cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.25993008
title: Ultra-Efficient MCMC for Bayesian Longitudinal Functional Data Analysis

description: Functional mixed models are widely useful for regression analysis with dependent functional data, including longitudinal functional data with scalar predictors. However, existing algorithms for Bayesian inference with these models only provide either scalable computing or accurate approximations to the posterior distribution, but not both. We introduce a new MCMC sampling strategy for highly efficient and fully Bayesian regression with longitudinal functional data. Using a novel blocking structure paired with an orthogonalized basis reparameterization, our algorithm jointly samples the fixed effects regression functions together with all subject- and replicate-specific random effects functions. Crucially, the joint sampler optimizes sampling efficiency for these key parameters while preserving computational scalability. Perhaps surprisingly, our new MCMC sampling algorithm even surpasses state-of-the-art algorithms for frequentist estimation and variational Bayes approximations for functional mixed models—while also providing accurate posterior uncertainty quantification—and is orders of magnitude faster than existing Gibbs samplers. Simulation studies show improved point estimation and interval coverage in nearly all simulation settings over competing approaches. We apply our method to a large physical activity dataset to study how various demographic and health factors associate with intraday activity. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.25993008.v1
title: Ultra-efficient MCMC for Bayesian longitudinal functional data analysis

description: <b>Functional mixed models are widely useful for regression analysis with dependent functional data, including longitudinal functional data with scalar predictors. However, existing algorithms for Bayesian inference with these models only provide either scalable computing or accurate approximations to the posterior distribution, but not both. We introduce a new MCMC sampling strategy for highly efficient and fully Bayesian regression with longitudinal functional data. Using a novel blocking structure paired with an orthogonalized basis reparametrization, our algorithm jointly samples the fixed effects regression functions together with all subject- and replicate-specific random effects functions. Crucially, the joint sampler optimizes sampling efficiency for these key parameters while preserving computational scalability. Perhaps surprisingly, our new MCMC sampling algorithm even surpasses state-of-the-art algorithms for frequentist estimation and variational Bayes approximations for functional mixed models—while also providing accurate posterior uncertainty quantification—and is orders of magnitude faster than existing Gibbs samplers. Simulation studies show improved point estimation and interval coverage in nearly all simulation settings over competing approaches. We apply our method to a large physical activity dataset to study how various demographic and health factors associate with intraday activity. Supplementary materials for this article are available online.</b>
DOI: 10.6084/m9.figshare.25993008.v2
title: Ultra-Efficient MCMC for Bayesian Longitudinal Functional Data Analysis

description: Functional mixed models are widely useful for regression analysis with dependent functional data, including longitudinal functional data with scalar predictors. However, existing algorithms for Bayesian inference with these models only provide either scalable computing or accurate approximations to the posterior distribution, but not both. We introduce a new MCMC sampling strategy for highly efficient and fully Bayesian regression with longitudinal functional data. Using a novel blocking structure paired with an orthogonalized basis reparameterization, our algorithm jointly samples the fixed effects regression functions together with all subject- and replicate-specific random effects functions. Crucially, the joint sampler optimizes sampling efficiency for these key parameters while preserving computational scalability. Perhaps surprisingly, our new MCMC sampling algorithm even surpasses state-of-the-art algorithms for frequentist estimation and variational Bayes approximations for functional mixed models—while also providing accurate posterior uncertainty quantification—and is orders of magnitude faster than existing Gibbs samplers. Simulation studies show improved point estimation and interval coverage in nearly all simulation settings over competing approaches. We apply our method to a large physical activity dataset to study how various demographic and health factors associate with intraday activity. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.25914570
title: Multiple-Use Calibration for All Future Values and Exact Two-Sided Simultaneous Tolerance Intervals in Linear Regression

description: Multiple-use calibration using regression is an important statistical tool. Confidence sets for the <i>x</i>-values associated with all future <i>y</i>-values should guarantee a <i>key property</i>, which can be satisfied by simultaneous tolerance intervals (STI’s), and so multiple-use calibration requires the construction of STI’s. In this article, exact two-sided STI’s have been constructed for polynomial regression over any given covariate interval. There is a misconception that two-sided pointwise tolerance intervals (PTI’s) can be employed for multiple-use calibration. This article shows that the confidence sets based on the two-sided PTI’s do not satisfy the <i>key property</i> and so should not be used. Real-world data examples are given in this article for illustration. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.25914570.v1
title: Multiple-use calibration for all future values and exact two-sided simultaneous tolerance intervals in linear regression

