cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10551497
title: Interplay between a heptamethine cyanine dye sensitizer (IR806) and lanthanide upconversion nanoparticles

description: This folder contains all raw data underlying the results presented in a manuscript, submitted to Angewandte Chemie, and entitled:

Interplay between a heptamethine cyanine dye sensitizer (IR806) and lanthanide upconversion nanoparticles

Authored by:

Haichun Liu1, Abhilash Kulkarni1, Uliana Kostiv1, Elin Sandberg1, Anbharasi Lakshmanan1, Georgios A. Sotiriou2, Jerker Widengren1,*

1 Department of Applied Physics, KTH Royal Institute of Technology, Roslagstullsbacken 21, SE-106 91, Stockholm, Sweden

2Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, SE-171 77, Stockholm, Sweden

Corresponding author:

*jwideng@kth.se

The data files containing raw data and results of the analysis are grouped according to the different figures in the manuscript where the extracted results are presented.

ABSTRACT

Lanthanide-doped upconversion nanoparticles (UCNPs) have attractive emission properties but suffer from weak light-absorbing capacities and thereby relatively low brightnesses. This motivates using strongly absorbing dye molecules as antennas and sensitizers. However, despite much effort, understanding of this dye-UCNP interplay is still limited. Major sensitization mechanisms are still under discussion, largely because there is a lack of effective means to observe key factors such as dark state transitions within the dyes. Here, we established a combined spectroscopic procedure to systematically investigate the photophysics behind the dye-UCNP interaction, embracing fluorescence-based transient-state excitation-modulation, lifetime and correlation spectroscopy, and spectrofluorometry/spectrophotometry. With this procedure we studied the heptamethine cyanine dye IR806, a typical UCNP sensitizer, established its photophysical model, deciphered its photophysics in UCL-sensitization-related environments and could identify energy transfer from the IR806 singlet excited state to Yb3+ (UCNP sensitizer ion) as the dominant sensitization mechanism. Our studies suggest that IR806 can form non-emissive H-aggregates at the nanoparticle surfaces, which can be dissociated after certain light excitation duration (typically>100µs). Moreover, buildup of a non-fluorescent, photo-redox state of IR806 after longer irradiation times (10–100ms) can deleteriously affect its UCL sensitization, inferring an optimal excitation duration for dye-sensitized UCNPs, relevant for e.g. optical imaging applications.
DOI: 10.5281/zenodo.10551498
title: Interplay between a heptamethine cyanine dye sensitizer (IR806) and lanthanide upconversion nanoparticles

description: This folder contains all raw data underlying the results presented in a manuscript, submitted to Angewandte Chemie, and entitled:

Interplay between a heptamethine cyanine dye sensitizer (IR806) and lanthanide upconversion nanoparticles

Authored by:

Haichun Liu1, Abhilash Kulkarni1, Uliana Kostiv1, Elin Sandberg1, Anbharasi Lakshmanan1, Georgios A. Sotiriou2, Jerker Widengren1,*

1 Department of Applied Physics, KTH Royal Institute of Technology, Roslagstullsbacken 21, SE-106 91, Stockholm, Sweden

2Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, SE-171 77, Stockholm, Sweden

Corresponding author:

*jwideng@kth.se

The data files containing raw data and results of the analysis are grouped according to the different figures in the manuscript where the extracted results are presented.

ABSTRACT

Lanthanide-doped upconversion nanoparticles (UCNPs) have attractive emission properties but suffer from weak light-absorbing capacities and thereby relatively low brightnesses. This motivates using strongly absorbing dye molecules as antennas and sensitizers. However, despite much effort, understanding of this dye-UCNP interplay is still limited. Major sensitization mechanisms are still under discussion, largely because there is a lack of effective means to observe key factors such as dark state transitions within the dyes. Here, we established a combined spectroscopic procedure to systematically investigate the photophysics behind the dye-UCNP interaction, embracing fluorescence-based transient-state excitation-modulation, lifetime and correlation spectroscopy, and spectrofluorometry/spectrophotometry. With this procedure we studied the heptamethine cyanine dye IR806, a typical UCNP sensitizer, established its photophysical model, deciphered its photophysics in UCL-sensitization-related environments and could identify energy transfer from the IR806 singlet excited state to Yb3+ (UCNP sensitizer ion) as the dominant sensitization mechanism. Our studies suggest that IR806 can form non-emissive H-aggregates at the nanoparticle surfaces, which can be dissociated after certain light excitation duration (typically>100µs). Moreover, buildup of a non-fluorescent, photo-redox state of IR806 after longer irradiation times (10–100ms) can deleteriously affect its UCL sensitization, inferring an optimal excitation duration for dye-sensitized UCNPs, relevant for e.g. optical imaging applications.
DOI: 10.5281/zenodo.12548502
title: Interplay between a heptamethine cyanine dye sensitizer (IR806) and lanthanide upconversion nanoparticles

