cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13955974
title: Deep Compositional Spatial Models

description: Spatial processes with nonstationary and anisotropic covariance structure are often used when modeling, analyzing, and predicting complex environmental phenomena. Such processes may often be expressed as ones that have stationary and isotropic covariance structure on a warped spatial domain. However, the warping function is generally difficult to fit and not constrained to be injective, often resulting in “space-folding.” Here, we propose modeling an injective warping function through a composition of multiple elemental injective functions in a deep-learning framework. We consider two cases; first, when these functions are known up to some weights that need to be estimated, and, second, when the weights in each layer are random. Inspired by recent methodological and technological advances in deep learning and deep Gaussian processes, we employ approximate Bayesian methods to make inference with these models using graphics processing units. Through simulation studies in one and two dimensions we show that the deep compositional spatial models are quick to fit, and are able to provide better predictions and uncertainty quantification than other deep stochastic models of similar complexity. We also show their remarkable capacity to model nonstationary, anisotropic spatial data using radiances from the MODIS instrument aboard the Aqua satellite.
DOI: 10.6084/m9.figshare.13955974.v1
title: Deep Compositional Spatial Models

description: Spatial processes with nonstationary and anisotropic covariance structure are often used when modeling, analyzing, and predicting complex environmental phenomena. Such processes may often be expressed as ones that have stationary and isotropic covariance structure on a warped spatial domain. However, the warping function is generally difficult to fit and not constrained to be injective, often resulting in “space-folding.” Here, we propose modeling an injective warping function through a composition of multiple elemental injective functions in a deep-learning framework. We consider two cases; first, when these functions are known up to some weights that need to be estimated, and, second, when the weights in each layer are random. Inspired by recent methodological and technological advances in deep learning and deep Gaussian processes, we employ approximate Bayesian methods to make inference with these models using graphics processing units. Through simulation studies in one and two dimensions we show that the deep compositional spatial models are quick to fit, and are able to provide better predictions and uncertainty quantification than other deep stochastic models of similar complexity. We also show their remarkable capacity to model nonstationary, anisotropic spatial data using radiances from the MODIS instrument aboard the Aqua satellite.
DOI: 10.25346/s6/aoyyt6/5at0kj
title: PasoDataRecordA1.tab
DOI: 10.25346/s6/aoyyt6
title: Data Record A1 - Expanded Version of the Appendix of Proveniences, Including Artifact by Minimal Provenience

description: Expanded version of the appendix of proveniences, including artifact by minimal provenience.
DOI: 10.6084/m9.figshare.14390408
title: DataFreeze.v5_2

description: Data-freeze for Sun et al., Nat. Commun., 2021<br>Title: Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidates for targeted treatment<br>
DOI: 10.6084/m9.figshare.14390408.v2
title: DataFreeze.v5_2

description: Data-freeze for PDX-pan-cancer manuscript (2021)<br><br>Title: Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidates for targeted treatment<br>
DOI: 10.25582/data.2023-01.2724499/2299479
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2726009/2299480
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2725589/2299481
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2725981/2299482
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2726238/2299483
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2726059/2299484
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2725897/2299485
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2725728/2299486
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2725773/2299487
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2725562/2299488
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2724409/2299489
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2726217/2299490
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2726111/2299491
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2724480/2299492
title: Data for EMSL Project 50969 from January 2023
DOI: 10.6084/m9.figshare.25018422
title: Code and publicly available data sets from A test of Conserving Nature's Stage: protecting a diversity of geophysical traits can also support a diversity of species at a landscape scale

description: Conserving Nature's Stage (CNS) is a concept from conservation planning that promotes the protection of areas encompassing a broad range of enduring geophysical traits to provide long-term habitat for diverse species. The efficacy of using enduring geophysical characteristics as surrogates for biodiversity, independent of non-geophysical features and when considering finer resolution area selections, has yet to be investigated. Here, we evaluated CNS using 33 fine-scale inventories of vascular plant, non-vascular plant, invertebrate or vertebrate species from 13 areas across three continents. For each inventory, we estimated a continuous multidimensional surrogate defined from topographic and soil estimates of the surveyed plots. We assessed surrogate effectiveness by comparing the species representation of surrogate selected plots to the representation from plots picked randomly and using species information. We then used correlation coefficients to assess the link between the performance and qualities of the inventories, surroundings and surrogates. The CNS surrogate showed positive performance for 24 of the 33 inventories, and among these tests, represented 28 more species than random and 83% of the total number of species on average. We also found a small number of weak correlations between performance and environmental variability, as well as qualities of the surrogate. Our study demonstrates that prioritizing areas for a variety of geophysical characteristics will, in most cases, promote the representation of species. Our findings also point to areas for future research that might enhance CNS surrogacy.This article is part of the Theo Murphy meeting issue ‘Geodiversity science for society’.
DOI: 10.6084/m9.figshare.25018422.v2
title: Code and publicly available data sets from A test of Conserving Nature's Stage: protecting a diversity of geophysical traits can also support a diversity of species at a landscape scale

