cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25346/s6/4ospzr/5g531n
title: M44-F30-12-1.JPG
DOI: 10.25346/s6/4ospzr/zr4sss
title: M44-D33-9-20.JPG
DOI: 10.25346/s6/4ospzr/amtlpa
title: M44-A25-6-1.JPG
DOI: 10.25346/s6/4ospzr/i8fspj
title: M44-E33-11-16.JPG
DOI: 10.25346/s6/4ospzr/rfmvt6
title: M44-C30-10-2.JPG
DOI: 10.25346/s6/4ospzr/daxip2
title: M44-E35-7-9.JPG
DOI: 10.25346/s6/4ospzr/y0ljr4
title: M44-G30-6-1.JPG
DOI: 10.25346/s6/4ospzr/algekl
title: M44-D30-10-6.JPG
DOI: 10.25346/s6/4ospzr/45vq2t
title: M44-D32-11-19.JPG
DOI: 10.25346/s6/4ospzr/lftntq
title: M44-C30-12-1.JPG
DOI: 10.25346/s6/4ospzr/jbmttx
title: M44-F30-9-6.JPG
DOI: 10.25346/s6/4ospzr/7yc1uu
title: M44-C30-10-16'.JPG
DOI: 10.25346/s6/4ospzr/i9e3fv
title: M44-E34-7-16.JPG
DOI: 10.25346/s6/4ospzr/9cjsse
title: M44-C30-9-4.JPG
DOI: 10.25346/s6/4ospzr/bf9aep
title: M44-D35-4-9.JPG
DOI: 10.25346/s6/4ospzr/iku9nu
title: M44-G35-6-3.JPG
DOI: 10.25346/s6/4ospzr/mctt7r
title: M44-D30-4-2.JPG
DOI: 10.25346/s6/4ospzr/lgastd
title: M44-D30-10-2.JPG
DOI: 10.25346/s6/4ospzr/runwd6
title: M44-E33-9-20.JPG
DOI: 10.25346/s6/4ospzr/tihbxx
title: M44-D32-8-4.JPG
DOI: 10.25346/s6/4ospzr/n7atrn
title: M44-C31-5-5.JPG
DOI: 10.25346/s6/4ospzr/9enzvy
title: M44-D32-11-22.JPG
DOI: 10.25346/s6/4ospzr/dsgt8j
title: M44-F10-3-1.JPG
DOI: 10.25346/s6/4ospzr/moqs0x
title: M44-E34-6-24.JPG
DOI: 10.25346/s6/4ospzr/xzsfqx
title: M44-D33-11-8.JPG
DOI: 10.25346/s6/4ospzr/0fzriw
title: M44-E31-8-6.JPG
DOI: 10.25346/s6/4ospzr/bjvnny
title: M44-G30-8-1.JPG
DOI: 10.25346/s6/4ospzr/fl0leb
title: M44-D33-9-11.JPG
DOI: 10.25346/s6/4ospzr/offtrl
title: M44-F35-8-8.JPG
DOI: 10.25346/s6/4ospzr/i1udfi
title: M44-E31-5-4.JPG
DOI: 10.25346/s6/4ospzr/unnlgu
title: M44-C31-7-3.JPG
DOI: 10.25346/s6/4ospzr/ey2dch
title: M44-E34-7-15.JPG
DOI: 10.25346/s6/4ospzr/2s51ta
title: M44-E30-11-2.JPG
DOI: 10.25346/s6/4ospzr/uaeion
title: M44-F30-10-6.JPG
DOI: 10.25346/s6/4ospzr/xguecg
title: M44-E33-8-5.JPG
DOI: 10.25346/s6/4ospzr/3dujm8
title: M44-C31-3-2.JPG
DOI: 10.25346/s6/4ospzr/lqhkpa
title: M44-E32-11-4.JPG
DOI: 10.25346/s6/4ospzr/dxe8tq
title: M44-E33-8-4.JPG
DOI: 10.25346/s6/4ospzr/gpvzer
title: M44-D34-9-12.JPG
DOI: 10.25346/s6/4ospzr/hdgpzj
title: M44-E34-5-5.JPG
DOI: 10.25346/s6/4ospzr/cnq9qh
title: M44-G30-4-5.JPG
DOI: 10.25346/s6/4ospzr/9hgeqa
title: M44-D30-10-14.JPG
DOI: 10.25346/s6/4ospzr/q6psjv
title: M44-D32-11-16.JPG
DOI: 10.25346/s6/4ospzr/r0yw8j
title: M44-E34-4-2.JPG
DOI: 10.25346/s6/4ospzr/ugt2uk
title: M44-C30-4-3.JPG
DOI: 10.25346/s6/4ospzr/utavg9
title: M44-D32-10-8.JPG
DOI: 10.25346/s6/4ospzr/jpaubt
title: M44-C30-9-3.JPG
DOI: 10.25346/s6/4ospzr/pr781t
title: M44-E30-10-1.JPG
DOI: 10.25346/s6/4ospzr/wcmavh
title: M44-E33-11-13.JPG
DOI: 10.25346/s6/4ospzr/kp9gra
title: M44-E32-5-2.JPG
DOI: 10.25346/s6/4ospzr/igonyd
title: M44-D32-11-11.JPG
DOI: 10.25346/s6/4ospzr/zfuopo
title: M44-C30-10-1.JPG
DOI: 10.25346/s6/4ospzr/kno2gp
title: M44-E34-6-25.JPG
DOI: 10.25346/s6/4ospzr/atyia2
title: M44-F30-9-2.JPG
DOI: 10.25346/s6/4ospzr/kcamue
title: M44-E36-9-20.JPG
DOI: 10.25346/s6/4ospzr/kfwulv
title: M44-D34-10-8.JPG
DOI: 10.25346/s6/4ospzr/gfb1nv
title: M44-E32-11-3.JPG
DOI: 10.25346/s6/4ospzr
title: Fishhook image catalog