description: This folder contains all raw data underlying the results presented in a manuscript, submitted to Angewandte Chemie, and entitled:

Interplay between a heptamethine cyanine dye sensitizer (IR806) and lanthanide upconversion nanoparticles

Authored by:

Haichun Liu1, Abhilash Kulkarni1, Uliana Kostiv1, Elin Sandberg1, Anbharasi Lakshmanan1, Georgios A. Sotiriou2, Jerker Widengren1,*

1 Department of Applied Physics, KTH Royal Institute of Technology, Roslagstullsbacken 21, SE-106 91, Stockholm, Sweden

2Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, SE-171 77, Stockholm, Sweden

Corresponding author:

*jwideng@kth.se

The data files containing raw data and results of the analysis are grouped according to the different figures in the manuscript where the extracted results are presented.

ABSTRACT

Lanthanide-doped upconversion nanoparticles (UCNPs) have attractive emission properties but suffer from weak light-absorbing capacities and thereby relatively low brightnesses. This motivates using strongly absorbing dye molecules as antennas and sensitizers. However, despite much effort, understanding of this dye-UCNP interplay is still limited. Major sensitization mechanisms are still under discussion, largely because there is a lack of effective means to observe key factors such as dark state transitions within the dyes. Here, we established a combined spectroscopic procedure to systematically investigate the photophysics behind the dye-UCNP interaction, embracing fluorescence-based transient-state excitation-modulation, lifetime and correlation spectroscopy, and spectrofluorometry/spectrophotometry. With this procedure we studied the heptamethine cyanine dye IR806, a typical UCNP sensitizer, established its photophysical model, deciphered its photophysics in UCL-sensitization-related environments and could identify energy transfer from the IR806 singlet excited state to Yb3+ (UCNP sensitizer ion) as the dominant sensitization mechanism. Our studies suggest that IR806 can form non-emissive H-aggregates at the nanoparticle surfaces, which can be dissociated after certain light excitation duration (typically>100µs). Moreover, buildup of a non-fluorescent, photo-redox state of IR806 after longer irradiation times (10–100ms) can deleteriously affect its UCL sensitization, inferring an optimal excitation duration for dye-sensitized UCNPs, relevant for e.g. optical imaging applications.
DOI: 10.6084/m9.figshare.24767679
title: Proteomics Data for "Streptozotocin induced diabetes augments expression of renal cell surface proteins and induces hypertension in cathepsin B knockout mice"

description: Cathepsin B (CtsB) plays a role in several biological processes in physiology and pathophysiology. Streptozotocin (STZ) treatment causes pancreatic beta cell distruction and type 1 diabetes in rodents. Here, we hypothesized STZ induces kidney damage, hypertension, and changes in expression of renal epithelial cell surface proteins independent of CtsB expression. In CtsB knockout mice, STZ treatment induced kidney damage, hypertension, and down-regulation of NPR-B and NPR-C protiens, presumably in part by causing a rearrangement of the actin cyctoskeleton in renal epithelial cells. These results suggest the expression of CtsB is not essential for the development of kidney damage, hypertension and reorganization of the actin cyctoskeleton in kidney.
DOI: 10.6084/m9.figshare.24767679.v1
title: Proteomics Data for "Streptozotocin induced diabetes augments expression of renal cell surface proteins and induces hypertension in cathepsin B knockout mice"

