cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25850/nioz/7b.b.dh
title: Extreme heat and drought did not affect interspecific interactions between dune grasses

description: The frequency of extreme climatic events, such as storm and heatwaves, is predicted to increase because of climate change. Understanding interactions between species in environmental extremes plays a vital role in predicting ecosystem resilience. In this study, we examined how heat and drought combined with interspecific interactions between pioneer dune builder sand couch (Elytrigia juncea) and primary foredune builder marram grass (Ammophila arenaria) affected growth and survival of the latter species in an embryonic dune system. In a 4-week field experiment, we transplanted marram grass within sand couch patches or on bare sediment. This plant interaction treatment was combined with a compound heat and drought treatment that was simulated with greenhouses that inhibited rainfall and increased temperatures (average daily maximum temperature +4°C). Results show that the presence of sand couch significantly reduced growth (i.e., formation of new shoots, shoot and root length and aboveground biomass) of marram grass. By contrast, the heat and drought treatment had no significant effects on growth or survival of marram grass, irrespective of species interactions. The neutral response suggests that even in its early establishment marram grass is highly heat and drought resistant. Since the competitive interaction between sand couch and establishing marram grass did not change under pressure of an extreme heat and drought event, we expect that these factors do not affect embryonic dune development.
DOI: 10.15468/dl.7gmtm7
title: Occurrence Download

description: A dataset containing 56174 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Maculinea arion (Linnaeus, 1758)"
 ]
}

 The dataset includes 56174 records from 785 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002501-241106120511685/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/9148608
title: Combinator Companies
DOI: 10.15468/dl.ku9gp5
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.12409 -34.291637, 149.12409 -34.298374, 149.119599 -34.298374, 149.119599 -34.305112, 149.12409 -34.305112, 149.12409 -34.307357, 149.126336 -34.307357, 149.126336 -34.305112, 149.128582 -34.305112, 149.128582 -34.298374, 149.126336 -34.298374, 149.126336 -34.291637, 149.12409 -34.291637))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060530-240626123714530/datasets/export for details.
DOI: 10.15468/dl.x363zx
title: Occurrence Download

description: A dataset containing 2600859 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Egretta garzetta (Linnaeus, 1766)",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 2600859 records from 1780 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060523-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f9n544
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phragmatobia amurensis Seitz, 1910",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 64 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0202441-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-2526/3
title: matrix2latex.m
DOI: 10.18419/darus-2526/4
title: A11_CalculateStiffness_onlyk.m

description: This script plots the effective stiffness for all participants and all trials; run this script to reproduce Figure 2 of the paper.
DOI: 10.18419/darus-2526/5
title: A2_Calculate_LinearAcceleration_y.m

description: This script calculates and saves the linear acceleration in y direction of the centre of gravity (head). It creates two figures as output (for the supine and prone case) and saves the calculated data as "linearacc_y.mat" file. 
Furthermore, it produces the first half of the latex tables Table 1 and Table 3 (linear peak accelerations) of the paper.
DOI: 10.18419/darus-2526/6
title: A8_CalculateMz.m

description: This script calculates the joint torque M_net exerted on the head-neck segment by the trunk segment at the connecting joint. This can be calculated according to equation (8) in the paper.
DOI: 10.18419/darus-2526/7
title: A10_plot_fallinghead_verticaldisp_shaded.m

description: This script plots the differences in vertical displacement for different ages and sexes; run this script to reproduce Figure 3 of the paper.
DOI: 10.15468/dl.bky5qk
title: Occurrence Download

description: A dataset containing 53 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is India",
 "TaxonKey is Batagur dhongoka (Gray, 1834)"
 ]
}