description: Photographs of fishhooks from Tangatatau Rockshelter
DOI: 10.3886/e155721
title: Does the GRE predict valued outcomes

description: Graduate school programs that are considering dropping the GRE as an admissions tool often focus on claims that the test is biased and does not predict valued outcomes. This paper addresses the bias issue and provides evidence related to the prediction of valued outcomes. Two studies are included. The first study used data from chemistry (N=320) and computer engineering (N=389) programs from a flagship state university and an Ivy League university to demonstrate the ability of the GRE to predict dropout. Dropout prediction for the chemistry programs was both statistically and practically significant for the GRE quantitative (GRE-Q) scores, but not for the verbal or analytical writing scores. In the computer engineering programs, significant dropout prediction by GRE-Q was evident only for domestic students. The second study showed the relationship of GRE Analytical Writing scores to writing produced as part of graduate school coursework by 217 students.
description: Students enrolled in graduate programs in the USA.
DOI: 10.3886/e155721v1
title: Does the GRE predict valued outcomes

description: Graduate school programs that are considering dropping the GRE as an admissions tool often focus on claims that the test is biased and does not predict valued outcomes. This paper addresses the bias issue and provides evidence related to the prediction of valued outcomes. Two studies are included. The first study used data from chemistry (N=320) and computer engineering (N=389) programs from a flagship state university and an Ivy League university to demonstrate the ability of the GRE to predict dropout. Dropout prediction for the chemistry programs was both statistically and practically significant for the GRE quantitative (GRE-Q) scores, but not for the verbal or analytical writing scores. In the computer engineering programs, significant dropout prediction by GRE-Q was evident only for domestic students. The second study showed the relationship of GRE Analytical Writing scores to writing produced as part of graduate school coursework by 217 students.
description: Students enrolled in graduate programs in the USA.
DOI: 10.6084/m9.figshare.16595973
title: Reinforced Risk Prediction With Budget Constraint Using Irregularly Measured Data From Electronic Health Records

