cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10644180
title: Chapter 2 Louise Finlayson Thesis

description: Figure 2:

optical_property_plot.py

ua_d.csv

ua_e.csv

ua_h.csv

ua_meg_dermis.csv

ua_sc.csv

us_d.csv

us_e.csv

us_sc.csv

 

Figure 3:

fulldataplot_direct.py

fulldataplot_diffuse.py

200-400_diffuse.csv

200-400_direct.csv

400-700_diffuse.csv

400-700_holes_direct.csv

700-1000_diffuse.csv

700-1000_direct.csv

 

Figure 4:

percentage plot.py

0umSC_holes.csv

10umSC_holes.csv

14_8umSC_holes.csv

20umSC_holes.csv

30umSC_holes.csv

 

Figure 5:

plot.py

222jmean_45deg.dat

222jmean_65deg.dat

222jmean_direct.dat

300jmean_65deg.dat

300jmean_45deg.dat

300jmean_direct.dat

350jmean_45deg.dat

350jmean_65deg.dat

350jmean_direct.dat

400jmean_45deg.dat

400jmean_65deg.dat

400jmean_direct.dat

630jmean_45.dat

630jmean_65.dat

630jmean_direct.dat

800jmean_45.dat

800jmean_65.dat

800jmean_direct.dat

 

Figure 6:

contour_image.py

222jmean_01mm_beam.dat

300jmean_01mm_beam.dat

350jmean_01mm_beam.dat

400jmean_01mm_beam.dat

630jmean_beam_no_rep_bound.dat

800jmean_beam_no_rep_bound.dat
DOI: 10.5281/zenodo.10644181
title: Chapter 2 Louise Finlayson Thesis

description: Figure 2:

optical_property_plot.py

ua_d.csv

ua_e.csv

ua_h.csv

ua_meg_dermis.csv

ua_sc.csv

us_d.csv

us_e.csv

us_sc.csv

 

Figure 3:

fulldataplot_direct.py

fulldataplot_diffuse.py

200-400_diffuse.csv

200-400_direct.csv

400-700_diffuse.csv

400-700_holes_direct.csv

700-1000_diffuse.csv

700-1000_direct.csv

 

Figure 4:

percentage plot.py

0umSC_holes.csv

10umSC_holes.csv

14_8umSC_holes.csv

20umSC_holes.csv

30umSC_holes.csv

 

Figure 5:

plot.py

222jmean_45deg.dat

222jmean_65deg.dat

222jmean_direct.dat

300jmean_65deg.dat

300jmean_45deg.dat

300jmean_direct.dat

350jmean_45deg.dat

350jmean_65deg.dat

350jmean_direct.dat

400jmean_45deg.dat

400jmean_65deg.dat

400jmean_direct.dat

630jmean_45.dat

630jmean_65.dat

630jmean_direct.dat

800jmean_45.dat

800jmean_65.dat

800jmean_direct.dat

 

Figure 6:

contour_image.py

222jmean_01mm_beam.dat

300jmean_01mm_beam.dat

350jmean_01mm_beam.dat

400jmean_01mm_beam.dat

630jmean_beam_no_rep_bound.dat

800jmean_beam_no_rep_bound.dat
DOI: 10.6084/m9.figshare.12495539
title: Additional file 16 of Gene coexpression network analysis and tissue-specific profiling of gene expression in jute (Corchorus capsularis L.)

description: Additional file 16: Table S13. The primer sequences used for ELF, for the seven genes that were directly regulated by transcription factor (OMO55970.1), and for the five genes that were randomly selected from among the differentially expressed genes.
DOI: 10.6084/m9.figshare.12495539.v1
title: Additional file 16 of Gene coexpression network analysis and tissue-specific profiling of gene expression in jute (Corchorus capsularis L.)

description: Additional file 16: Table S13. The primer sequences used for ELF, for the seven genes that were directly regulated by transcription factor (OMO55970.1), and for the five genes that were randomly selected from among the differentially expressed genes.
DOI: 10.5281/zenodo.10646327
title: Chapter 3 Louise Finlayson Thesis

description: Figure 4:

flu_validation_plot.py

validation-420.dat

validation-630.dat

 

Figure 5:

esc_flur_plot.py

esc_flur.dat

pl_absor.dat

 

