cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8206537
title: Data associated with "Lightning and radar characteristics of tornadic cells in landfalling tropical cyclones"

description: These data include all tropical cyclone tornado reports from 2013–2020, as part of all data from 1995–2022, included in the Storm Prediction Center (SPC) Tropical Cyclone TORnado database (TCTOR; Edwards and Mosier, 2022) used in the following publication: Schenkel, B., K. Calhoun, T. Sandmael, M. Ake, Z. Fruits, B. Kassel, and I. Schick, 2023: Lightning and radar characteristics of tornadic cells in landfalling tropical cyclones. <em>J. Geophys. Res.: Atmospheres</em>, <strong>accepted</strong>. <br> Each specific tropical cyclone tornado record has been extracted from the broader SPC tornado database, for all Atlantic and Gulf of Mexico tropical cyclones impacting the continental United States from 1995–2022. The tornado records were analyzed individually to determine their presence within the circulation envelope of either a classified or remnant tropical cyclone, without regard to fixed radii from tropical cyclone center, inland extent, temporal cutoffs before or after landfall, or other such arbitrary thresholds that may either exclude tropical cyclone events or include non-tropical cyclone tornadoes unnecessarily. These data will not be updated regularly. Citation for SPC TCTOR dataset: Edwards, R., & Mosier, R. M. (2022). Over a quarter century of TCTOR: Tropical cyclone tornadoes in the WSR-88D era [Dataset]. In Proc., 30th conf. on severe local storms (p. 171). Santa Fe, NM.
description: Please contact Ben Schenkel (benschenkel@gmail.com) for questions about the data on Zenodo. These data will not be kept up to date. Please refer to https://www.spc.noaa.gov/misc/edwards/TCTOR/ or contact roger.edwards@noaa.gov for the latest data updates.
description: {"references": ["Edwards, R., & Mosier, R. M. (2022). Over a quarter century of TCTOR: Trop-966 ical cyclone tornadoes in the WSR-88D era [Dataset]. In Proc., 30th conf. on967 severe local storms (p. 171). Santa Fe, NM."]}
DOI: 10.5281/zenodo.8206536
title: Data associated with "Lightning and radar characteristics of tornadic cells in landfalling tropical cyclones"

description: These data include all tropical cyclone tornado reports from 2013–2020, as part of all data from 1995–2022, included in the Storm Prediction Center (SPC) Tropical Cyclone TORnado database (TCTOR; Edwards and Mosier, 2022) used in the following publication: Schenkel, B., K. Calhoun, T. Sandmael, M. Ake, Z. Fruits, B. Kassel, and I. Schick, 2023: Lightning and radar characteristics of tornadic cells in landfalling tropical cyclones. <em>J. Geophys. Res.: Atmospheres</em>, <strong>accepted</strong>. <br> Each specific tropical cyclone tornado record has been extracted from the broader SPC tornado database, for all Atlantic and Gulf of Mexico tropical cyclones impacting the continental United States from 1995–2022. The tornado records were analyzed individually to determine their presence within the circulation envelope of either a classified or remnant tropical cyclone, without regard to fixed radii from tropical cyclone center, inland extent, temporal cutoffs before or after landfall, or other such arbitrary thresholds that may either exclude tropical cyclone events or include non-tropical cyclone tornadoes unnecessarily. These data will not be updated regularly. Citation for SPC TCTOR dataset: Edwards, R., & Mosier, R. M. (2022). Over a quarter century of TCTOR: Tropical cyclone tornadoes in the WSR-88D era [Dataset]. In Proc., 30th conf. on severe local storms (p. 171). Santa Fe, NM.
description: Please contact Ben Schenkel (benschenkel@gmail.com) for questions about the data on Zenodo. These data will not be kept up to date. Please refer to https://www.spc.noaa.gov/misc/edwards/TCTOR/ or contact roger.edwards@noaa.gov for the latest data updates.
description: {"references": ["Edwards, R., & Mosier, R. M. (2022). Over a quarter century of TCTOR: Trop-966 ical cyclone tornadoes in the WSR-88D era [Dataset]. In Proc., 30th conf. on967 severe local storms (p. 171). Santa Fe, NM."]}
DOI: 10.5281/zenodo.8208566
title: Distanace

description: Distance harvesting sites
DOI: 10.5281/zenodo.8208565
title: Distanace