description: Multiple-use calibration using regression is an important statistical tool. Confidence sets for the <i>x</i>-values associated with all future <i>y</i>-values should guarantee a <i>key property</i>, which can be satisfied by simultaneous tolerance intervals (STI’s), and so multiple-use calibration requires the construction of STI’s. In this paper, exact two-sided STI’s have been constructed for polynomial regression over any given covariate interval. There is a misconception that two-sided pointwise tolerance intervals (PTI’s) can be employed for multiple-use calibration. This paper shows that the confidence sets based on the two-sided PTI’s do not satisfy the <i>key property</i> and so should not be used. Real-world data examples are given in this paper for illustration.
DOI: 10.6084/m9.figshare.25914570.v2
title: Multiple-Use Calibration for All Future Values and Exact Two-Sided Simultaneous Tolerance Intervals in Linear Regression

description: Multiple-use calibration using regression is an important statistical tool. Confidence sets for the <i>x</i>-values associated with all future <i>y</i>-values should guarantee a <i>key property</i>, which can be satisfied by simultaneous tolerance intervals (STI’s), and so multiple-use calibration requires the construction of STI’s. In this article, exact two-sided STI’s have been constructed for polynomial regression over any given covariate interval. There is a misconception that two-sided pointwise tolerance intervals (PTI’s) can be employed for multiple-use calibration. This article shows that the confidence sets based on the two-sided PTI’s do not satisfy the <i>key property</i> and so should not be used. Real-world data examples are given in this article for illustration. Supplementary materials for this article are available online.
DOI: 10.15468/dl.ckd3ds
title: Occurrence Download

description: A dataset containing 2237 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Fragaria nilgerrensis Schltdl. ex J.Gay"
 ]
}

 The dataset includes 2237 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032644-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25625/4flfh8/1gnibm
title: harpaz_274.log

description: :unav
DOI: 10.25625/4flfh8/gkoo8b
title: harpaz_175.log

description: :unav
DOI: 10.25625/4flfh8/yyqt8e
title: harpaz_356.log

description: :unav
DOI: 10.25625/4flfh8/1hwk8v
title: harpaz_318.log

description: :unav
DOI: 10.25625/4flfh8/mvh1vf
title: harpaz_99.log

description: :unav
DOI: 10.25625/4flfh8/sxdbzj
title: harpaz_413.log

description: :unav
DOI: 10.25625/4flfh8/xxj52r
title: harpaz_469.log

description: :unav
DOI: 10.25625/4flfh8/zpg81a
title: harpaz_536.log

description: :unav
DOI: 10.25625/4flfh8/cxkjwc
title: harpaz_35.log

description: :unav
DOI: 10.25625/4flfh8/gnzypu
title: harpaz_222.log

description: :unav
DOI: 10.25625/4flfh8/hgywsk
title: harpaz_528.log

description: :unav
DOI: 10.25625/4flfh8/rzcfh2
title: harpaz_198.log

description: :unav
DOI: 10.25625/4flfh8/zsuxib
title: harpaz_21.log

description: :unav
DOI: 10.25625/4flfh8/0uypmj
title: harpaz_518.log

description: :unav
DOI: 10.25625/4flfh8/cv25mn
title: harpaz_436.log

description: :unav
DOI: 10.25625/4flfh8/grtqkg
title: harpaz_373.log

description: :unav
DOI: 10.25625/4flfh8/wftkth
title: harpaz_139.log

description: :unav
DOI: 10.25625/4flfh8/zw461i
title: harpaz_213.log

description: :unav
DOI: 10.25625/4flfh8/haej6m
title: harpaz_108.log

description: :unav
DOI: 10.25625/4flfh8/hnnwzc
title: harpaz_497.log

description: :unav
DOI: 10.25625/4flfh8/pcsgfr
title: harpaz_166.log

description: :unav
DOI: 10.25625/4flfh8/w1gzhn
title: harpaz_292.log

description: :unav
DOI: 10.25625/4flfh8/dhcboo
title: harpaz_328.log

description: :unav
DOI: 10.25625/4flfh8/s9qg0e
title: harpaz_472.log

description: :unav
DOI: 10.25625/4flfh8/vbnnl8
title: harpaz_330.log

description: :unav
DOI: 10.25625/4flfh8/wbx38x
title: harpaz_582.log

description: :unav
DOI: 10.25625/4flfh8/y4dxyt
title: harpaz_538.log

description: :unav
DOI: 10.5281/zenodo.7870623
title: Artifact for "Software Engineering Educational Experience in Building an Intelligent Tutoring System"

description: This is the supplementary material for our work on "Software Engineering Educational Experience in Building an Intelligent Tutoring System". The corresponding paper has been submitted to ACM Conference on International Computing Education Research (ICER) 2024.