 The dataset includes 53 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007198-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-2526/8
title: A5_CalculateHeadmassAndMomentOfInertia.m

description: This script calculates and saves the headmass and the moment of inertia of all participants based on their size and height according to description given in the paper (section 5.5.).
DOI: 10.18419/darus-2526/9
title: A9_PlotInvDynamikAnalysis.m

description: This script plots the results of the inverse dynamic analysis. With the current settings, it plots Fig (6). If you want to reproduce Figure 7, you need to switch the "caseflagreproduce_figure7_exptraj" to on, and the other caseflagreproduces off; same applied to reproduce figure S1 of the supplementary material.
DOI: 10.18419/darus-2526/10
title: A12_PlotDisplacement.m

description: This scripts plots the vertical displacmement of the 'falling heads' experiment for all participants and all trials; run this script to reproduce Figure 1 of the paper.
DOI: 10.18419/darus-2526/11
title: kinematic_analysis_expdata_chinmarker.mat

description: This data file is needed to run A0_LoadTrajectories and contains the trajectory data of the chin marker.
DOI: 10.15468/dl.xpqpjv
title: Occurrence Download

description: A dataset containing 2600 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Achnatherum sibiricum (L.) Keng ex Tzvelev"
 ]
}

 The dataset includes 2600 records from 71 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007199-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-2526/12
title: A13_PlotAccelerations.m
DOI: 10.18419/darus-2526/13
title: A7_CalculateFxy.m

description: This script calculates the force acting in the sagittal x-y plane on the rigid head-neck segment. The force in x-direction can be calculated according to equation (6) in the paper, the force in y-direction according to equation (7).
DOI: 10.18419/darus-2526/14
title: A4_Calculate_RotationalAcceleration.m

description: This script calculates and saves the rotational acceleration of the centre of gravity (head). It creates two figures as output (for the supine and prone case) and saves the calculated data as "rotationalpeakacc.mat" file. 
Furthermore, it produces the second half of the latex tables Table 1 and Table 3 (rotational peak accelerations) of the paper.
DOI: 10.18419/darus-2526/15
title: A1_Calculate_LinearAcceleration_x.m

description: This script calculates and saves the linear acceleration in x direction of the centre of gravity (head). It creates two figures as output (for the supine and prone case) and saves the calculated data as "linearacc_x.mat" file.
DOI: 10.18419/darus-2526/16
title: A6_Calculate_rx_ry.m

description: This script calculates and saves the moment arms r_x and r_y of the headneck joint necessary to calculate the inverse dynamics.
DOI: 10.18419/darus-2526/17
title: A3_CalculateAngle.m

description: This script calculates the angle (rotational displacement) of the head. It creates two figures as output (for the supine and prone case) and saves the calculated data as "Angle.mat" file.
DOI: 10.18419/darus-2526
title: Postprocessing Code for 'Falling Heads': investigating reflexive responses to head-neck perturbations

description: &lt;p&gt;This dataset contains the code necessary to postprocess the experimental trajectory data of the Falling Heads experiment and perform the inverse dynamics analysis as described in the paper "’Falling Heads’: investigating reflexive responses to head-neck perturbations" by Isabell Wochner, Lennart V. Nölle, Oleksandr V. Martynenko and Syn Schmitt. Always cite the paper together with this dataset because this dataset is not self-explanatory.&lt;/p&gt; 

&lt;p&gt;The motion analyses were performed with custom software written in Matlab (Mathworks, Natick, MA) based on the recorded marker positions. From these recorded trajectories, we calculated displacements, velocities and accelerations and processed the signals with a Butterworth low-pass filter (cut-off frequency 15 Hz; fourth-order).&lt;/p&gt;
description: The folder contains 14 matlab codes, which you can and should run in their consecutive order to perform the postprocessing of the experimental trajectories (starting from A0.. up to A13). <br>
Furthermore, the matlab data files named "kinematic_analysis_expdata" contains the experimental data which is needed to run the scripts (they should be in the same folder).
DOI: 10.18419/darus-2526/18
title: A0_LoadTrajectories.m