description: Distance harvesting sites
DOI: 10.5281/zenodo.7935299
title: Dataset for Detecting Motorcyclists in Pedestrian Areas

description: The file PedestrianDataSet.zip contains 2 directories and 1 file, the directory named images contains all the images captured; the directory named “etiquetas” contains all annotations done based on four classes ordered as [MMNAP, MMAP, MNAP, PCP], the name's correspondence with the label is [motorcycle with motorcyclist not in pedestrian area, motorcycle with motorcyclist in pedestrian area, motorcycle without motorcyclist in pedestrian area, pedestrian in crosswalk]; the file Distribution.png is an image that explain how the DataSet is distributed. The dataset contains a 6324 images in .jpg format for a total of 643.9MB, the resolution of each image is 1280x720 (HD), and the weight per image is under 120 KB, the images were obtained from a closed circuit television CCTV free public access located in the city of Medellín Colombia consisting of 80 cameras located on the road, these images were captured mainly during daylight hours. The format of the name of each image is "image (camera number)_(date format"AAAAMMDD")_( time format "hhmmss"), where (AAAA) is the year, (MM) is the month, (DD) is the day, (HH) is the 24-hour hour, (MM) is the minute and (SS) is the second.
DOI: 10.5281/zenodo.7935298
title: Dataset for Detecting Motorcyclists in Pedestrian Areas

description: The file PedestrianDataSet.zip contains 2 directories and 1 file, the directory named images contains all the images captured; the directory named “etiquetas” contains all annotations done based on four classes ordered as [MMNAP, MMAP, MNAP, PCP], the name's correspondence with the label is [motorcycle with motorcyclist not in pedestrian area, motorcycle with motorcyclist in pedestrian area, motorcycle without motorcyclist in pedestrian area, pedestrian in crosswalk]; the file Distribution.png is an image that explain how the DataSet is distributed. The dataset contains a 6324 images in .jpg format for a total of 643.9MB, the resolution of each image is 1280x720 (HD), and the weight per image is under 120 KB, the images were obtained from a closed circuit television CCTV free public access located in the city of Medellín Colombia consisting of 80 cameras located on the road, these images were captured mainly during daylight hours. The format of the name of each image is "image (camera number)_(date format"AAAAMMDD")_( time format "hhmmss"), where (AAAA) is the year, (MM) is the month, (DD) is the day, (HH) is the 24-hour hour, (MM) is the minute and (SS) is the second.
DOI: 10.6084/m9.figshare.23823105
title: <b>Molecular phylogeny and character-mapping supports the synonymy of </b><i><b>Cordobia </b></i><b>and </b><i><b>Gallardoa </b></i><b>in </b><i><b>Mionandra </b></i><b>(Malpighiaceae)</b>

description: Data from the manuscript entitled "<b>Molecular phylogeny and character-mapping supports the synonymy of </b><i><b>Cordobia </b></i><b>and </b><i><b>Gallardoa </b></i><b>in </b><i><b>Mionandra </b></i><b>(Malpighiaceae)</b>"
DOI: 10.6084/m9.figshare.23823105.v1
title: <b>Molecular phylogeny and character-mapping supports the synonymy of </b><i><b>Cordobia </b></i><b>and </b><i><b>Gallardoa </b></i><b>in </b><i><b>Mionandra </b></i><b>(Malpighiaceae)</b>

description: Data from the manuscript entitled "<b>Molecular phylogeny and character-mapping supports the synonymy of </b><i><b>Cordobia </b></i><b>and </b><i><b>Gallardoa </b></i><b>in </b><i><b>Mionandra </b></i><b>(Malpighiaceae)</b>"
DOI: 10.5281/zenodo.5601988
title: Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study

description: Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges—and resultant bugs—involved in writing reliable yet performant imperative DL code by studying 250 open-source projects, consisting of 19.7 MLOC, along with 470 and 446 manually examined code patches and bug reports, respectively. The results indicate that hybridization: (i) is prone to API misuse, (ii) can result in performance degradation—the opposite of its intention, and (iii) has limited application due to execution mode incompatibility. We put forth several recommendations, best practices, and anti-patterns for effectively hybridizing imperative DL code, potentially benefiting DL practitioners, API designers, tool developers, and educators.
DOI: 10.5281/zenodo.5889201
title: Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study