The material is organized in two parts:



the artifacts for our user evaluations, and

additional artifact for the projects in our course that focuses on developing the intelligent tutoring system.


 

1. Artifacts for the User Evaluations

The user_evaluation/students folder includes the main artifacts for the conducted experiments with the first-year CS students:



students_questionnaire_part1.pdf -- It shows the PDF version of the web form of our survey for students before they solve the programming assignment.

students_questionnaire_part2_groupA.pdf -- It shows the PDF version of the web form of our survey for group A students after they solve programming assignments with ITS.

students_questionnaire_part2_groupB.pdf -- It shows the PDF version of the web form of our survey for group B students after they solve the programming assignments without ITS.

students_responses.xlsx -- It shows all students' responses.


The user_evaluation/tutors folder includes the main artifacts for the conducted surveys with the CS tutors:



tutors_questionnaire_part1.pdf -- It shows the PDF version of the web form of our survey for tutors before they grade students' programming assignments.

tutors_questionnaire_part3.pdf -- It shows the PDF version of the web form of our in-person interview for tutors after they grade students' programming assignments.

tutors_responses.xlsx -- It shows all tutors' responses.


2. Course Project Descriptions

The projects folder includes the main artifacts for the conducted experiments with the first-year CS students:



2022_project.pdf -- It includes the project descriptions, which have been provided to the third-year students in the first instance of our Software Engineering course.

2023_project.pdf -- It includes the project descriptions, which have been provided to the third-year students in the second instance of our Software Engineering course.

project_schedule.pdf -- It includes the overview of the assignments in our course illustrating the project's overall schedule.
DOI: 10.25625/4flfh8/9vjrkw
title: harpaz_362.log