description: Cathepsin B (CtsB) plays a role in several biological processes in physiology and pathophysiology. Streptozotocin (STZ) treatment causes pancreatic beta cell distruction and type 1 diabetes in rodents. Here, we hypothesized STZ induces kidney damage, hypertension, and changes in expression of renal epithelial cell surface proteins independent of CtsB expression. In CtsB knockout mice, STZ treatment induced kidney damage, hypertension, and down-regulation of NPR-B and NPR-C protiens, presumably in part by causing a rearrangement of the actin cyctoskeleton in renal epithelial cells. These results suggest the expression of CtsB is not essential for the development of kidney damage, hypertension and reorganization of the actin cyctoskeleton in kidney.
DOI: 10.57830/2304801
title: Appalachian Highlands Network Published Water Quality Data through 2023 from the APHN_WQ Project as of 2024-05-07

description: This data package was created 2024-06-22 05:12:27 by NPSTORET and includes selected project, location, and result data.

Data contained in the Appalachian Highlands Network NPSTORET back-end file (APHN_NPSTORET_BE_20240507.ACCDB) were filtered to include:
Organization:
- APHN: Appalachian Highlands Network
Station:
- Include Trip QC And All Station Visit Results
Value Status:
- Accepted or Certified (exported as Final) or Final

The data package is organized into five data tables:
- Projects.csv - describes the purpose and background of the monitoring efforts
- Locations.csv - documents the attributes of the monitoring locations/stations
- Results.csv - contains the field measurements, observations, and/or lab analyses for each sample/event/data grouping
- HUC12.csv - enumerates the domain of allowed values for 12-digit hydrologic unit codes utilized by the Locations datatable
- Characteristics.csv - enumerates the domain of characteristics available in NPSTORET to identify what was sampled, measured or observed in Results

Period of record for filtered data is 2009-03-10 to 2023-09-25.  This data package is a snapshot in time of one National Park Service project. The most current data for this project, which may be more or less extensive than that in this data package, can be found on the Water Quality Portal at:
https://www.waterqualitydata.us/data/Result/search?project=APHN_WQ
DOI: 10.15468/dl.e3sxut
title: Occurrence Download

description: A dataset containing 115 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Spain",
 "Geometry POLYGON((-0.91597 42.9049,-1.72938 42.11595,-2.45716 40.50827,-1.36548 39.9034,0.00447 41.01089,0.58242 42.685,-0.91597 42.9049))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Lucanus cervus (Linnaeus, 1758)",
 "Year 2019-2024"
 ]
}

 The dataset includes 115 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000114-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12548629
title: Ghost Echoes Revealed: Benchmarking Maintainability Metrics and Machine Learning Predictions Against Human Assessments

description: This repo contains a complete replication package, including raw data and scripts for the statistical analysis, for the paper "Ghost Echoes Revealed: Benchmarking Maintainability Metrics and Machine Learning Predictions Against Human Assessments" submitted to the industry track of the 40th International Conference on Software Maintenance and Evolution (ICSME), Flagstaff, AZ, USA, Oct 6-11, 2024.



Authors
Markus Borg, Marwa Ezzouhri, and Adam Tornhill



Abstract
As generative AI is expected to increase global code volumes, the importance of maintainability from a human perspective will become even greater. Various methods have been developed to identify the most important maintainability issues, including aggregated metrics and advanced Machine Learning (ML) models. This study benchmarks several maintainability prediction approaches, including State-of-the-Art (SotA) ML, SonarQube's Maintainability Rating, CodeScene's Code Health, and Microsoft's Maintainability Index. Our results indicate that CodeScene matches the accuracy of SotA ML and outperforms the average human expert. Importantly, unlike SotA ML, CodeScene also provides end users with actionable code smell details to remedy identified issues. Finally, caution is advised with SonarQube due to its tendency to generate many false positives. Unfortunately, our findings call into question the validity of previous studies that solely relied on SonarQube output for establishing ground truth labels. To improve reliability in future maintainability and technical debt studies, we recommend employing more accurate metrics. Moreover, reevaluating previous findings with Code Health would mitigate this revealed validity threat.