description: Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges—and resultant bugs—involved in writing reliable yet performant imperative DL code by studying 250 open-source projects, consisting of 19.7 MLOC, along with 470 and 446 manually examined code patches and bug reports, respectively. The results indicate that hybridization: (i) is prone to API misuse, (ii) can result in performance degradation—the opposite of its intention, and (iii) has limited application due to execution mode incompatibility. We put forth several recommendations, best practices, and anti-patterns for effectively hybridizing imperative DL code, potentially benefiting DL practitioners, API designers, tool developers, and educators.
DOI: 10.5281/zenodo.5949836
title: Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study

description: Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges—and resultant bugs—involved in writing reliable yet performant imperative DL code by studying 250 open-source projects, consisting of 19.7 MLOC, along with 470 and 446 manually examined code patches and bug reports, respectively. The results indicate that hybridization: (i) is prone to API misuse, (ii) can result in performance degradation—the opposite of its intention, and (iii) has limited application due to execution mode incompatibility. We put forth several recommendations, best practices, and anti-patterns for effectively hybridizing imperative DL code, potentially benefiting DL practitioners, API designers, tool developers, and educators.
DOI: 10.5281/zenodo.5949878
title: Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study

description: Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges—and resultant bugs—involved in writing reliable yet performant imperative DL code by studying 250 open-source projects, consisting of 19.7 MLOC, along with 470 and 446 manually examined code patches and bug reports, respectively. The results indicate that hybridization: (i) is prone to API misuse, (ii) can result in performance degradation—the opposite of its intention, and (iii) has limited application due to execution mode incompatibility. We put forth several recommendations, best practices, and anti-patterns for effectively hybridizing imperative DL code, potentially benefiting DL practitioners, API designers, tool developers, and educators.
description: Support for this project was provided by PSC-CUNY Award #638010051, jointly funded by The Professional Staff Congress and The City University of New York.
DOI: 10.5281/zenodo.6403785
title: Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study

description: Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges—and resultant bugs—involved in writing reliable yet performant imperative DL code by studying 250 open-source projects, consisting of 19.7 MLOC, along with 470 and 446 manually examined code patches and bug reports, respectively. The results indicate that hybridization: (i) is prone to API misuse, (ii) can result in performance degradation—the opposite of its intention, and (iii) has limited application due to execution mode incompatibility. We put forth several recommendations, best practices, and anti-patterns for effectively hybridizing imperative DL code, potentially benefiting DL practitioners, API designers, tool developers, and educators.
description: Support for this project was provided by PSC-CUNY Award #638010051, jointly funded by The Professional Staff Congress and The City University of New York.
DOI: 10.5281/zenodo.5601987
title: Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study

description: Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges—and resultant bugs—involved in writing reliable yet performant imperative DL code by studying 250 open-source projects, consisting of 19.7 MLOC, along with 470 and 446 manually examined code patches and bug reports, respectively. The results indicate that hybridization: (i) is prone to API misuse, (ii) can result in performance degradation—the opposite of its intention, and (iii) has limited application due to execution mode incompatibility. We put forth several recommendations, best practices, and anti-patterns for effectively hybridizing imperative DL code, potentially benefiting DL practitioners, API designers, tool developers, and educators.
description: Support for this project was provided by PSC-CUNY Award #638010051, jointly funded by The Professional Staff Congress and The City University of New York.
DOI: 10.5281/zenodo.6403796
title: Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study

description: Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges—and resultant bugs—involved in writing reliable yet performant imperative DL code by studying 250 open-source projects, consisting of 19.7 MLOC, along with 470 and 446 manually examined code patches and bug reports, respectively. The results indicate that hybridization: (i) is prone to API misuse, (ii) can result in performance degradation—the opposite of its intention, and (iii) has limited application due to execution mode incompatibility. We put forth several recommendations, best practices, and anti-patterns for effectively hybridizing imperative DL code, potentially benefiting DL practitioners, API designers, tool developers, and educators.
description: Support for this project was provided by PSC-CUNY Award #638010051, jointly funded by The Professional Staff Congress and The City University of New York.
DOI: 10.5281/zenodo.8208571
title: spinus_spinus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8208570
title: spinus_spinus_spades_01.fasta.gz

description: .
DOI: 10.6084/m9.figshare.23822736
title: Untitled Item

description: Analysis ResultsBranchpointsCollagen fibersIntensity of CollagenOriginal ImageThresholdTransformed Image
DOI: 10.6084/m9.figshare.23822736.v1
title: Untitled Item