description: :unav
DOI: 10.25625/4flfh8/ehfv38
title: harpaz_10.log

description: :unav
DOI: 10.25625/4flfh8/envnsr
title: harpaz_409.log

description: :unav
DOI: 10.25625/4flfh8/ozc6vh
title: harpaz_543.log

description: :unav
DOI: 10.25625/4flfh8/ponp7s
title: harpaz_505.log

description: :unav
DOI: 10.25625/4flfh8/dqbkmf
title: harpaz_80.log

description: :unav
DOI: 10.25625/4flfh8/igve5w
title: harpaz_351.log

description: :unav
DOI: 10.25625/4flfh8/ko13hi
title: harpaz_325.log

description: :unav
DOI: 10.25625/4flfh8/nasqmz
title: harpaz_178.log

description: :unav
DOI: 10.25625/4flfh8/u65egu
title: harpaz_66.log

description: :unav
DOI: 10.25625/4flfh8/zukttz
title: harpaz_495.log

description: :unav
DOI: 10.25625/4flfh8/9wpegi
title: harpaz_195.log

description: :unav
DOI: 10.25625/4flfh8/mcnn5f
title: harpaz_418.log

description: :unav
DOI: 10.25625/4flfh8/onspcs
title: harpaz_496.log

description: :unav
DOI: 10.25625/4flfh8/rrsvwf
title: harpaz_199.log

description: :unav
DOI: 10.25625/4flfh8/wpkmj8
title: harpaz_455.log

description: :unav
DOI: 10.25625/4flfh8/berx9m
title: var_harpaz_curves.py

description: :unav
DOI: 10.25625/4flfh8/pnzsr7
title: harpaz_463.log

description: :unav
DOI: 10.25625/4flfh8/pq4zce
title: harpaz_415.log

description: :unav
DOI: 10.25625/4flfh8/quk4xu
title: harpaz_603.log

description: :unav
DOI: 10.25625/4flfh8/sj740l
title: harpaz_526.log

description: :unav
DOI: 10.25625/4flfh8/tibutg
title: harpaz_427.log

description: :unav
DOI: 10.25625/4flfh8/3ttdng
title: harpaz_151.log

description: :unav
DOI: 10.25625/4flfh8/8sdun9
title: harpaz_17.log

description: :unav
DOI: 10.25625/4flfh8/jn501a
title: harpaz_547.log

description: :unav
DOI: 10.25625/4flfh8/sbsbq1
title: harpaz_68.log

description: :unav
DOI: 10.25625/4flfh8/tnqcu0
title: harpaz_231.log

description: :unav
DOI: 10.25625/4flfh8/ckuzmw
title: harpaz_180.log

description: :unav
DOI: 10.25625/4flfh8/ki356d
title: harpaz_105.log

description: :unav
DOI: 10.25625/4flfh8/mwyx4t
title: harpaz_464.log

description: :unav
DOI: 10.25625/4flfh8/udobrg
title: var_harpaz_analysis.ipynb

description: :unav
DOI: 10.25625/4flfh8/bto8yx
title: harpaz_414.log

description: :unav
DOI: 10.25625/4flfh8/fqxvz4
title: harpaz_481.log

description: :unav
DOI: 10.25625/4flfh8/nyh9np
title: harpaz_103.log

description: :unav
DOI: 10.25625/4flfh8/rhdw6b
title: harpaz_580.log

description: :unav
DOI: 10.25625/4flfh8/9nvnjh
title: harpaz_281.log

description: :unav
DOI: 10.25625/4flfh8/i5aalt
title: harpaz_353.log

description: :unav
DOI: 10.25625/4flfh8/mbfqp6
title: harpaz_185.log

description: :unav
DOI: 10.25625/4flfh8/rcmntc
title: harpaz_44.log

description: :unav
DOI: 10.25625/4flfh8/thivsb
title: harpaz_192.log

description: :unav
DOI: 10.25625/4flfh8/4gnl34
title: harpaz_45.log

description: :unav
DOI: 10.25625/4flfh8/7iv5fu
title: harpaz_492.log

description: :unav
DOI: 10.25625/4flfh8/oibcuk
title: harpaz_491.log

description: :unav
DOI: 10.25625/4flfh8/vmsw27
title: harpaz_273.log

description: :unav
DOI: 10.25625/4flfh8/zkygoj
title: harpaz_290.log

description: :unav
DOI: 10.25625/4flfh8/4cg1my
title: harpaz_562.log

description: :unav
DOI: 10.25625/4flfh8/egudxl
title: harpaz_424.log

description: :unav
DOI: 10.25625/4flfh8/gmcko1
title: harpaz_300.log

description: :unav
DOI: 10.25625/4flfh8/hj7p4v
title: harpaz_535.log

description: :unav
DOI: 10.25625/4flfh8/x3b1wf
title: harpaz_54.log

description: :unav
DOI: 10.25625/4flfh8/0fyvvo
title: harpaz_458.log

description: :unav
DOI: 10.25625/4flfh8/631lix
title: harpaz_183.log

description: :unav
DOI: 10.25625/4flfh8/jcbctm
title: harpaz_403.log

description: :unav
DOI: 10.25625/4flfh8/xqz4nb
title: harpaz_574.log

description: :unav
DOI: 10.25625/4flfh8/zkwrd0
title: harpaz_138.log

description: :unav
DOI: 10.25625/4flfh8/0c5jhy
title: harpaz_141.log

description: :unav
DOI: 10.25625/4flfh8/ehcpyk