Figure 6:

obs_flu_plot.py

obs_flur.dat

obs_flur420.dat

 

Figure 7:

PDD_plot.py

1min_pdd.dat

2min_pdd.dat

3min_pdd.dat

4min_pdd.dat

5min_pdd.dat

6min_pdd.dat

7min_pdd.dat

8min_pdd.dat

9min_pdd.dat

10min_pdd.dat

20min_pdd.dat

30min_pdd.dat

40min_pdd.dat

50min_pdd.dat

60min_pdd.dat

 

Figure 9:

jmean_dataplot.py

fluence_wang_linear.csv

jmean-wang_linear.dat

 

Figure 10:

wang_6b_s0.py

so_linear_m3_wang.csv

2632so.dat

 

Figure 11:

wang_6b_o23.py

o23_linear_m3_wang.csv

2632o23.dat

 

Figure 12:

wang_6a_o21.py

o21_linear_m3_wang.csv

2632o21.dat
DOI: 10.5281/zenodo.10646328
title: Chapter 3 Louise Finlayson Thesis

description: Figure 4:

flu_validation_plot.py

validation-420.dat

validation-630.dat

 

Figure 5:

esc_flur_plot.py

esc_flur.dat

pl_absor.dat

 

Figure 6:

obs_flu_plot.py

obs_flur.dat

obs_flur420.dat

 

Figure 7:

PDD_plot.py

1min_pdd.dat

2min_pdd.dat

3min_pdd.dat

4min_pdd.dat

5min_pdd.dat

6min_pdd.dat

7min_pdd.dat

8min_pdd.dat

9min_pdd.dat

10min_pdd.dat

20min_pdd.dat

30min_pdd.dat

40min_pdd.dat

50min_pdd.dat

60min_pdd.dat

 

Figure 9:

jmean_dataplot.py

fluence_wang_linear.csv

jmean-wang_linear.dat

 

Figure 10:

wang_6b_s0.py

so_linear_m3_wang.csv

2632so.dat

 

Figure 11:

wang_6b_o23.py

o23_linear_m3_wang.csv

2632o23.dat

 

Figure 12:

wang_6a_o21.py

o21_linear_m3_wang.csv

2632o21.dat
DOI: 10.5281/zenodo.10646374
title: Chapter 4 Louise Finlayson Thesis

description: Figure 10:

white_grey_depth.py

validation-gbm.dat

validation-greymatter.dat

validation-intralipid.dat

validation-saline.dat

validation-whitematter.dat

 

Figure 12:

pen_depth_plot.py

pen_depth_plot_y.py

pen_depth_plot_z.py

jmean_run2NB.dat

o21_full_run2NB.dat

rhokap_run2NB.dat

tumour.dat

 

Figure 13:

tumour_percentage.py

tumkill_full_994.dat

tumkill_full_1.dat

 

Figure 14:

fractionation.py

o21_slice_run2NB.dat

o23_slice_run2NB.dat

power_run2NB.dat

s0_slice_run2NB.dat

 

Figure 15:

cell_kill_fractionated.py

percent_left_run2NB.dat

percent_left_run11NB.dat

percent_left_run12NB.dat

percent_left_run13NB.dat

 

Figure 16:

tumour_percentage.py

tumkill_slice_run2r.dat

 

Figure 17:

percent_left_run2NB.dat

percent_left_run9NB.dat

percent_left_run10NB.dat

 

Figure 18:

percent_left_run1NB.dat

percent_left_run2NB.dat

percent_left_run3NB.dat

percent_left_run4NB.dat

 

Figure 19:

percent_left_run1NB.dat

percent_left_run2NB.dat

 

Figure 20:

percent_left_run2NB.dat

percent_left_run14NB.dat

percent_left_run15NB.dat

 

Figure 21:

percent_left_run2NB.dat

percent_left_run18NB.dat

percent_left_run19NB.dat

 

Figure 22:

percent_left_run2NB.dat

percent_left_run20NB.dat

percent_left_run21NB.dat
DOI: 10.5281/zenodo.10646375
title: Chapter 4 Louise Finlayson Thesis

description: Figure 10:

white_grey_depth.py

validation-gbm.dat

validation-greymatter.dat

validation-intralipid.dat

validation-saline.dat

validation-whitematter.dat

 