description: Analysis ResultsBranchpointsCollagen fibersIntensity of CollagenOriginal ImageThresholdTransformed Image
DOI: 10.6084/m9.figshare.23823078.v1
title: The mobility of Cu, Zn, Mo, and W in subduction zone fluids

description: Raw data for the study on the mobility of Cu,Zn, Mo, and W in subduction zone fluids, including chemical analyses of all phases
DOI: 10.6084/m9.figshare.23660241
title: Supplementary Material for: Methylation of the glucocorticoid receptor gene (NR3C1) in adolescents with a history of childhood adversity engaging in non-suicidal self-injury

description: Introduction: Non-suicidal self-injury (NSSI) is a large phenomenon among adolescents and adverse childhood experiences (ACE) are a major risk factor in its development. Malfunctioning of the hypothalamus-pituitary-adrenal (HPA) axis has been repeatedly reported for ACE as for NSSI. The glucocorticoid receptor (GR) is essential for the correct functioning of the HPA axis, thus alterations in the expression of the GR through altered methylation of the GR gene (NR3C1) (and more specifically exon 1F) might contribute to the development of NSSI in individuals with a past of ACEs as has been reported for different other mental disorders. Methods: In this case-control study we compared the methylation levels of exon 1F of the GR gene (NR3C1-1F) in adolescents with engagement in NSSI (n = 67) and a healthy control group (HC; n = 47). We preserved buccal swabs and used a mass-spectrometry based method called EpiTYPER for analyzing mean methylation of NR3C1-1F. Results: Adolescents of the NSSI group reported significantly more ACEs.The mean methylation level was about 3 % in both groups with no significant group differences. Furthermore, no significant relation was found between ACE and methylation of NR3C1-1F neither in the overall sample nor in the NSSI or HC group. Discussion/Conclusion: Our results are contradictory to previous research showing an increased methylation in individuals with ACE. Regarding relations between methylation of NR3C1-1F and mental disorders, previous studies reported inconsistent findings. Our study points to NSSI being either unrelated to methylation of NR3C1-1F or to yet not identified moderators on relations between methylation of NR3C1-1F and engagement in NSSI during adolescence.
DOI: 10.5281/zenodo.8208584
title: Clock documentation for STIMTEC and STIMTEC-X

description: Clock documentation to synchronize hydraulic measuring systems, and the seismic and hydraulic measuring systems used in projects STIMTEC and STIMTEC-X (Freiberg, Germany)
DOI: 10.5281/zenodo.8208583
title: Clock documentation for STIMTEC and STIMTEC-X

description: Clock documentation to synchronize hydraulic measuring systems, and the seismic and hydraulic measuring systems used in projects STIMTEC and STIMTEC-X (Freiberg, Germany)
DOI: 10.6084/m9.figshare.23659857.v1
title: Supplementary Material for: Delayed diagnosis of T-cell prolymphocytic leukemia: Approach to chronic lymphocytosis

description: We present a case of lymphocytosis assumed and managed initially as a chronic lymphocytic leukemia. Shortly after initial visit the patient’s condition deteriorated rapidly with hepatosplenomegaly, pleural effusion, ascites, and skin lesions. Flow cytometry (FC) showed the presence of clonal cell T-cell population, reported as T-cell lymphoma. Due to rapid clinical deterioration, urgent therapy with CHOEP (cyclophosphamide, doxorubicin, vincristine, etoposide, prednisone) was initiated, but with minimal response. This prompted further diagnostic testing and demonstrated tumor cells positivity for CD3, CD30 and TCL-1 markers. The diagnosis was changed to T-cell prolymphocytic leukemia. The patient responded well to Alemtuzumab (anti-CD52 monoclonal antibody) and reached complete remission. FC is an essential modality in assessing and screening circulating lymphocytes when a lymphoproliferative disorder (LPD) is suspected. There are several LPD that present with different degrees of clonal lymphocytosis. Reactive lymphocytosis should be appropriately investigated. Indolent LPDs can be surveyed by the internist or family physician, while more aggressive LPDs typically require management by hematologists.
DOI: 10.5281/zenodo.8208605
title: spinus_atriceps_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8208604
title: spinus_atriceps_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8208613
title: Daily Reports from the projects STIMTEC and STIMTEC-X

description: Daily Reports of the activities in the Research Mine Reiche Zeche (Freiberg, Germany) during the projects STIMTEC and STIMTEC-X