cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17876/musewide/dr.1/08947
title: Object GUO_ID 8947 in candels-cdfs-23

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08948
title: Object GUO_ID 8948 in candels-cdfs-25

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08949
title: Object GUO_ID 8949 in candels-cdfs-23

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08950
title: Object GUO_ID 8950 in candels-cdfs-08

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08951
title: Object GUO_ID 8951 in candels-cdfs-21

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08952
title: Object GUO_ID 8952 in candels-cdfs-20

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08953
title: Object GUO_ID 8953 in candels-cdfs-24

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08954
title: Object GUO_ID 8954 in candels-cdfs-23

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08955
title: Object GUO_ID 8955 in candels-cdfs-22

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08956
title: Object GUO_ID 8956 in candels-cdfs-24

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08957
title: Object GUO_ID 8957 in candels-cdfs-20

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08959
title: Object GUO_ID 8959 in candels-cdfs-28

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08960
title: Object GUO_ID 8960 in candels-cdfs-23

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.15468/dl.6apfms
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gypsophila elegans",
 "Country is Latvia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012221-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17876/musewide/dr.1/08961
title: Object GUO_ID 8961 in candels-cdfs-20

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08962
title: Object GUO_ID 8962 in candels-cdfs-20

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.15468/dl.j9aqj4
title: Occurrence Download

description: A dataset containing 1934 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Aves Argentinas / Asociación Ornitológica del Plata - Colección de registros observacionales de aves silvestres argentinas - Lista Narosky: familia Tyrannidae"
 ]
}

 The dataset includes 1934 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012223-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17876/musewide/dr.1/08963
title: Object GUO_ID 8963 in candels-cdfs-28

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08964
title: Object GUO_ID 8964 in candels-cdfs-24

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08965
title: Object GUO_ID 8965 in candels-cdfs-21

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08966
title: Object GUO_ID 8966 in candels-cdfs-25

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08967
title: Object GUO_ID 8967 in candels-cdfs-19

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08968
title: Object GUO_ID 8968 in candels-cdfs-03

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08970
title: Object GUO_ID 8970 in candels-cdfs-23

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08971
title: Object GUO_ID 8971 in candels-cdfs-20

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08973
title: Object GUO_ID 8973 in candels-cdfs-02

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08974
title: Object GUO_ID 8974 in candels-cdfs-13

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08975
title: Object GUO_ID 8975 in candels-cdfs-19

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08976
title: Object GUO_ID 8976 in candels-cdfs-08

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08978
title: Object GUO_ID 8978 in candels-cdfs-28

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08979
title: Object GUO_ID 8979 in candels-cdfs-23

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08980
title: Object GUO_ID 8980 in candels-cdfs-23

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08981
title: Object GUO_ID 8981 in candels-cdfs-20

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.15468/dl.8yxsf3
title: Occurrence Download

description: A dataset containing 5327 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bonnemaisonia hamifera",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Living Specimen, Occurrence evidence)"
 ]
}

 The dataset includes 5327 records from 79 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012224-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17876/musewide/dr.1/08982
title: Object GUO_ID 8982 in candels-cdfs-02

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08983
title: Object GUO_ID 8983 in candels-cdfs-02

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08984
title: Object GUO_ID 8984 in candels-cdfs-07

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08986
title: Object GUO_ID 8986 in candels-cdfs-22

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.21947/2588707
title: Buoy 130 - Lidar / Reviewed Data Reformatted with Corrected Wind Direction Profiles

description: This dataset contains 10-minute averaged data from DOE Buoy 130 during WFIP-3. This includes wind speed and corrected wind direction profiles from a ZX 300 M lidar as well as surface meteorology from the ZX and from the WXT sensors.
DOI: 10.15468/dl.bk59pc
title: Occurrence Download

description: A dataset containing 134 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Aves Argentinas / Asociación Ornitológica del Plata - Colección de registros observacionales de aves silvestres argentinas - Lista Narosky: familia Strigidae"
 ]
}

 The dataset includes 134 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012226-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17876/musewide/dr.1/08988
title: Object GUO_ID 8988 in candels-cdfs-19

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08989
title: Object GUO_ID 8989 in candels-cdfs-24

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08990
title: Object GUO_ID 8990 in candels-cdfs-08

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08991
title: Object GUO_ID 8991 in candels-cdfs-21

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08992
title: Object GUO_ID 8992 in candels-cdfs-23

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08994
title: Object GUO_ID 8994 in candels-cdfs-28

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08995
title: Object GUO_ID 8995 in candels-cdfs-19

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08996
title: Object GUO_ID 8996 in candels-cdfs-24

