cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.ztuvtx
title: Occurrence Download

description: A dataset containing 321 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Vitis bourgaeana Planch., Vitis vulpina var. yzabalana S.Watson)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 321 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034193-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12798094
title: VA3TWN CHU 7.850 MHz from April 8, 2024 Solar Eclipse

description: Radio: RTL-SDRv4

GPS-disciplined: No

AGC: Off

Antenna: HF20A, North-South

Lat/lon:




43.6847319
79.3083075




Notes: Railway near the receiving station, but the trains run on diesel.

Thank you for this activity. It's meaningful, even if I am not 100% sure I've done it right. It would be even more helpful if I could use rtl_tcp or other ways to record the reception. I was using gqrx to feed the voice to fldigi.My original plan was IC-705 with fldigi, but I didn't get the right 12V DC. Pity.
DOI: 10.5281/zenodo.12798095
title: VA3TWN CHU 7.850 MHz from April 8, 2024 Solar Eclipse

description: Radio: RTL-SDRv4

GPS-disciplined: No

AGC: Off

Antenna: HF20A, North-South

Lat/lon:




43.6847319
79.3083075




Notes: Railway near the receiving station, but the trains run on diesel.

Thank you for this activity. It's meaningful, even if I am not 100% sure I've done it right. It would be even more helpful if I could use rtl_tcp or other ways to record the reception. I was using gqrx to feed the voice to fldigi.My original plan was IC-705 with fldigi, but I didn't get the right 12V DC. Pity.
DOI: 10.15468/dl.jutxrs
title: Occurrence Download

description: A dataset containing 1369 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Acacia estrophiolata F.Muell."
 ]
}

 The dataset includes 1369 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034194-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7kwsxf
title: Occurrence Download

description: A dataset containing 1827 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Vitis californica Benth.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1827 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034195-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12796476
title: Stream Thermal Sensitivities in Southern Alaska

description: This project characterized stream temperature regimes in the Bristol Bay, Kodiak Island, Cook Inlet, Copper River, and Prince William Sound regions to better understand thermal habitats for spawning and juvenile salmon. We aggregated 1,691 summertime stream temperature time series from 420 monitoring locations across southwestern and southcentral Alaska and calculated a suite of metrics related to the magnitude, frequency, duration, timing, and variability of stream temperatures. We categorized streams into one of six different thermal regimes using metrics including temperature range, stability, and timing. Thermal regimes represent a gradient of cold to warm habitats with Groups 1 and 6 experiencing the coldest temperatures, and 2, 3, 4, and 5 ranging from colder to warmest. Group 1 had the latest timing of maximum stream temperatures and Group 6 had the most stable stream temperatures. A comparison of stream thermal regimes among regions showed that cold habitats with later timing of maximum temperatures, compared to other thermal regime types, were most common in all regions and that cold stable habitats were most common in the Copper River and Prince William Sound regions. However, all regions included all six thermal regimes described in our classification.

Thermal sensitivity measures how responsive water temperature is to air temperature, which can help reveal the processes that govern stream temperature and how readily water temperature will be affected by air temperature changes. We calculated stream thermal sensitivity (𝛕) across monitoring sites. Estimated 𝛕 was highest in the Cook Inlet and Kodiak regions, followed by Bristol Bay and Copper River, and was lowest in Prince William Sound. We modeled variation in 𝛕 using geomorphic, hydrologic, climatic, and landcover covariates. The model was used to map thermal sensitivities across 1,597 salmon streams for high and low scenarios of spring snowpack and summer precipitation. Thermal sensitivities decreased under higher summertime precipitation but changed minimally between years with low and high snowpack. The strongest control on 𝛕 was watershed slope, with a lower 𝛕 in streams draining steeper watersheds. This result may be due to snowmelt contributions later in the summer period, shorter water residence times, and deeper flowpaths that experience less solar radiation. Chum and pink salmon habitats had the lowest 𝛕, followed by spawning habitats for all species, while rearing habitats and Chinook, coho, and sockeye salmon habitats all had higher 𝛕. Thus, in a warming future, salmon may face tradeoffs between physical habitat preferences for low gradient systems with adaptations for cold water.