description: Uncontrolled glycated hemoglobin (HbA1c) levels are associated with adverse events among complex diabetic patients. These adverse events present serious health risks to affected patients and are associated with significant financial costs. Thus, a high-quality predictive model that could identify high-risk patients so as to inform preventative treatment has the potential to improve patient outcomes while reducing healthcare costs. Because the biomarker information needed to predict risk is costly and burdensome, it is desirable that such a model collect only as much information as is needed on each patient so as to render an accurate prediction. We propose a sequential predictive model that uses accumulating patient longitudinal data to classify patients as: high-risk, low-risk, or uncertain. Patients classified as high-risk are then recommended to receive preventative treatment and those classified as low-risk are recommended to standard care. Patients classified as uncertain are monitored until a high-risk or low-risk determination is made. We construct the model using claims and enrollment files from Medicare, linked with patient electronic health records (EHR) data. The proposed model uses functional principal components to accommodate noisy longitudinal data and weighting to deal with missingness and sampling bias. The proposed method demonstrates higher predictive accuracy and lower cost than competing methods in a series of simulation experiments and application to data on complex patients with diabetes. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.16595973.v2
title: Reinforced Risk Prediction With Budget Constraint Using Irregularly Measured Data From Electronic Health Records

description: Uncontrolled glycated hemoglobin (HbA1c) levels are associated with adverse events among complex diabetic patients. These adverse events present serious health risks to affected patients and are associated with significant financial costs. Thus, a high-quality predictive model that could identify high-risk patients so as to inform preventative treatment has the potential to improve patient outcomes while reducing healthcare costs. Because the biomarker information needed to predict risk is costly and burdensome, it is desirable that such a model collect only as much information as is needed on each patient so as to render an accurate prediction. We propose a sequential predictive model that uses accumulating patient longitudinal data to classify patients as: high-risk, low-risk, or uncertain. Patients classified as high-risk are then recommended to receive preventative treatment and those classified as low-risk are recommended to standard care. Patients classified as uncertain are monitored until a high-risk or low-risk determination is made. We construct the model using claims and enrollment files from Medicare, linked with patient electronic health records (EHR) data. The proposed model uses functional principal components to accommodate noisy longitudinal data and weighting to deal with missingness and sampling bias. The proposed method demonstrates higher predictive accuracy and lower cost than competing methods in a series of simulation experiments and application to data on complex patients with diabetes. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.16595973.v1
title: Reinforced risk prediction with budget constraint using irregularly measured data from electronic health records

description: Uncontrolled glycated hemoglobin (HbA1c) levels are associated with adverse events among complex diabetic patients. These adverse events present serious health risks to affected patients and are associated with significant financial costs. Thus, a high-quality predictive model that could identify high-risk patients so as to inform preventative treatment has the potential to improve patient outcomes while reducing healthcare costs. Because the biomarker information needed to predict risk is costly and burdensome, it is desirable that such a model collect only as much information as is needed on each patient so as to render an accurate prediction. We propose a sequential predictive model that uses accumulating patient longitudinal data to classify patients as: high-risk, low-risk, or uncertain. Patients classified as high-risk are then recommended to receive preventative treatment and those classified as low-risk are recommended to standard care. Patients classified as uncertain are monitored until a high-risk or low-risk determination is made. We construct the model using claims and enrollment files from Medicare, linked with patient Electronic Health Records (EHR) data. The proposed model uses functional principal components to accommodate noisy longitudinal data and weighting to deal with missingness and sampling bias. The proposed method demonstrates higher predictive accuracy and lower cost than competing methods in a series of simulation experiments and application to data on complex patients with diabetes.
DOI: 10.6084/m9.figshare.16653126
title: Hamiltonian-Assisted Metropolis Sampling

description: Various Markov chain Monte Carlo (MCMC) methods are studied to improve upon random walk Metropolis sampling, for simulation from complex distributions. Examples include Metropolis-adjusted Langevin algorithms, Hamiltonian Monte Carlo, and other algorithms related to underdamped Langevin dynamics. We propose a broad class of irreversible sampling algorithms, called Hamiltonian-assisted Metropolis sampling (HAMS), and develop two specific algorithms with appropriate tuning and preconditioning strategies. Our HAMS algorithms are designed to simultaneously achieve two distinctive properties, while using an augmented target density with a momentum as an auxiliary variable. One is generalized detailed balance, which induces an irreversible exploration of the target. The other is a rejection-free property for a Gaussian target with a prespecified variance matrix. This property allows our preconditioned algorithms to perform satisfactorily with relatively large step sizes. Furthermore, we formulate a framework of generalized Metropolis–Hastings sampling, which not only highlights our construction of HAMS at a more abstract level, but also facilitates possible further development of irreversible MCMC algorithms. We present several numerical experiments, where the proposed algorithms consistently yield superior results among existing algorithms using the same preconditioning schemes.
DOI: 10.6084/m9.figshare.16653126.v2
title: Hamiltonian-Assisted Metropolis Sampling