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.6084/m9.figshare.30152964
title: Dynamic Decision Making With Individualized Variable Selection

description: Physicians today have access to a variety of tests for diagnosing and prognosticating medical conditions. Ideally, they would apply a high-quality prediction model utilizing all relevant features to facilitate appropriate decision-making (e.g., treatment selection; risk assessment). However, some of these features incur additional costs and are not readily available to patients and physicians. In practice, predictors are typically gathered sequentially, i.e., physicians continually evaluate information dynamically until sufficient information is acquired to make a reasonable confidence decision. More importantly, the prospective information to collect may differ for each patient and depend on the predictor values already known. In this paper, we design a novel adaptive prediction rule to determine the optimal order of acquiring features in predicting a clinical outcome of interest. The objective is to maximize prediction accuracy while minimizing the cost associated with measuring features for individual subjects. To achieve this, we employ reinforcement learning, where the agent decides the best action at each step: either making a final clinical decision or continuing to collect new predictors based on the current state of knowledge. Extensive simulation studies have been conducted to evaluate the efficacy of the proposed strategy. Additionally, real examples are presented to illustrate the practical utility.
DOI: 10.6084/m9.figshare.30152964.v1
title: Dynamic Decision Making With Individualized Variable Selection

description: Physicians today have access to a variety of tests for diagnosing and prognosticating medical conditions. Ideally, they would apply a high-quality prediction model utilizing all relevant features to facilitate appropriate decision-making (e.g., treatment selection; risk assessment). However, some of these features incur additional costs and are not readily available to patients and physicians. In practice, predictors are typically gathered sequentially, i.e., physicians continually evaluate information dynamically until sufficient information is acquired to make a reasonable confidence decision. More importantly, the prospective information to collect may differ for each patient and depend on the predictor values already known. In this paper, we design a novel adaptive prediction rule to determine the optimal order of acquiring features in predicting a clinical outcome of interest. The objective is to maximize prediction accuracy while minimizing the cost associated with measuring features for individual subjects. To achieve this, we employ reinforcement learning, where the agent decides the best action at each step: either making a final clinical decision or continuing to collect new predictors based on the current state of knowledge. Extensive simulation studies have been conducted to evaluate the efficacy of the proposed strategy. Additionally, real examples are presented to illustrate the practical utility.
DOI: 10.17876/musewide/dr.1/08997
title: Object GUO_ID 8997 in candels-cdfs-02

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/08998
title: Object GUO_ID 8998 in candels-cdfs-24

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.15468/dl.7khveg
title: Occurrence Download

description: A dataset containing 5963 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 {
 "or" : [
 "Geometry POLYGON((-82.70017 26.4253,-79.2634 26.53362,-79.55383 29.3765,-78.97297 31.5253,-73.45479 35.40055,-67.74298 40.87813,-58.25558 44.84968,-47.02559 46.47336,-52.44696 56.96644,-56.31937 62.77991,-81.49004 64.16327,-83.039 57.38624,-88.07314 61.14207,-104.7245 63.65234,-119.61572 63.51714,-127.45652 63.71512,-122.46412 54.00265,-108.2585 48.32947,-100.86068 47.83023,-95.33849 43.99477,-87.24506 38.79854,-98.40986 34.75924,-100.99683 30.17531,-97.59293 26.58987,-88.46038 23.6407,-82.70017 26.4253))",
 "Geometry POLYGON((-125.82264 35.68475,-119.92254 38.13556,-120.60332 46.25954,-125.55033 45.7603,-125.82264 35.68475))",
 "Geometry POLYGON((-71.50984 3.32742,-70.78607 0.03024,-61.72414 -4.53854,-55.85279 4.83074,-60.43667 12.22928,-70.86649 10.24345,-71.50984 3.32742))"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Sarraceniaceae"
 ]
}

 The dataset includes 5963 records from 163 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012227-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17876/musewide/dr.1/08999
title: Object GUO_ID 8999 in candels-cdfs-02

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09000
title: Object GUO_ID 9000 in candels-cdfs-23

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.15468/dl.nrhfkt
title: Occurrence Download

description: A dataset containing 385 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Aves Argentinas / Asociación Ornitológica del Plata - Colección de registros observacionales de aves silvestres argentinas - Lista Narosky: familia Laridae"
 ]
}

 The dataset includes 385 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012228-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17876/musewide/dr.1/09001
title: Object GUO_ID 9001 in candels-cdfs-13

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09002
title: Object GUO_ID 9002 in candels-cdfs-24

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.6084/m9.figshare.30152967.v1
title: Kernel Spectral Joint Embeddings for High-Dimensional Noisy Datasets Using Duo-Landmark Integral Operators

description: Integrative analysis of multiple heterogeneous datasets has arised in many research fields. Existing approaches oftentimes suffer from limited power in capturing nonlinear structures, insufficient account of noisiness and effects of high-dimensionality, lack of adaptivity to signals and sample sizes imbalance, and their results are sometimes difficult to interpret. To address these limitations, we propose a kernel spectral method that achieves joint embeddings of two independently observed high-dimensional noisy datasets. The proposed method automatically captures and leverages shared low-dimensional structures across datasets to enhance embedding quality. The obtained low-dimensional embeddings can be used for downstream tasks such as simultaneous clustering, data visualization, and denoising. The proposed method is justified by rigorous theoretical analysis, which guarantees its consistency in capturing the signal structures, and provides a geometric interpretation of the embeddings. Under a joint manifolds model framework, we establish the convergence of the embeddings to the eigenfunctions of some natural integral operators. These operators, referred to as duo-landmark integral operators, are defined by the convolutional kernel maps of some reproducing kernel Hilbert spaces (RKHSs). These RKHSs capture the underlying, shared low-dimensional nonlinear signal structures between the two datasets. Our numerical experiments and analyses of two pairs of single-cell omics datasets demonstrate the empirical advantages of the proposed method over existing methods in both embeddings and several downstream tasks. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.17876/musewide/dr.1/09003
title: Object GUO_ID 9003 in candels-cdfs-24