title: harpaz_348.log

description: :unav
DOI: 10.25625/4flfh8/hlhlvp
title: harpaz_565.log

description: :unav
DOI: 10.25625/4flfh8/q6rnmw
title: harpaz_146.log

description: :unav
DOI: 10.25625/4flfh8/simemg
title: harpaz_20.log

description: :unav
DOI: 10.25625/4flfh8/kdvatv
title: harpaz_286.log

description: :unav
DOI: 10.25625/4flfh8/om5h5a
title: harpaz_101.log

description: :unav
DOI: 10.25625/4flfh8/onwbuy
title: harpaz_140.log

description: :unav
DOI: 10.25625/4flfh8/opcmwe
title: harpaz_590.log

description: :unav
DOI: 10.25625/4flfh8/osu8ig
title: harpaz_254.log

description: :unav
DOI: 10.15468/dl.3ecuqn
title: Occurrence Download

description: A dataset containing 2605 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is one of (Benin, Burkina Faso, Cabo Verde, Côte d’Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, Togo)",
 "DatasetKey is one of (Naturalis Biodiversity Center (NL) - Botany, Floristic diversity of the protected areas of Mont Kouffé and Wari Maro in the north of Benin, Floristic diversity of potential exploitable ranches in the Zou and Collines departments of Benin)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (28eb1a3f-1c15-4a95-931a-4af90ecb574d, b2db85d5-c5fe-4125-9e36-590a5f651829, d2583793-e5c1-41ae-83bd-86300d34e2cb, 5ca9a113-2397-4540-84b4-41114a1404ef, 396d5f30-dea9-11db-8ab4-b8a03c50a862, 633602e8-4c6f-4619-b433-c4968da3ed08)",
 "TaxonKey is Vitaceae"
 ]
}

 The dataset includes 2605 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032645-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25625/4flfh8/4l9mkq
title: harpaz_0.log

description: :unav
DOI: 10.25625/4flfh8/bgstnp
title: harpaz_421.log

description: :unav
DOI: 10.25625/4flfh8/edyggn
title: harpaz_419.log

description: :unav
DOI: 10.25625/4flfh8/w7cwtg
title: harpaz_163.log

description: :unav
DOI: 10.25625/4flfh8/wjvmdl
title: harpaz_265.log

description: :unav
DOI: 10.15468/dl.qycpdw
title: Occurrence Download

description: A dataset containing 1157 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Israel",
 "TaxonKey is Lactuca serriola L."
 ]
}

 The dataset includes 1157 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032646-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25625/4flfh8/e4pygp
title: harpaz_337.log

description: :unav
DOI: 10.25625/4flfh8/hmqphx
title: harpaz_376.log

description: :unav
DOI: 10.25625/4flfh8/ndmxxa
title: harpaz_23.log

description: :unav
DOI: 10.25625/4flfh8/orosup
title: harpaz_524.log

description: :unav
DOI: 10.25625/4flfh8/ovcgnu
title: harpaz_48.log

description: :unav
DOI: 10.25625/4flfh8/psp3sm
title: harpaz_14.log

description: :unav
DOI: 10.25625/4flfh8/cvsutw
title: harpaz_483.log

description: :unav
DOI: 10.25625/4flfh8/max9s5
title: harpaz_78.log

description: :unav
DOI: 10.25625/4flfh8/sxeon2
title: harpaz_100.log

description: :unav
DOI: 10.25625/4flfh8/ycmo0f
title: harpaz_245.log

description: :unav
DOI: 10.25625/4flfh8/yzzlum
title: harpaz_346.log

description: :unav
DOI: 10.25625/4flfh8/zkdnqw
title: harpaz_336.log

description: :unav
DOI: 10.25625/4flfh8/6nl7ha
title: harpaz_445.log

description: :unav
DOI: 10.25625/4flfh8/ayas87
title: harpaz_508.log

description: :unav
DOI: 10.25625/4flfh8/g791ze
title: harpaz_50.log

description: :unav
DOI: 10.25625/4flfh8/tv2xfy
title: harpaz_16.log

description: :unav
DOI: 10.25625/4flfh8/vr6oto
title: harpaz_197.log

description: :unav
DOI: 10.25625/4flfh8/03amd3
title: harpaz_24.log

description: :unav
DOI: 10.25625/4flfh8/ajspkq
title: harpaz_557.log

description: :unav
DOI: 10.25625/4flfh8/epv90k
title: harpaz_150.log

description: :unav
DOI: 10.25625/4flfh8/rfcjmt
title: harpaz_251.log

description: :unav
DOI: 10.25625/4flfh8/v1vpch
title: harpaz_447.log

description: :unav
DOI: 10.34740/kaggle/dsv/9011096
title: Public Opinion on Climate Change (Updated Daily)
DOI: 10.25625/4flfh8/8zqvmx
title: harpaz_487.log