description: Various Markov chain Monte Carlo (MCMC) methods are studied to improve upon random walk Metropolis sampling, for simulation from complex distributions. Examples include Metropolis-adjusted Langevin algorithms, Hamiltonian Monte Carlo, and other algorithms related to underdamped Langevin dynamics. We propose a broad class of irreversible sampling algorithms, called Hamiltonian-assisted Metropolis sampling (HAMS), and develop two specific algorithms with appropriate tuning and preconditioning strategies. Our HAMS algorithms are designed to simultaneously achieve two distinctive properties, while using an augmented target density with a momentum as an auxiliary variable. One is generalized detailed balance, which induces an irreversible exploration of the target. The other is a rejection-free property for a Gaussian target with a prespecified variance matrix. This property allows our preconditioned algorithms to perform satisfactorily with relatively large step sizes. Furthermore, we formulate a framework of generalized Metropolis–Hastings sampling, which not only highlights our construction of HAMS at a more abstract level, but also facilitates possible further development of irreversible MCMC algorithms. We present several numerical experiments, where the proposed algorithms consistently yield superior results among existing algorithms using the same preconditioning schemes.
DOI: 10.6084/m9.figshare.16653126.v1
title: Hamiltonian Assisted Metropolis Sampling

description: Various Markov chain Monte Carlo (MCMC) methods are studied to improve upon random walk Metropolis sampling, for simulation from complex distributions. Examples include Metropolis-adjusted Langevin algorithms, Hamiltonian Monte Carlo, and other algorithms related to underdamped Langevin dynamics. We propose a broad class of irreversible sampling algorithms, called Hamiltonian assisted Metropolis sampling (HAMS), and develop two specific algorithms with appropriate tuning and preconditioning strategies. Our HAMS algorithms are designed to simultaneously achieve two distinctive properties, while using an augmented target density with a momentum as an auxiliary variable. One is generalized detailed balance, which induces an irreversible exploration of the target. The other is a rejection-free property for a Gaussian target with a pre-specified variance matrix. This property allows our preconditioned algorithms to perform satisfactorily with relatively large step sizes. Furthermore, we formulate a framework of generalized Metropolis–Hastings sampling, which not only highlights our construction of HAMS at a more abstract level, but also facilitates possible further development of irreversible MCMC algorithms. We present several numerical experiments, where the proposed algorithms consistently yield superior results among existing algorithms using the same preconditioning schemes.
DOI: 10.34740/kaggle/dsv/2741357
title: Universal Design Space Building Energy Simulation
DOI: 10.18739/a2610vt0g
title: Thermokarst acceleration in Arctic tundra driven by climate change and fire disturbance, 1950-2015

description: Climate warming is projected to intensify tundra wildfire, with profound implications for permafrost thaw. A major uncertainty is how increased burning will interact with climate change to exacerbate thermokarst (ground-surface collapse resulting from permafrost thaw). Here we used ~70 years of remote sensing observation combined with spatially-explicit modeling to show that thermokarst rates increased by ~60% with warming climate and wildfire from 1950 to 2015 in Arctic Alaska. Wildfire amplified thermokarst over 40+ years, cumulatively creating ~9 times thermokarst formation as that in unburned tundra. However, thermokarst triggered by repeat burns did not differ from that triggered by single burns, irrespective of time between fires. Our simulation identified climate change as a principal driver for all thermokarst formed during 1950-2015 (4,700 square kilometers (km2)) in Arctic Alaska, but wildfire was disproportionately responsible for 10.5% of the thermokarst by burning merely 3.4% of the landscape. These results combined suggest that climate change and wildfire will synergistically accelerate thermokarst as the Arctic transitions in this century.
DOI: 10.6084/m9.figshare.17102636
title: Spatial Heterogeneity of Extensively Drug Resistant-Tuberculosis in Western Cape Province, South Africa