Figure 12:

pen_depth_plot.py

pen_depth_plot_y.py

pen_depth_plot_z.py

jmean_run2NB.dat

o21_full_run2NB.dat

rhokap_run2NB.dat

tumour.dat

 

Figure 13:

tumour_percentage.py

tumkill_full_994.dat

tumkill_full_1.dat

 

Figure 14:

fractionation.py

o21_slice_run2NB.dat

o23_slice_run2NB.dat

power_run2NB.dat

s0_slice_run2NB.dat

 

Figure 15:

cell_kill_fractionated.py

percent_left_run2NB.dat

percent_left_run11NB.dat

percent_left_run12NB.dat

percent_left_run13NB.dat

 

Figure 16:

tumour_percentage.py

tumkill_slice_run2r.dat

 

Figure 17:

percent_left_run2NB.dat

percent_left_run9NB.dat

percent_left_run10NB.dat

 

Figure 18:

percent_left_run1NB.dat

percent_left_run2NB.dat

percent_left_run3NB.dat

percent_left_run4NB.dat

 

Figure 19:

percent_left_run1NB.dat

percent_left_run2NB.dat

 

Figure 20:

percent_left_run2NB.dat

percent_left_run14NB.dat

percent_left_run15NB.dat

 

Figure 21:

percent_left_run2NB.dat

percent_left_run18NB.dat

percent_left_run19NB.dat

 

Figure 22:

percent_left_run2NB.dat

percent_left_run20NB.dat

percent_left_run21NB.dat
DOI: 10.6084/m9.figshare.12495545
title: Additional file 1 of Gene coexpression network analysis and tissue-specific profiling of gene expression in jute (Corchorus capsularis L.)

description: Additional file 1: Table S1. Transcriptional genes that were identified in stem bast tissue during the vegetative growth period (BVGP).
DOI: 10.6084/m9.figshare.12495545.v1
title: Additional file 1 of Gene coexpression network analysis and tissue-specific profiling of gene expression in jute (Corchorus capsularis L.)

description: Additional file 1: Table S1. Transcriptional genes that were identified in stem bast tissue during the vegetative growth period (BVGP).
DOI: 10.5281/zenodo.10646475
title: Chapter 5 Louise Finlayson Thesis

description: Figure 3:

initial_temp.txt

 

Figure 4:

max_temp_timeNB.dat

percent_left_run2NB.dat

 

Figure 5:

temp_x_y_z_view.py

temp_slice_max_run2NB.dat

 

Figure 6:

max_temp_timeNB.dat

max_temp_time_run10NB.dat

percent_left_run2NB.dat

percent_left_run10NB.dat

 

Figure 7:

max_temp_timeNB.dat

max_temp_time_run9NB.dat

percent_left_run2NB.dat

percent_left_run9NB.dat

 

Figure 8:

max_temp_timeNB.dat

max_temp_time_run1NB.dat

percent_left_run1NB.dat

percent_left_run2NB.dat

 

Figure 9:

max_temp_timeNB.dat

max_temp_time_run56NB.dat

percent_left_run2NB.dat

percent_left_run56NB.dat

percent_left_run62NB.dat

 

Figure 10:

max_temp_time_run73NB.dat

 

Figure 11:

max_temp_timeNB.dat

max_temp_time_run71NB.dat

percent_left_run2NB.dat

percent_left_run71NB.dat

percent_left_run72NB.dat

 

Figure 12:

max_temp_timeNB.dat

max_temp_time_run1NB.dat

percent_left_run1NB.dat

percent_left_run2NB.dat

 

Figure 13:

jmean_run74NB.dat

jmean_run2NB.dat
DOI: 10.5281/zenodo.10646476
title: Chapter 5 Louise Finlayson Thesis

description: Figure 3:

initial_temp.txt

 

Figure 4:

max_temp_timeNB.dat

percent_left_run2NB.dat

 

Figure 5:

temp_x_y_z_view.py

temp_slice_max_run2NB.dat

 

Figure 6:

max_temp_timeNB.dat

max_temp_time_run10NB.dat

percent_left_run2NB.dat

percent_left_run10NB.dat

 

Figure 7:

max_temp_timeNB.dat

max_temp_time_run9NB.dat

percent_left_run2NB.dat

percent_left_run9NB.dat

 