description: :unav
DOI: 10.25625/4flfh8/cw53n4
title: sums_of_cubes_constants.py

description: :unav
DOI: 10.25625/4flfh8/eyumkx
title: harpaz_433.log

description: :unav
DOI: 10.25625/4flfh8/runmoj
title: harpaz_389.log

description: :unav
DOI: 10.25625/4flfh8/vi7dhm
title: harpaz_589.log

description: :unav
DOI: 10.25625/4flfh8/0s2tmb
title: harpaz_410.log

description: :unav
DOI: 10.25625/4flfh8/0uzhlm
title: harpaz_analysis.ipynb

description: :unav
DOI: 10.25625/4flfh8/hxpka9
title: harpaz_147.log

description: :unav
DOI: 10.25625/4flfh8/mkzqou
title: harpaz_360.log

description: :unav
DOI: 10.25625/4flfh8/rfsvwj
title: harpaz_454.log

description: :unav
DOI: 10.25625/4flfh8/u5bp3d
title: harpaz_135.log

description: :unav
DOI: 10.25625/4flfh8/1ntyhp
title: harpaz_400.log

description: :unav
DOI: 10.25625/4flfh8/32kt1l
title: harpaz_470.log

description: :unav
DOI: 10.25625/4flfh8/3cu0t0
title: harpaz_119.log

description: :unav
DOI: 10.25625/4flfh8/gtedsm
title: harpaz_237.log

description: :unav
DOI: 10.25625/4flfh8/ncy7vq
title: harpaz_394.log

description: :unav
DOI: 10.25625/4flfh8/sbmw7m
title: harpaz_304.log

description: :unav
DOI: 10.25625/4flfh8/emywk9
title: harpaz_426.log

description: :unav
DOI: 10.25625/4flfh8/kpv0jr
title: harpaz_451.log

description: :unav
DOI: 10.25625/4flfh8/l0zagy
title: harpaz_604.log

description: :unav
DOI: 10.25625/4flfh8/mh3fbg
title: harpaz_53.log

description: :unav
DOI: 10.25625/4flfh8/n6drn3
title: harpaz_357.log

description: :unav
DOI: 10.25625/4flfh8/ol9tni
title: harpaz_232.log

description: :unav
DOI: 10.25625/4flfh8/ahqoyu
title: harpaz_279.log

description: :unav
DOI: 10.25625/4flfh8/efzuv0
title: harpaz_57.log

description: :unav
DOI: 10.25625/4flfh8/iaffre
title: harpaz_404.log

description: :unav
DOI: 10.25625/4flfh8/yynyr2
title: harpaz_530.log

description: :unav
DOI: 10.25625/4flfh8/2sgwxo
title: harpaz_584.log

description: :unav
DOI: 10.25625/4flfh8/f1tbeq
title: harpaz_350.log

description: :unav
DOI: 10.25625/4flfh8/kksaer
title: harpaz_564.log

description: :unav
DOI: 10.25625/4flfh8/l7ho0x
title: harpaz_460.log

description: :unav
DOI: 10.25625/4flfh8/nv2ag7
title: harpaz_157.log

description: :unav
DOI: 10.25625/4flfh8/zfchge
title: harpaz_257.log

description: :unav
DOI: 10.25625/4flfh8/bt3oqu
title: harpaz_534.log

description: :unav
DOI: 10.25625/4flfh8/cvopyh
title: harpaz_131.log

description: :unav
DOI: 10.25625/4flfh8/dcfqvl
title: harpaz_322.log

description: :unav
DOI: 10.25625/4flfh8/lpfuae
title: harpaz_411.log

description: :unav
DOI: 10.25625/4flfh8/qi6i2z
title: harpaz_395.log

description: :unav
DOI: 10.25625/4flfh8/qzk2dp
title: harpaz_249.log

description: :unav
DOI: 10.25625/4flfh8/cavzs3
title: harpaz_266.log

description: :unav
DOI: 10.25625/4flfh8/kgogem
title: harpaz_392.log

description: :unav
DOI: 10.25625/4flfh8/qnsqpk
title: harpaz_64.log

description: :unav
DOI: 10.25625/4flfh8/tc3y3v
title: harpaz_38.log

description: :unav
DOI: 10.25625/4flfh8/wad1vi
title: harpaz_233.log

description: :unav
DOI: 10.25625/4flfh8/dzvkcq
title: harpaz_115.log

description: :unav
DOI: 10.25625/4flfh8/ghbmoo
title: harpaz_155.log

description: :unav
DOI: 10.25625/4flfh8/ogws2v
title: harpaz_239.log

description: :unav
DOI: 10.25625/4flfh8/op2lqy
title: harpaz_32.log

description: :unav