description: Tuberculosis (TB) remains a leading global infectious disease killer. Treatment outcomes are especially poor among people with extensively drug-resistant (XDR) TB, until recently defined as rifampicin-resistant (RR) TB with resistance to an aminoglycoside and a fluoroquinolone, with efforts urgently needed to improve response. We used laboratory TB test results from Western Cape province, South Africa between 2012 and 2015 to identify XDR-TB and pre-XDR-TB (RR-TB with resistance to one second-line drug) spatial hotspots. We mapped the percentage and count of individuals with RR-TB that had XDR-TB and pre-XDR-TB across the province and in Cape Town, as well as amikacin-resistant and ofloxacin-resistant TB. We found the percentage of pre-XDR-TB and the count of XDR-TB/pre-XDR-TB highly heterogeneous with clear geographic hotspots within RR-TB high burden areas. We also found hotspots in both percentage and count of amikacin-resistant and ofloxacin-resistant TB. The spatial distribution of percentage ofloxacin-resistant TB hotspots is similar to XDR-TB hotspots, suggesting that fluoroquinolone-resistace is often the first step to additional resistance. Our work shows that interventions used to reduce XDR-TB incidence and improve outcomes may need to be targeted within spatial locations with RR-TB. Further research is required to understand underlying drivers of XDR-TB transmission in these locations.
DOI: 10.6084/m9.figshare.17102636.v1
title: Spatial Heterogeneity of Extensively Drug Resistant-Tuberculosis in Western Cape Province, South Africa

description: Tuberculosis (TB) remains a leading global infectious disease killer. Treatment outcomes are especially poor among people with extensively drug-resistant (XDR) TB, until recently defined as rifampicin-resistant (RR) TB with resistance to an aminoglycoside and a fluoroquinolone, with efforts urgently needed to improve response. We used laboratory TB test results from Western Cape province, South Africa between 2012 and 2015 to identify XDR-TB and pre-XDR-TB (RR-TB with resistance to one second-line drug) spatial hotspots. We mapped the percentage and count of individuals with RR-TB that had XDR-TB and pre-XDR-TB across the province and in Cape Town, as well as amikacin-resistant and ofloxacin-resistant TB. We found the percentage of pre-XDR-TB and the count of XDR-TB/pre-XDR-TB highly heterogeneous with clear geographic hotspots within RR-TB high burden areas. We also found hotspots in both percentage and count of amikacin-resistant and ofloxacin-resistant TB. The spatial distribution of percentage ofloxacin-resistant TB hotspots is similar to XDR-TB hotspots, suggesting that fluoroquinolone-resistace is often the first step to additional resistance. Our work shows that interventions used to reduce XDR-TB incidence and improve outcomes may need to be targeted within spatial locations with RR-TB. Further research is required to understand underlying drivers of XDR-TB transmission in these locations.
DOI: 10.6084/m9.figshare.3761562.v2059
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.25345/c5ts18
title: MassIVE MSV000088476 - Swab_MK_revision_data_redo_11_22_2022_backup_data
title: back up the data, LCMS run, redo the revision data, run on 11-22-2021, LC run by zongyuan liu, sample by mitchelle
DOI: 10.48428/adattar/o9amu9
title: A Magyar Természettudományi Múzeum modern informatikai fejlesztési lehetőségei kutatás és biodiverzitás megőrzés támogatási szempontból

description: A Magyar Természettudományi Múzeum modern informatikai fejlesztési lehetőségei kutatás és biodiverzitás megőrzés támogatási szempontból
DOI: 10.17632/jvt7j7y39j.1
title: Data and Code for "Quality Differentiation and Spatial Clustering among Restaurants"

description: In the accompanying folders, we provide data and computer code that can be used to replicate the results in our article. The "Data" folder contains various data files that serve as inputs for the analyses done in Matlab, R, and Stata. The sources of these data are explained in the article. In the "Computer Code" folder, there are three groups of programmes: i) Matlab code to execute all theoretical computations and produce various plots in the article, ii) R code to execute all statistical tests appertaining to the spatial analysis, and iii) Stata code to replicate all regression results. The "City Maps.pdf" file supplements Figure 1 in our article by showing the location of top-rated and bottom-rated restaurants across all the 96 cities in our sample.
DOI: 10.48428/adattar/idjg5x
title: Using Dataverses through OpenBioMaps