description: This script is needed to load the experimental trajectories. Furthermore it saves the x and y displacement of the centre of gravity (head), the x-y displacement of a second node, and the x-y displacement of the chin and neck marker as "node_traj.mat" file.
DOI: 10.15468/dl.5xjz66
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pelophylax esculentus (Linnaeus, 1758)",
 "Year is 14",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007200-241107131044228/datasets/export for details.
DOI: 10.18419/darus-2978/1
title: mc_sgd_keras.py
DOI: 10.18419/darus-2978/2
title: TrainingSetup.py
DOI: 10.18419/darus-2978/3
title: tex_head.txt
DOI: 10.18419/darus-2978/4
title: README.md
DOI: 10.18419/darus-2978/5
title: show_training.py
DOI: 10.18419/darus-2978/6
title: train_star_dataset.py
DOI: 10.18419/darus-2978/7
title: plot_examples.py
DOI: 10.18419/darus-2978/8
title: utils.py
DOI: 10.18419/darus-2978/9
title: eval_nn_setups.py
DOI: 10.18419/darus-2978/10
title: custom_paths.py
DOI: 10.18419/darus-2978/11
title: tex_tail.txt
DOI: 10.18419/darus-2978/12
title: mc_training.py
DOI: 10.18419/darus-2978/13
title: eval_star_dataset.py
DOI: 10.18419/darus-2978/14
title: mc_plotting.py
DOI: 10.18419/darus-2978/15
title: LICENSE
DOI: 10.18419/darus-2978/16
title: mc_event_estimation.py
DOI: 10.18419/darus-2978/17
title: requirements.txt
DOI: 10.18419/darus-2978
title: Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent

description: This data set contains code used to generate figures and tables in our paper "Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent". The code is also available on &lt;a href=https://github.com/dholzmueller/nn_inconsistency&gt;GitHub&lt;/a&gt;. Information on the code and installation instructions can be found in the file README.md.
description: Basic instructions for installing and running the software can be found in the README.md file.
DOI: 10.18419/darus-2978/18
title: run_nn_setups.py
DOI: 10.15468/dl.m4hqm6
title: Occurrence Download

description: A dataset containing 86177 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pinus strobus L."
 ]
}

 The dataset includes 86177 records from 463 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007197-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-3135/1
title: participant4_1.mat
DOI: 10.18419/darus-3135/11
title: participant3_0.mat
DOI: 10.18419/darus-3135/14
title: participant3_1.mat
DOI: 10.18419/darus-3135/15
title: participant1_1.mat
DOI: 10.18419/darus-3135/16
title: participant1_0.mat
DOI: 10.18419/darus-3135/4
title: participant2_1.mat
DOI: 10.18419/darus-3135/6
title: participant4_0.mat
DOI: 10.18419/darus-3135/8
title: participant2_0.mat
DOI: 10.18419/darus-3135/10
title: participant7_0.mat
DOI: 10.18419/darus-3135/12
title: participant6_1.mat
DOI: 10.18419/darus-3135/13
title: participant7_1.mat
DOI: 10.18419/darus-3135/17
title: README.txt
DOI: 10.18419/darus-3135/2
title: participant8_1.mat
DOI: 10.18419/darus-3135/3
title: participant8_0.mat
DOI: 10.18419/darus-3135/5
title: participant5_1.mat
DOI: 10.18419/darus-3135/7
title: participant6_0.mat
DOI: 10.18419/darus-3135/9
title: participant5_0.mat
DOI: 10.18419/darus-3135
title: Data for "Eye Movement Analysis for Activity Recognition Using Electrooculography"

description: This dataset was recorded to investigate the problem of recognising common office activities from eye movements. The experimental scenario involved five office-based activities - copying a text, reading a printed paper, taking handwritten notes, watching a video, and browsing the Web - and periods during which participants took a rest (the NULL class). The dataset has the following characteristics:

&lt;ul&gt;
&lt;li&gt;~8 hours of eye movement data recorded using a wearable Electrooculography (EOG) system
&lt;li&gt;8 participants (2 female, 6 male), aged between 23 and 31 years
&lt;li&gt;2 experimental runs for each participant, each run involving them in a sequence of five different, randomly ordered office activities and a period of rest
&lt;li&gt;separate horizontal and vertical EOG channels, joint sampling frequency of 128Hz
&lt;li&gt;fully ground truth annotated (5 activity classes plus NULL)
&lt;/ul&gt;
description: The data is only to be used for non-commercial scientific purposes.
DOI: 10.18419/darus-3135/18
title: changelog.txt
DOI: 10.18419/darus-3445/1
title: classic_training.py

description: Cost function and training routines procedures for PyTorch QNN simulation.
DOI: 10.18419/darus-3445/2
title: generate_experiments.py