description: Conserving Nature's Stage (CNS) is a concept from conservation planning that promotes the protection of areas encompassing a broad range of enduring geophysical traits to provide long-term habitat for diverse species. The efficacy of using enduring geophysical characteristics as surrogates for biodiversity, independent of non-geophysical features and when considering finer resolution area selections, has yet to be investigated. Here, we evaluated CNS using 33 fine-scale inventories of vascular plant, non-vascular plant, invertebrate or vertebrate species from 13 areas across three continents. For each inventory, we estimated a continuous multidimensional surrogate defined from topographic and soil estimates of the surveyed plots. We assessed surrogate effectiveness by comparing the species representation of surrogate selected plots to the representation from plots picked randomly and using species information. We then used correlation coefficients to assess the link between the performance and qualities of the inventories, surroundings and surrogates. The CNS surrogate showed positive performance for 24 of the 33 inventories, and among these tests, represented 28 more species than random and 83% of the total number of species on average. We also found a small number of weak correlations between performance and environmental variability, as well as qualities of the surrogate. Our study demonstrates that prioritizing areas for a variety of geophysical characteristics will, in most cases, promote the representation of species. Our findings also point to areas for future research that might enhance CNS surrogacy.This article is part of the Theo Murphy meeting issue ‘Geodiversity science for society’.
DOI: 10.25582/data.2023-01.2725582/2299493
title: Data for EMSL Project 50969 from January 2023
DOI: 10.6084/m9.figshare.25018422.v1
title: Code and publicly available data sets from A test of Conserving Nature's Stage: protecting a diversity of geophysical traits can also support a diversity of species at a landscape scale

description: Conserving Nature's Stage (CNS) is a concept from conservation planning that promotes the protection of areas encompassing a broad range of enduring geophysical traits to provide long-term habitat for diverse species. The efficacy of using enduring geophysical characteristics as surrogates for biodiversity, independent of non-geophysical features and when considering finer resolution area selections, has yet to be investigated. Here, we evaluated CNS using 33 fine-scale inventories of vascular plant, non-vascular plant, invertebrate or vertebrate species from 13 areas across three continents. For each inventory, we estimated a continuous multidimensional surrogate defined from topographic and soil estimates of the surveyed plots. We assessed surrogate effectiveness of by comparing the species representation of surrogate selected plots to the representation from plots picked randomly and using species information. We then used correlation coefficients to assess the link between the performance and qualities of the inventories, surroundings and surrogates. The CNS surrogate showed positive performance for 24 of the 33 inventories, and among these tests, represented 28 more species than random and 83% of the total number of species on average. We also found a small number of weak correlations between performance and environmental variability, as well as qualities of the surrogate. Our study demonstrates that prioritizing areas for a variety of geophysical characteristics will, in most cases, promote the representation of species. Our findings also point to areas for future research that might enhance CNS surrogacy.This article is part of the Theo Murphy meeting issue ‘Geodiversity science for society’.
DOI: 10.25582/data.2023-01.2724424/2299494
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2726038/2299495
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2725629/2299496
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2724471/2299497
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2725722/2299498
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2725783/2299499
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2725972/2299500
title: Data for EMSL Project 50969 from January 2023
DOI: 10.25582/data.2023-01.2724463/2299501
title: Data for EMSL Project 50969 from January 2023
DOI: 10.5281/zenodo.7793636
title: BRUVS_Seabed_APEX

description: This dataset contains abundance and size of species recorded on stereo baited remote underwater video stations (BRUVS) deployed on seabed in New Caledonia, French Polynesia and Tonga between 2012 and 2014. Data collection was performed under several authorizations from the Government of New-Caledonia, the Southern Province of New Caledonia, the Northern Province of New Caledonia, the Government of French Polynesia, and the Ministry of Foreign Affairs of the Kingdom of Tonga. Collection was performed during the APEX and PRISTINE expeditions led by Laurent Vigliola.
DOI: 10.5281/zenodo.7793637
title: BRUVS_Seabed_APEX

description: This dataset contains abundance and size of species recorded on stereo baited remote underwater video stations (BRUVS) deployed on seabed in New Caledonia, French Polynesia and Tonga between 2012 and 2014. Data collection was performed under several authorizations from the Government of New-Caledonia, the Southern Province of New Caledonia, the Northern Province of New Caledonia, the Government of French Polynesia, and the Ministry of Foreign Affairs of the Kingdom of Tonga. Collection was performed during the APEX and PRISTINE expeditions led by Laurent Vigliola.
DOI: 10.25582/data.2021-12.2396237/2299724
title: Data for EMSL Project 60141 from December 2021
DOI: 10.25582/data.2023-07.2846427/2299725
title: Data for EMSL Project 60141 from July 2023
DOI: 10.25582/data.2022-02.2439981/2299726
title: Data for EMSL Project 60141 from February 2022
DOI: 10.25582/data.2023-01.2725093/2301859
title: Data for EMSL Project 51353 from January 2023
DOI: 10.5281/zenodo.8031513
title: Envy-Free Vaccination Strategy (Dataset)