Figure 8:

max_temp_timeNB.dat

max_temp_time_run1NB.dat

percent_left_run1NB.dat

percent_left_run2NB.dat

 

Figure 9:

max_temp_timeNB.dat

max_temp_time_run56NB.dat

percent_left_run2NB.dat

percent_left_run56NB.dat

percent_left_run62NB.dat

 

Figure 10:

max_temp_time_run73NB.dat

 

Figure 11:

max_temp_timeNB.dat

max_temp_time_run71NB.dat

percent_left_run2NB.dat

percent_left_run71NB.dat

percent_left_run72NB.dat

 

Figure 12:

max_temp_timeNB.dat

max_temp_time_run1NB.dat

percent_left_run1NB.dat

percent_left_run2NB.dat

 

Figure 13:

jmean_run74NB.dat

jmean_run2NB.dat
DOI: 10.6084/m9.figshare.12495551
title: Additional file 2 of Gene coexpression network analysis and tissue-specific profiling of gene expression in jute (Corchorus capsularis L.)

description: Additional file 2: Table S2. Transcriptional genes that were identified in bast tissue during the flowering period (BFP).
DOI: 10.6084/m9.figshare.12495551.v1
title: Additional file 2 of Gene coexpression network analysis and tissue-specific profiling of gene expression in jute (Corchorus capsularis L.)

description: Additional file 2: Table S2. Transcriptional genes that were identified in bast tissue during the flowering period (BFP).
DOI: 10.5281/zenodo.8428761
title: Evaluation Tools for Human-AI Interactions Involving Older Adults with Mild Cognitive Impairments

description: As artificial intelligence (AI) systems have already proven useful in human lives generally, there is an opportunity for specialized human-AI interaction (HAI) systems to support and provide care for older adults with mild cognitive impairment (MCI). However, the integration of this technology in this population must be thoughtfully designed to accommodate specific needs and limitations. This includes careful measurement of both humans and systems. We developed an evolving dataset categorizing relevant measurement tools into five groups: cognitive ability, demographics & personality, activity level, state of mind, and perceptions of the AI system. This dataset will serve as a valuable resource for future research on AI for MCI, aiding in the identification of promising areas and trends in AI systems for older adults with MCI as well as providing essential tools for future studies.
DOI: 10.5281/zenodo.8428879
title: Evaluation Tools for Human-AI Interactions Involving Older Adults with Mild Cognitive Impairments

description: As artificial intelligence (AI) systems have already proven useful in human lives generally, there is an opportunity for specialized human-AI interaction (HAI) systems to support and provide care for older adults with mild cognitive impairment (MCI). However, the integration of this technology in this population must be thoughtfully designed to accommodate specific needs and limitations. This includes careful measurement of both humans and systems. We developed an evolving dataset categorizing relevant measurement tools into five groups: cognitive ability, demographics & personality, activity level, state of mind, and perceptions of the AI system. This dataset will serve as a valuable resource for future research on AI for MCI, aiding in the identification of promising areas and trends in AI systems for older adults with MCI as well as providing essential tools for future studies.
DOI: 10.6084/m9.figshare.25289404.v1
title: MArine Bioprospecting PATent dataset

description: The goal of the MABPAT database is to provide a useful insight into the scope and scale of marine bioprospecting. We offer the research and practitioner community with direct access to marine genetic sequences submitted to patent bureaus, their potential to encode biological functions, species taxonomy, type of applicant, and other information that might be relevant for the discussion on the access and utilization of marine genetic resources.Erik Zhivkoplias, Agnes Pranindita, Paul Dunshirn et al. Novel database reveals growing prominence of deep-sea life for marine bioprospecting, https://doi.org/10.21203/rs.3.rs-3136354/v1
DOI: 10.17632/3c542bxz65.1
title: Description of Simulated Fear Dynamics Data

description: The provided code generates synthetic data to simulate the dynamics of fear under the influence of external and decision-making forces. Let's delve into the components and describe the characteristics of the generated data:

System Components:
Mass of Fear Element: mass: A constant representing the mass of the fear element. In physics, mass influences how an object responds to external forces.
Time Array: Time: An array representing time, ranging from 0 to 10 with steps of 0.1. This defines the temporal aspect of the simulation.
Forces:

external_force: Simulated external force, represented by the sine function over time. It models environmental factors impacting fear.
decision_force: Simulated decision-making force, a linear function of time. It represents an illustrative force related to decision-making.
Fear Dynamics Simulation:
The fear dynamics are simulated using numerical integration. The fear element's position (fear_position) and velocity (fear_velocity) are updated over time based on the external and decision-making forces.

