zo0qky
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002717-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mtmenc
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058789-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dmnv29
title: Occurrence Download

description: A dataset containing 55086845 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Occurrence evidence, Living Specimen, Fossil, Specimen, Material sample)",
 "CoordinateUncertaintyInMeters 0.0-1000000.0",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 55086845 records from 3006 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0175798-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4z2p4m
title: Occurrence Download

description: A dataset containing 104 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-90.0430944999999952 29.9455756999999991, -90.0430944999999952 29.9464206399999995, -90.0430944999999952 29.9475673499999999, -90.0430944999999952 29.9491968799999988, -90.0430944999999952 29.9505850000000002, -90.0430944999999952 29.9517317100000007, -90.0430944999999952 29.9531801900000012, -90.0430944999999952 29.9544476000000017, -90.0421917799999960 29.9544476000000017, -90.0411085300000025 29.9544476000000017, -90.0394234599999947 29.9544476000000017, -90.0384003799999988 29.9544476000000017, -90.0370764000000037 29.9544476000000017, -90.0370764000000037 29.9532405399999995, -90.0370764000000037 29.9523955999999991, -90.0370764000000037 29.9511281800000013, -90.0370764000000037 29.9502228899999992, -90.0370764000000037 29.9489554700000014, -90.0370764000000037 29.9483519399999985, -90.0370764000000037 29.9471448799999997, -90.0370764000000037 29.9455756999999991, -90.0380994799999996 29.9455756999999991, -90.0394234599999947 29.9455756999999991, -90.0411687099999938 29.9455756999999991, -90.0421316000000047 29.9455756999999991, -90.0430944999999952 29.9455756999999991))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1932",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Observation, Specimen, Machine Observation)"
 ]
}

 The dataset includes 104 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0091676-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8yqqpt
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Heliophila affinis Sond."
 ]
}

 The dataset includes 38 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0150121-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.97dpts
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Haltichella nipponensis Habu, 1960",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0217094-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7mdxfj
title: Occurrence Download

description: A dataset containing 80 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aubrieta libanotica Boiss."
 ]
}

 The dataset includes 80 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0157314-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b7mu4g
title: Occurrence Download

description: A dataset containing 187 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Heliophila diffusa (Thunb.) DC."
 ]
}

 The dataset includes 187 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0150162-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.