The final report and web mapper are on the Data Catalog for the Alaska Center for Conservation Science, University of Alaska Anchorage: https://accscatalog.uaa.alaska.edu/dataset/stream-thermal-sensitivities
DOI: 10.5281/zenodo.6564825
title: Stream Thermal Sensitivities in Southern Alaska

description: This project characterized stream temperature regimes in the Bristol Bay, Kodiak Island, Cook Inlet, Copper River, and Prince William Sound regions to better understand thermal habitats for spawning and juvenile salmon. We aggregated 1,691 summertime stream temperature time series from 420 monitoring locations across southwestern and southcentral Alaska and calculated a suite of metrics related to the magnitude, frequency, duration, timing, and variability of stream temperatures. We categorized streams into one of six different thermal regimes using metrics including temperature range, stability, and timing. Thermal regimes represent a gradient of cold to warm habitats with Groups 1 and 6 experiencing the coldest temperatures, and 2, 3, 4, and 5 ranging from colder to warmest. Group 1 had the latest timing of maximum stream temperatures and Group 6 had the most stable stream temperatures. A comparison of stream thermal regimes among regions showed that cold habitats with later timing of maximum temperatures, compared to other thermal regime types, were most common in all regions and that cold stable habitats were most common in the Copper River and Prince William Sound regions. However, all regions included all six thermal regimes described in our classification.

Thermal sensitivity measures how responsive water temperature is to air temperature, which can help reveal the processes that govern stream temperature and how readily water temperature will be affected by air temperature changes. We calculated stream thermal sensitivity (𝛕) across monitoring sites. Estimated 𝛕 was highest in the Cook Inlet and Kodiak regions, followed by Bristol Bay and Copper River, and was lowest in Prince William Sound. We modeled variation in 𝛕 using geomorphic, hydrologic, climatic, and landcover covariates. The model was used to map thermal sensitivities across 1,597 salmon streams for high and low scenarios of spring snowpack and summer precipitation. Thermal sensitivities decreased under higher summertime precipitation but changed minimally between years with low and high snowpack. The strongest control on 𝛕 was watershed slope, with a lower 𝛕 in streams draining steeper watersheds. This result may be due to snowmelt contributions later in the summer period, shorter water residence times, and deeper flowpaths that experience less solar radiation. Chum and pink salmon habitats had the lowest 𝛕, followed by spawning habitats for all species, while rearing habitats and Chinook, coho, and sockeye salmon habitats all had higher 𝛕. Thus, in a warming future, salmon may face tradeoffs between physical habitat preferences for low gradient systems with adaptations for cold water.

The final report and web mapper are on the Data Catalog for the Alaska Center for Conservation Science, University of Alaska Anchorage: https://accscatalog.uaa.alaska.edu/dataset/stream-thermal-sensitivities
DOI: 10.5281/zenodo.6564826
title: Stream Thermal Sensitivities in Southern Alaska

description: This project characterized stream temperature regimes in the Cook Inlet, Copper River, and Prince William Sound regions. We aggregated 1,548 summertime stream temperature time series from 355 monitoring locations across southwestern and southcentral Alaska and calculated a suite of metrics related to the magnitude, frequency, duration, timing, and variability of stream temperatures. We categorized streams into one of six different thermal regimes. A comparison of stream thermal regimes among regions showed that cold habitats with later timing of maximum temperatures were most common in all regions, cold stable habitats were more common in the Copper River and Prince William Sound regions, and all regions included all six thermal regimes described in our classification. We also calculated stream thermal sensitivity (𝛕) across monitoring sites to describe how closely stream temperatures track air temperatures. Estimated 𝛕 was highest in the Cook Inlet and Kodiak regions, followed by Bristol Bay and Copper River, and was lowest in Prince William Sound. We modeled variation in 𝛕 using geomorphic, hydrologic, climatic, and landcover covariates. The model was used to map thermal sensitivities across 966 salmon streams for high and low scenarios of spring snowpack and summer precipitation. Thermal sensitivities decreased under higher summertime precipitation and changed minimally between years with low and high snowpack. The strongest control on 𝛕 was watershed slope. Streams draining steeper watersheds had lower 𝛕, which may be due to snowmelt contributions later in the summer period, shorter water residence times, and deeper flowpaths that experience less solar radiation. Chum and pink salmon habitats had the lowest 𝛕, followed by spawning habitats. Rearing habitats and Chinook, coho, and sockeye salmon habitats all had higher 𝛕. In a warming future, salmon may need to balance physical habitat preferences for low gradient systems with adaptations for cold water.
DOI: 10.6084/m9.figshare.24271730
title: A Note on Monte Carlo Integration in High Dimensions