description: Documentation and examples of usage Dataverses through OpenBioMaps
DOI: 10.5517/ccdc.csd.cc23zvdc
title: CCDC 1966063: Experimental Crystal Structure Determination

description: Related Article: Nhat Tam Vo, Christian Herrero, Régis Guillot, Tanya Inceoglu, Winfried Leibl, Martin Clémancey, Patrick Dubourdeaux, Geneviève Blondin, Ally Aukauloo, Marie Sircoglou|2021|Chem.Commun.|57|12836|doi:10.1039/D1CC04521K
DOI: 10.5517/ccdc.csd.cc2734l3
title: CCDC 2058604: Experimental Crystal Structure Determination

description: Related Article: Muhittin Aygun|2021|CSD Communication|||
description: Related Article: Nurzhan Beksultanova,Zeynep Gözükara,Mihrimah Araz,Merve Bulut,Sıdıka Polat-Çakır,Muhittin Aygün,Özdemir Dogan|2021|Chirality|33|465|doi:10.1002/chir.23320
DOI: 10.5517/ccdc.csd.cc27b00m
title: CCDC 2065189: Experimental Crystal Structure Determination

description: Related Article: Fabian Thomas, Dominik Steden, Alexander Eith, Alexander Hoffmann, Sonja Herres-Pawlis|2021|Z.Naturforsch.,B:Chem.Sci.|76|835|doi:10.1515/znb-2021-0140
DOI: 10.5517/ccdc.csd.cc27b01n
title: CCDC 2065190: Experimental Crystal Structure Determination

description: Related Article: Fabian Thomas, Dominik Steden, Alexander Eith, Alexander Hoffmann, Sonja Herres-Pawlis|2021|Z.Naturforsch.,B:Chem.Sci.|76|835|doi:10.1515/znb-2021-0140
DOI: 10.5517/ccdc.csd.cc27b02p
title: CCDC 2065191: Experimental Crystal Structure Determination

description: Related Article: Fabian Thomas, Dominik Steden, Alexander Eith, Alexander Hoffmann, Sonja Herres-Pawlis|2021|Z.Naturforsch.,B:Chem.Sci.|76|835|doi:10.1515/znb-2021-0140
DOI: 10.5517/ccdc.csd.cc29391s
title: CCDC 2118324: Experimental Crystal Structure Determination

description: Related Article: I.S. Konovalova, S.V. Shishkina, D. Kobzev, O. Semenova, A. Tatarets|2021|Acta Crystallogr.,Sect.E:Cryst.Commun.|77|1203|doi:10.1107/S2056989021011385
DOI: 10.5517/ccdc.csd.cc29392t
title: CCDC 2118325: Experimental Crystal Structure Determination

description: Related Article: I.S. Konovalova, S.V. Shishkina, D. Kobzev, O. Semenova, A. Tatarets|2021|Acta Crystallogr.,Sect.E:Cryst.Commun.|77|1203|doi:10.1107/S2056989021011385
DOI: 10.5517/ccdc.csd.cc28ml9t
title: CCDC 2104196: Experimental Crystal Structure Determination

description: Related Article: Minling Zhong, Jie Zhang, Zhenpin Lu, Zuowei Xie|2021|Dalton Trans.|50|17150|doi:10.1039/D1DT03665C
DOI: 10.5517/ccdc.csd.cc28mlcw
title: CCDC 2104198: Experimental Crystal Structure Determination

description: Related Article: Minling Zhong, Jie Zhang, Zhenpin Lu, Zuowei Xie|2021|Dalton Trans.|50|17150|doi:10.1039/D1DT03665C
DOI: 10.5517/ccdc.csd.cc28mlfy
title: CCDC 2104200: Experimental Crystal Structure Determination

description: Related Article: Minling Zhong, Jie Zhang, Zhenpin Lu, Zuowei Xie|2021|Dalton Trans.|50|17150|doi:10.1039/D1DT03665C