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.6084/m9.figshare.30152967
title: Kernel Spectral Joint Embeddings for High-Dimensional Noisy Datasets Using Duo-Landmark Integral Operators

description: Integrative analysis of multiple heterogeneous datasets has arised in many research fields. Existing approaches oftentimes suffer from limited power in capturing nonlinear structures, insufficient account of noisiness and effects of high-dimensionality, lack of adaptivity to signals and sample sizes imbalance, and their results are sometimes difficult to interpret. To address these limitations, we propose a kernel spectral method that achieves joint embeddings of two independently observed high-dimensional noisy datasets. The proposed method automatically captures and leverages shared low-dimensional structures across datasets to enhance embedding quality. The obtained low-dimensional embeddings can be used for downstream tasks such as simultaneous clustering, data visualization, and denoising. The proposed method is justified by rigorous theoretical analysis, which guarantees its consistency in capturing the signal structures, and provides a geometric interpretation of the embeddings. Under a joint manifolds model framework, we establish the convergence of the embeddings to the eigenfunctions of some natural integral operators. These operators, referred to as duo-landmark integral operators, are defined by the convolutional kernel maps of some reproducing kernel Hilbert spaces (RKHSs). These RKHSs capture the underlying, shared low-dimensional nonlinear signal structures between the two datasets. Our numerical experiments and analyses of two pairs of single-cell omics datasets demonstrate the empirical advantages of the proposed method over existing methods in both embeddings and several downstream tasks. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.17876/musewide/dr.1/09004
title: Object GUO_ID 9004 in candels-cdfs-21

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09006
title: Object GUO_ID 9006 in candels-cdfs-20

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09007
title: Object GUO_ID 9007 in candels-cdfs-13

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09008
title: Object GUO_ID 9008 in candels-cdfs-20

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09009
title: Object GUO_ID 9009 in candels-cdfs-02

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09010
title: Object GUO_ID 9010 in candels-cdfs-19

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09011
title: Object GUO_ID 9011 in candels-cdfs-02

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09014
title: Object GUO_ID 9014 in candels-cdfs-23

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09015
title: Object GUO_ID 9015 in candels-cdfs-23

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09017
title: Object GUO_ID 9017 in candels-cdfs-20

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09018
title: Object GUO_ID 9018 in candels-cdfs-21

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09019
title: Object GUO_ID 9019 in candels-cdfs-13

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09021
title: Object GUO_ID 9021 in candels-cdfs-13

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.21227/gaq8-k852
title: "T2VAttackBench: A High-Quality Dataset for Adversarial Attack on Text-to-video Diffusion Models"

description: "T2VAttackBench is a textual benchmark intended for rigorous evaluation of adversarial robustness on text-to-video (T2V) diffusion models. While prior resources such as VBench provide a comprehensive benchmark, the limited video generation quality makes them suboptimal for robustness studies. The dataset focuses on two distinct evaluation objectives: semantic fidelity (how well generated video matches the prompt) and temporal dynamics (quality of motion and dynamics). A two-stage filtering process (model-centric selection and consensus filtering) ensures that all prompts yield semantically faithful and temporally coherent videos, thereby isolating degradation caused by adversarial factors rather than intrinsic model weaknesses.The final dataset includes 105 semantic prompts, describing visually rich and contextually coherent scenarios, and 52 temporal prompts, specifying fine-grained motion and dynamic video content. These curated prompts provide a reliable foundation for evaluating semantic fidelity and temporal consistency in T2V generation, while facilitating reproducible research and the development of more robust and secure video generation systems."
DOI: 10.17876/musewide/dr.1/09022
title: Object GUO_ID 9022 in candels-cdfs-23

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09026
title: Object GUO_ID 9026 in candels-cdfs-19

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09028
title: Object GUO_ID 9028 in candels-cdfs-07

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09030
title: Object GUO_ID 9030 in candels-cdfs-21

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09031
title: Object GUO_ID 9031 in candels-cdfs-19

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09032
title: Object GUO_ID 9032 in candels-cdfs-28

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09033
title: Object GUO_ID 9033 in candels-cdfs-02

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.
DOI: 10.17876/musewide/dr.1/09034
title: Object GUO_ID 9034 in candels-cdfs-13

description: A spectrum of the MUSE-Wide survey DR1 photometric catalog.