Repository Content
Two Jupyter Notebooks.







uc1_maintainability_prediction.ipynb: A Notebook for Use Case 1 - Maintainability Prediction.

uc2_liability_prediction.ipynb: A Notebook for Use Case 2 - Liability Prediction.



maintainability_data.csv: The dataset covering the 404 open-source files from the Maintainability Dataset (Bertrand et al., 2020)



Majority vote ground truth labels from Schnappinger et al. (2020)

Low-level code metrics from Bertrand et al. (2023)

Code Health

SonarQube output, i.e., TD Ratio and TD Time

Microsoft Maintainability index provided by MetricsTree






References




Schnappinger et al., A Software Maintainability Dataset, 10.6084/m9.figshare.12801215, 2020.

Schnappinger et al., Defining a Software Maintainability Dataset: Collecting, Aggregating and Analysing Expert Evaluations of Software Maintainability, in Proc. of the 36th International Conference on Software Maintenance and Evolution, pp. 278–289, 2020.

S. Bertrand et al., Replication and Extension of Schnappinger’s Study on Human-level Ordinal Maintainability Prediction Based on Static Code Metrics, in Proc. of the 27th International Conference on Evaluation and Assessment in Software Engineering, pp. 241–246, 2023.
DOI: 10.5281/zenodo.12548630
title: Ghost Echoes Revealed: Benchmarking Maintainability Metrics and Machine Learning Predictions Against Human Assessments

description: This repo contains a complete replication package, including raw data and scripts for the statistical analysis, for the paper "Ghost Echoes Revealed: Benchmarking Maintainability Metrics and Machine Learning Predictions Against Human Assessments" submitted to the industry track of the 40th International Conference on Software Maintenance and Evolution (ICSME), Flagstaff, AZ, USA, Oct 6-11, 2024.



Authors
Markus Borg, Marwa Ezzouhri, and Adam Tornhill



Abstract
As generative AI is expected to increase global code volumes, the importance of maintainability from a human perspective will become even greater. Various methods have been developed to identify the most important maintainability issues, including aggregated metrics and advanced Machine Learning (ML) models. This study benchmarks several maintainability prediction approaches, including State-of-the-Art (SotA) ML, SonarQube's Maintainability Rating, CodeScene's Code Health, and Microsoft's Maintainability Index. Our results indicate that CodeScene matches the accuracy of SotA ML and outperforms the average human expert. Importantly, unlike SotA ML, CodeScene also provides end users with actionable code smell details to remedy identified issues. Finally, caution is advised with SonarQube due to its tendency to generate many false positives. Unfortunately, our findings call into question the validity of previous studies that solely relied on SonarQube output for establishing ground truth labels. To improve reliability in future maintainability and technical debt studies, we recommend employing more accurate metrics. Moreover, reevaluating previous findings with Code Health would mitigate this revealed validity threat.



Repository Content
Two Jupyter Notebooks.







uc1_maintainability_prediction.ipynb: A Notebook for Use Case 1 - Maintainability Prediction.

uc2_liability_prediction.ipynb: A Notebook for Use Case 2 - Liability Prediction.



maintainability_data.csv: The dataset covering the 404 open-source files from the Maintainability Dataset (Bertrand et al., 2020)



Majority vote ground truth labels from Schnappinger et al. (2020)

Low-level code metrics from Bertrand et al. (2023)

Code Health

SonarQube output, i.e., TD Ratio and TD Time

Microsoft Maintainability index provided by MetricsTree






References




Schnappinger et al., A Software Maintainability Dataset, 10.6084/m9.figshare.12801215, 2020.

Schnappinger et al., Defining a Software Maintainability Dataset: Collecting, Aggregating and Analysing Expert Evaluations of Software Maintainability, in Proc. of the 36th International Conference on Software Maintenance and Evolution, pp. 278–289, 2020.