Visualisation:
The simulated fear dynamics are visualised through two subplots:

Forces Influencing Fear Dynamics:
External force and decision force are plotted against time. This provides insight into how these forces vary over the simulation duration.
Fear Dynamics with Decision-Making:
Fear element position is plotted against time. This plot illustrates how the fear element's position evolves over time, considering both external and decision-making forces.
Key Takeaways:
The simulation combines external and decision-making forces to model fear dynamics.
Visualisation helps understand the impact of forces on fear over time.
This is a simplified model, and specific force functions can be adjusted for more realistic simulations.
Note:
In a real-world scenario, such simulations can be more complex, considering various factors and interactions influencing fear dynamics. This model provides a foundational understanding for more intricate simulations.
DOI: 10.17632/4x8gcvzp6f.1
title: Fear Factors and decision making parameters

description: Description of Synthetic Data: Exploring Fear Factors and Decision-Making Parameters
The provided code generates synthetic data to simulate the interaction between fear factors and decision-making parameters. Let's break down the components and describe the characteristics of the generated data:

Parameters:
Fear Factors:

k_stimuli: Weight for the intensity of stimuli.
k_past: Weight for the impact of past experiences.
k_uncertainty: Weight for the degree of uncertainty.
Decision-Making Parameters:

k_utility: Weight for perceived utility.
k_risk: Weight for perceived risk.
k_reward: Weight for anticipation of reward.
Synthetic Data:
The synthetic data is generated for 100 data points for each of the following variables:

Fear Factors:

intensity_of_stimulus: Random values representing the intensity of stimuli.
impact_of_past_experiences: Random values representing the impact of past experiences.
degree_of_uncertainty: Random values representing the degree of uncertainty.
Decision-Making Parameters:

perceived_utility: Random values representing perceived utility.
perceived_risk: Random values representing perceived risk.
anticipation_of_reward: Random values representing anticipation of reward.
Calculation of Fear and Decision-Making:
Fear (fear) and Decision-Making Outcome (decision) are calculated based on the provided weights and generated synthetic data. These calculations represent a simplified model of how fear and decision-making might be influenced by various factors.

Visualisation:
The generated data is visualised using scatter plots for both fear and decision-making outcomes. Each point on the scatter plots corresponds to a data point, providing a visual representation of the relationship between fear factors and decision-making parameters.

Data Analysis:
The correlation coefficient between fear and decision-making is calculated. This coefficient quantifies the degree of linear relationship between the two variables. The resulting value provides insights into how fear and decision-making parameters are associated in the synthetic dataset.

Key Takeaway:
This synthetic dataset and analysis offer a simulated exploration into the relationships between fear factors and decision-making parameters. The visualisations and correlation coefficient provide a preliminary understanding of how these elements might interact in a simplified model.
DOI: 10.6084/m9.figshare.12495557
title: Additional file 3 of Gene coexpression network analysis and tissue-specific profiling of gene expression in jute (Corchorus capsularis L.)

description: Additional file 3: Table S3. Transcriptional genes that were identified in leaf tissues during the vegetative growth period (LVGP).
DOI: 10.6084/m9.figshare.12495557.v1
title: Additional file 3 of Gene coexpression network analysis and tissue-specific profiling of gene expression in jute (Corchorus capsularis L.)

description: Additional file 3: Table S3. Transcriptional genes that were identified in leaf tissues during the vegetative growth period (LVGP).
DOI: 10.17632/3fk4nvthmw.1
title: Supplement: Lack of association of exogenous testosterone administration with exacerbation of pre-existing hidradenitis suppurativa

description: Clinical characteristics among patients with Hidradenitis Suppurativa disease exacerbation in six months after initiating testosterone
DOI: 10.6084/m9.figshare.12495554
title: Additional file 2 of The differential expression patterns of paralogs in response to stresses indicate expression and sequence divergences