description: Data generated by Envy-Free Vaccination Protocol model (DOI: 10.5281/zenodo.8026443).
DOI: 10.5281/zenodo.8031514
title: Envy-Free Vaccination Strategy (Dataset)

description: Data generated by Envy-Free Vaccination Protocol model (DOI: 10.5281/zenodo.8026443).
DOI: 10.25582/data.2023-01.2725033/2301860
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726321/2301861
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725127/2301862
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725987/2301863
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725859/2301864
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725052/2301865
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725102/2301866
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725875/2301867
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725308/2301868
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725297/2301869
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726164/2301870
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725291/2301871
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725280/2301872
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725996/2301873
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725304/2301874
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725310/2301875
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726276/2301876
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725300/2301877
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725934/2301878
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725125/2301879
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725091/2301880
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725954/2301881
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726277/2301882
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725282/2301883
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726289/2301884
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726023/2301885
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726228/2301886
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726306/2301887
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725336/2301888
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725101/2301889
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725277/2301890
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725932/2301891
title: Data for EMSL Project 51353 from January 2023
DOI: 10.6084/m9.figshare.25018482
title: Interaction effects from The latent net effectiveness of institutional complexes: a heuristic model

description: Coded data on 1620 interaction effects between multilateral environmental agreements
DOI: 10.6084/m9.figshare.25018482.v2
title: Interaction effects from The latent net effectiveness of institutional complexes: a heuristic model

description: Coded data on 1620 interaction effects between multilateral environmental agreements
DOI: 10.6084/m9.figshare.25018485
title: Institutional capacity from The latent net effectiveness of institutional complexes: a heuristic model

description: Coded data on 378 multilateral environmental agreements
DOI: 10.6084/m9.figshare.25018485.v2
title: Institutional capacity from The latent net effectiveness of institutional complexes: a heuristic model

description: Coded data on 378 multilateral environmental agreements
DOI: 10.25582/data.2023-01.2726017/2301892
title: Data for EMSL Project 51353 from January 2023
DOI: 10.6084/m9.figshare.25018482.v1
title: Interaction effects from The latent net effectiveness of institutional complexes: a heuristic model

description: Coded data on 1620 interaction effects between multilateral environmental agreements
DOI: 10.6084/m9.figshare.25018485.v1
title: Institutional capacity from The latent net effectiveness of institutional complexes: a heuristic model

description: Coded data on 378 multilateral environmental agreements
DOI: 10.25582/data.2023-01.2725095/2301893
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726205/2301894
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726256/2301895
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726153/2301896
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726136/2301897
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725035/2301898
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726188/2301899
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725933/2301900
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726245/2301901
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725139/2301902
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725271/2301903
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726235/2301904
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726011/2301905
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726236/2301906
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725827/2301907
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725135/2301908
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725272/2301909
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725955/2301910
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725860/2301911
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726260/2301912
title: Data for EMSL Project 51353 from January 2023
DOI: 10.34740/kaggle/dsv/7628193
title: Layline Shareholder activism dataset
DOI: 10.25582/data.2023-01.2725050/2301913
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725959/2301914
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726305/2301915
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726105/2301916
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726066/2301917
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725833/2301918
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726310/2301919
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725315/2301920
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726315/2301921
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725768/2301922
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725303/2301923
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726124/2301924
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725977/2301925
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725931/2301926
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725938/2301927
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725350/2301928
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725119/2301929
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725269/2301930
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725118/2301931
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725281/2301932
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725270/2301933
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726067/2301934
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725956/2301935
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726187/2301936
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726204/2301937
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725273/2301938
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726240/2301939
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725964/2301940
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725298/2301941
title: Data for EMSL Project 51353 from January 2023
DOI: 10.5281/zenodo.10662904
title: "DEVONU LUG'OTIT TURK" ASARI QADIMGI TURKIY MIFOLOGIYAGA OID MANBA SIFATIDA
DOI: 10.5281/zenodo.10662905
title: "DEVONU LUG'OTIT TURK" ASARI QADIMGI TURKIY MIFOLOGIYAGA OID MANBA SIFATIDA
DOI: 10.6084/m9.figshare.25223951
title: 数据库

description: 同位素数据
DOI: 10.6084/m9.figshare.25223951.v1
title: 数据库

description: 同位素数据
DOI: 10.25582/data.2023-01.2725960/2301942
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725989/2301943
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726098/2301944
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725283/2301945
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726180/2301946
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725258/2301947
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726288/2301948
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2726297/2301949
title: Data for EMSL Project 51353 from January 2023
DOI: 10.25582/data.2023-01.2725138/2301950
title: Data for EMSL Project 51353 from January 2023