S. Bertrand et al., Replication and Extension of Schnappinger’s Study on Human-level Ordinal Maintainability Prediction Based on Static Code Metrics, in Proc. of the 27th International Conference on Evaluation and Assessment in Software Engineering, pp. 241–246, 2023.
DOI: 10.6084/m9.figshare.25211555
title: Correcting for Sampling Error in between-Cluster Effects: An Empirical Bayes Cluster-Mean Approach with Finite Population Corrections

description: With clustered data, such as where students are nested within schools or employees are nested within organizations, it is often of interest to estimate and compare associations among variables separately for each level. While researchers routinely estimate between-cluster effects using the sample cluster means of a predictor, previous research has shown that such practice leads to biased estimates of coefficients at the between level, and recent research has recommended the use of latent cluster means with the multilevel structural equation modeling framework. However, the latent cluster mean approach may not always be the best choice as it (a) relies on the assumption that the population cluster sizes are close to infinite, (b) requires a relatively large number of clusters, and (c) is currently only implemented in specialized software such as Mplus. In this paper, we show how using empirical Bayes estimates of the cluster means can also lead to consistent estimates of between-level coefficients, and illustrate how the empirical Bayes estimate can incorporate finite population corrections when information on population cluster sizes is available. Through a series of Monte Carlo simulation studies, we show that the empirical Bayes cluster-mean approach performs similarly to the latent cluster mean approach for estimating the between-cluster coefficients in most conditions when the infinite-population assumption holds, and applying the finite population correction provides reasonable point and interval estimates when the population is finite. The performance of EBM can be further improved with restricted maximum likelihood estimation and likelihood-based confidence intervals. We also provide an R function that implements the empirical Bayes cluster-mean approach, and illustrate it using data from the classic High School and Beyond Study.
DOI: 10.6084/m9.figshare.25211555.v2
title: Correcting for Sampling Error in between-Cluster Effects: An Empirical Bayes Cluster-Mean Approach with Finite Population Corrections

description: With clustered data, such as where students are nested within schools or employees are nested within organizations, it is often of interest to estimate and compare associations among variables separately for each level. While researchers routinely estimate between-cluster effects using the sample cluster means of a predictor, previous research has shown that such practice leads to biased estimates of coefficients at the between level, and recent research has recommended the use of latent cluster means with the multilevel structural equation modeling framework. However, the latent cluster mean approach may not always be the best choice as it (a) relies on the assumption that the population cluster sizes are close to infinite, (b) requires a relatively large number of clusters, and (c) is currently only implemented in specialized software such as Mplus. In this paper, we show how using empirical Bayes estimates of the cluster means can also lead to consistent estimates of between-level coefficients, and illustrate how the empirical Bayes estimate can incorporate finite population corrections when information on population cluster sizes is available. Through a series of Monte Carlo simulation studies, we show that the empirical Bayes cluster-mean approach performs similarly to the latent cluster mean approach for estimating the between-cluster coefficients in most conditions when the infinite-population assumption holds, and applying the finite population correction provides reasonable point and interval estimates when the population is finite. The performance of EBM can be further improved with restricted maximum likelihood estimation and likelihood-based confidence intervals. We also provide an R function that implements the empirical Bayes cluster-mean approach, and illustrate it using data from the classic High School and Beyond Study.
DOI: 10.6084/m9.figshare.23065437
title: Geographical, temporal variability and sexual size dimorphism of mandible in cave lion (<i>Panthera spelaea</i>) across Northern Eurasia

description: The mandible size variability including sexual size dimorphism (SSD) of the cave lion (<i>Panthera spelaea</i>) in the Pleistocene of Northern Eurasia time has been studied basing on the univariate and multivariate statistical analyses of 76 specimens from 43 localities. It has been confirmed that SSD in adult <i>P. spelaea</i>, as well as in extant <i>P. leo</i>, is the dominant form of group variability of mandible and, to somewhat lesser extent, of variability of cheek teeth. The lowest degree of SSD has been found in a relatively small Beringian subspecies, <i>P. spelaea vereshchagini</i>. The geographical variability of the cave lion from Western to Eastern Eurasia was not a cline at least in the Late Pleistocene. Our results confirm a decrease in overall mandible size from the Middle Pleistocene to the Late Pleistocene over a large part of the species’ range. We speculate that evolutionary changes in mandible size, lower canines or cheek teeth may have proceeded at different rates, allometry and probable heterochrony. Our results have added a new evidence to support the subspecies status of the Beringian lion (<i>P. spelaea vereshchagini</i>), but they are insufficient for other decisions on cave lion taxonomy at the subspecies level.