description: Monte Carlo integration is a commonly used technique to compute intractable integrals and is typically thought to perform poorly for very high-dimensional integrals. To show that this is not always the case, we examine Monte Carlo integration using techniques from the high-dimensional statistics literature by allowing the dimension of the integral to increase. In doing so, we derive nonasymptotic bounds for the relative and absolute error of the approximation for some general classes of functions through concentration inequalities. We provide concrete examples in which the magnitude of the number of points sampled needed to guarantee a consistent estimate varies between polynomial to exponential, and show that in theory arbitrarily fast or slow rates are possible. This demonstrates that the behavior of Monte Carlo integration in high dimensions is not uniform. Through our methods we also obtain nonasymptotic confidence intervals which are valid regardless of the number of points sampled.
DOI: 10.6084/m9.figshare.24271727
title: One-Step Weighting to Generalize and Transport Treatment Effect Estimates to a Target Population

description: The problems of generalization and transportation of treatment effect estimates from a study sample to a target population are central to empirical research and statistical methodology. In both randomized experiments and observational studies, weighting methods are often used with this objective. Traditional methods construct the weights by separately modeling the treatment assignment and study selection probabilities and then multiplying functions (e.g., inverses) of their estimates. In this work, we provide a justification and an implementation for weighting in a single step. We show a formal connection between this one-step method and inverse probability and inverse odds weighting. We demonstrate that the resulting estimator for the target average treatment effect is consistent, asymptotically Normal, multiply robust, and semiparametrically efficient. We evaluate the performance of the one-step estimator in a simulation study. We illustrate its use in a case study on the effects of physician racial diversity on preventive healthcare utilization among Black men in California. We provide R code implementing the methodology.
DOI: 10.6084/m9.figshare.24271727.v2
title: One-Step Weighting to Generalize and Transport Treatment Effect Estimates to a Target Population

description: The problems of generalization and transportation of treatment effect estimates from a study sample to a target population are central to empirical research and statistical methodology. In both randomized experiments and observational studies, weighting methods are often used with this objective. Traditional methods construct the weights by separately modeling the treatment assignment and study selection probabilities and then multiplying functions (e.g., inverses) of their estimates. In this work, we provide a justification and an implementation for weighting in a single step. We show a formal connection between this one-step method and inverse probability and inverse odds weighting. We demonstrate that the resulting estimator for the target average treatment effect is consistent, asymptotically Normal, multiply robust, and semiparametrically efficient. We evaluate the performance of the one-step estimator in a simulation study. We illustrate its use in a case study on the effects of physician racial diversity on preventive healthcare utilization among Black men in California. We provide R code implementing the methodology.
DOI: 10.6084/m9.figshare.24271730.v1
title: A Note on Monte Carlo Integration in High Dimensions

description: Monte Carlo integration is a commonly used technique to compute intractable integrals and is typically thought to perform poorly for very high-dimensional integrals. To show that this is not always the case, we examine Monte Carlo integration using techniques from the high-dimensional statistics literature by allowing the dimension of the integral to increase. In doing so, we derive nonasymptotic bounds for the relative and absolute error of the approximation for some general classes of functions through concentration inequalities. We provide concrete examples in which the magnitude of the number of points sampled needed to guarantee a consistent estimate varies between polynomial to exponential, and show that in theory arbitrarily fast or slow rates are possible. This demonstrates that the behavior of Monte Carlo integration in high dimensions is not uniform. Through our methods we also obtain nonasymptotic confidence intervals which are valid regardless of the number of points sampled.
DOI: 10.6084/m9.figshare.24975132
title: Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot

description: Observational studies of treatment effects require adjustment for confounding variables. However, causal inference methods typically cannot deliver perfect adjustment on all measured baseline variables, and there is often ambiguity about which variables should be prioritized. Standard prioritization methods based on treatment imbalance alone neglect variables’ relationships with the outcome. We propose the joint variable importance plot to guide variable prioritization for observational studies. Since not all variables are equally relevant to the outcome, the plot adds outcome associations to quantify the potential confounding jointly with the standardized mean difference. To enhance comparisons on the plot between variables with different confounding relationships, we also derive and plot bias curves. Variable prioritization using the plot can produce recommended values for tuning parameters in many existing matching and weighting methods. We showcase the use of the joint variable importance plots in the design of a balance-constrained matched study to evaluate whether taking an antidiabetic medication, glyburide, increases the incidence of C-section delivery among pregnant individuals with gestational diabetes.
DOI: 10.6084/m9.figshare.24975132.v2
title: Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot

description: Observational studies of treatment effects require adjustment for confounding variables. However, causal inference methods typically cannot deliver perfect adjustment on all measured baseline variables, and there is often ambiguity about which variables should be prioritized. Standard prioritization methods based on treatment imbalance alone neglect variables’ relationships with the outcome. We propose the joint variable importance plot to guide variable prioritization for observational studies. Since not all variables are equally relevant to the outcome, the plot adds outcome associations to quantify the potential confounding jointly with the standardized mean difference. To enhance comparisons on the plot between variables with different confounding relationships, we also derive and plot bias curves. Variable prioritization using the plot can produce recommended values for tuning parameters in many existing matching and weighting methods. We showcase the use of the joint variable importance plots in the design of a balance-constrained matched study to evaluate whether taking an antidiabetic medication, glyburide, increases the incidence of C-section delivery among pregnant individuals with gestational diabetes.
DOI: 10.6084/m9.figshare.23609578
title: Introducing Variational Inference in Statistics and Data Science Curriculum

description: Probabilistic models such as logistic regression, Bayesian classification, neural networks, and models for natural language processing, are increasingly more present in both undergraduate and graduate statistics and data science curricula due to their wide range of applications. In this article, we present a one-week course module for students in advanced undergraduate and applied graduate courses on variational inference, a popular optimization-based approach for approximate inference with probabilistic models. Our proposed module is guided by active learning principles: In addition to lecture materials on variational inference, we provide an accompanying class activity, an R shiny app, and guided labs based on real data applications of logistic regression and clustering documents using Latent Dirichlet Allocation with R code. The main goal of our module is to expose students to a method that facilitates statistical modeling and inference with large datasets. Using our proposed module as a foundation, instructors can adopt and adapt it to introduce more realistic case studies and applications in data science, Bayesian statistics, multivariate analysis, and statistical machine learning courses.
DOI: 10.6084/m9.figshare.23609578.v1
title: Introducing Variational Inference in Statistics and Data Science Curriculum

description: Probabilistic models such as logistic regression, Bayesian classification, neural networks, and models for natural language processing, are increasingly more present in both undergraduate and graduate statistics and data science curricula due to their wide range of applications. In this article, we present a one-week course module for students in advanced undergraduate and applied graduate courses on variational inference, a popular optimization-based approach for approximate inference with probabilistic models. Our proposed module is guided by active learning principles: In addition to lecture materials on variational inference, we provide an accompanying class activity, an R shiny app, and guided labs based on real data applications of logistic regression and clustering documents using Latent Dirichlet Allocation with R code. The main goal of our module is to expose students to a method that facilitates statistical modeling and inference with large datasets. Using our proposed module as a foundation, instructors can adopt and adapt it to introduce more realistic case studies and applications in data science, Bayesian statistics, multivariate analysis, and statistical machine learning courses.
DOI: 10.6084/m9.figshare.24112584.v1
title: Melded Confidence Intervals Do Not Provide Guaranteed Coverage

description: Melded confidence intervals were proposed as a way to combine two independent one-sample confidence intervals to obtain a two-sample confidence interval for a quantity like a difference or a ratio. Simulation-based work has suggested that melded confidence intervals always provide at least the nominal coverage. However, we show here that for the case of melded confidence intervals for a difference in population quantiles, the confidence intervals do not guarantee the nominal coverage. We derive a lower bound on the coverage for a one-sided confidence interval, and we show that there are pairs of distributions that make the coverage arbitrarily close to this lower bound. One specific example of our results is that the 95% melded upper bound on the difference between two population medians offers a guaranteed coverage of only 88.3% when both samples are of size 20.
DOI: 10.6084/m9.figshare.24975132.v1
title: Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot

description: Observational studies of treatment effects require adjustment for confounding variables. However, causal inference methods typically cannot deliver perfect adjustment on all measured baseline variables, and there is often ambiguity about which variables should be prioritized. Standard prioritization methods based on treatment imbalance alone neglect variables’ relationships with the outcome. We propose the joint variable importance plot to guide variable prioritization for observational studies. Since not all variables are equally relevant to the outcome, the plot adds outcome associations to quantify the potential confounding jointly with the standardized mean difference. To enhance comparisons on the plot between variables with different confounding relationships, we also derive and plot bias curves. Variable prioritization using the plot can produce recommended values for tuning parameters in many existing matching and weighting methods. We showcase the use of the joint variable importance plots in the design of a balance-constrained matched study to evaluate whether taking an antidiabetic medication, glyburide, increases the incidence of C-section delivery among pregnant individuals with gestational diabetes.