description: Additional file 2: Table S2. The interaction information of paralogs.
DOI: 10.3886/e171381
title: Data and Code for: Effects of Maturing Private School Choice Programs on Public School Students

description: Using a rich dataset that merges student-level school records with birth records, and leveraging a student fixed effects design, we explore how a Florida private school choice program affected public school students’ outcomes as the program matured and scaled up. We observe growing benefits (higher standardized test scores and lower absenteeism and suspension rates) to students attending public schools with more pre-program private school options as the program matured. Effects are particularly pronounced for lower-income students, but results are positive for more affluent students as well. Local and district-wide private school competition are both independently related to student outcomes.
description: Students born in Florida and attending Florida public schools .
DOI: 10.3886/e171381v1
title: Data and Code for: Effects of Maturing Private School Choice Programs on Public School Students

description: Using a rich dataset that merges student-level school records with birth records, and leveraging a student fixed effects design, we explore how a Florida private school choice program affected public school students’ outcomes as the program matured and scaled up. We observe growing benefits (higher standardized test scores and lower absenteeism and suspension rates) to students attending public schools with more pre-program private school options as the program matured. Effects are particularly pronounced for lower-income students, but results are positive for more affluent students as well. Local and district-wide private school competition are both independently related to student outcomes.
description: Students born in Florida and attending Florida public schools .
DOI: 10.6084/m9.figshare.12495554.v1
title: Additional file 2 of The differential expression patterns of paralogs in response to stresses indicate expression and sequence divergences

description: Additional file 2: Table S2. The interaction information of paralogs.
DOI: 10.6084/m9.figshare.12495563
title: Additional file 4 of Gene coexpression network analysis and tissue-specific profiling of gene expression in jute (Corchorus capsularis L.)

description: Additional file 4: Table S4. Transcriptional genes that were identified in leaf tissues during the flowering period (LFP).
DOI: 10.6084/m9.figshare.12495560
title: Additional file 3 of The differential expression patterns of paralogs in response to stresses indicate expression and sequence divergences

description: Additional file 3: Table S3. The interaction information of repeats.
DOI: 10.6084/m9.figshare.12495563.v1
title: Additional file 4 of Gene coexpression network analysis and tissue-specific profiling of gene expression in jute (Corchorus capsularis L.)

description: Additional file 4: Table S4. Transcriptional genes that were identified in leaf tissues during the flowering period (LFP).
DOI: 10.6084/m9.figshare.12495560.v1
title: Additional file 3 of The differential expression patterns of paralogs in response to stresses indicate expression and sequence divergences

description: Additional file 3: Table S3. The interaction information of repeats.
DOI: 10.6084/m9.figshare.13544140
title: Additional file 7 of Adaptation Mechanism of Roots to Low and High Nitrogen Revealed by Proteomic Analysis

description: Additional file 7: Table S5. Peptide information list for protein identification results.
DOI: 10.6084/m9.figshare.13544140.v1
title: Additional file 7 of Adaptation Mechanism of Roots to Low and High Nitrogen Revealed by Proteomic Analysis

description: Additional file 7: Table S5. Peptide information list for protein identification results.
DOI: 10.6084/m9.figshare.13544143
title: Additional file 8 of Adaptation Mechanism of Roots to Low and High Nitrogen Revealed by Proteomic Analysis

description: Additional file 8: Table S6. Protein information list for protein identification results.
DOI: 10.6084/m9.figshare.13544131
title: Additional file 4 of Adaptation Mechanism of Roots to Low and High Nitrogen Revealed by Proteomic Analysis

description: Additional file 4: Table S2. Proteomic experiment design.
DOI: 10.6084/m9.figshare.13544143.v1
title: Additional file 8 of Adaptation Mechanism of Roots to Low and High Nitrogen Revealed by Proteomic Analysis

description: Additional file 8: Table S6. Protein information list for protein identification results.
DOI: 10.6084/m9.figshare.13544131.v1
title: Additional file 4 of Adaptation Mechanism of Roots to Low and High Nitrogen Revealed by Proteomic Analysis

description: Additional file 4: Table S2. Proteomic experiment design.
DOI: 10.6084/m9.figshare.12495566
title: Additional file 4 of The differential expression patterns of paralogs in response to stresses indicate expression and sequence divergences

description: Additional file 4: Table S4. The list of FF, FP and PP paralogs under four different stresses.