description: Experiment entry point. Generates training data, calls simulation and training routines and saves results. Also responsible for distributing experiment workloads to multiple processors.
DOI: 10.18419/darus-3445/3
title: quantum_gates.py

description: Gate implementations of common quantum gates for simulation.
DOI: 10.18419/darus-3445/4
title: logger.py

description: Logging utilities
DOI: 10.18419/darus-3445/5
title: requirements.txt

description: Python requirements for reproduction.
DOI: 10.18419/darus-3445/6
title: avg_rank_exp.py

description: Experiments for training QNNs using training data of varying Schmidt rank.
DOI: 10.18419/darus-3445/7
title: cuda_qnn.py

description: QNN ansatz implementations for classical simulation using PyTorch.
DOI: 10.18419/darus-3445/8
title: config.py

description: Configuration structures for experiments.
DOI: 10.18419/darus-3445/9
title: utils.py

description: Utility functions for data generation and experiment setup.
DOI: 10.18419/darus-3445/10
title: cost_modifying_functions.py

description: Various functions to modify the loss function after evaluation.
DOI: 10.18419/darus-3445/11
title: qnn.py

description: General QNN description
DOI: 10.18419/darus-3445/12
title: vis_utils.py

description: Utility functions for visualisation.
DOI: 10.18419/darus-3445/13
title: ortho_exp.py

description: Experiments for training QNNs using orthogonal training data.
DOI: 10.18419/darus-3445/14
title: nlihx_exp.py

description: Experiments for training QNNs using linearly dependent data.
DOI: 10.18419/darus-3445/15
title: data.py

description: Data generation routines for the experiments.
DOI: 10.18419/darus-3445/16
title: metrics.py

description: Functions for evaluating the quantum risk
DOI: 10.18419/darus-3445/17
title: plots.py

description: Python script for generating the plots for the quantum risk for all experiments and for aggregating and analyzing the raw experiment results
DOI: 10.18419/darus-3445
title: Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs
title: Constraints on the Linear Structure of the Training Data

description: &lt;p&gt;
Replication code for training Quantum Neural Networks using entangled datasets. &lt;br /&gt;This is the version of the code that was used to generate the experiment results in the related publication. For future developments and discussion see &lt;a href="https://github.com/UST-QuAntiL/entangled_qnn_training"&gt;the Github repository&lt;/a&gt;.
&lt;p/&gt;
&lt;p&gt;
&lt;b&gt;Experiments:&lt;/b&gt;&lt;br /&gt;
&lt;code&gt;avg_rank_exp.py&lt;/code&gt;: Experiments for training QNNs using training data of varying Schmidt rank&lt;br /&gt;
&lt;code&gt;nlihx_exp.py&lt;/code&gt;: Experiments for training QNNs using linearly dependent data&lt;br /&gt;
&lt;code&gt;ortho_exp.py&lt;/code&gt;: Experiments for training QNNs using orthogonal training data&lt;br /&gt;
&lt;/p&gt;

&lt;p&gt;
&lt;b&gt;Visualisation/Analysis of data (plots.py):&lt;/b&gt;&lt;br /&gt;
- Generates plots for the experiments above either from the data in &lt;code&gt;experimental_results&lt;/code&gt; or from the processed results (see Data).&lt;br /&gt;
- Processes results to extract information from raw data in &lt;code&gt;experimental_results&lt;/code&gt; (to change behavior see the function calls at the end of &lt;code&gt;plots.py&lt;/code&gt;).&lt;br /&gt;
&lt;/p&gt;
&lt;p&gt;
&lt;b&gt;Data:&lt;/b&gt;&lt;br /&gt;
The raw data for the experiments is available in &lt;a href="https://doi.org/10.18419/darus-3442"&gt;the experiment dataset&lt;/a&gt;.
&lt;/p&gt;
DOI: 10.18419/darus-3445/18
title: README.md

description: Readme file with additional information.
DOI: 10.5441/001/1.625
title: Data from: Study "Leopards, vervets, and baboons in Laikipia, Kenya" [baboons]

description: Sleeping refuges—like other important, scarce and shareable resources—can serve as hotspots for animal interaction, shaping patterns of attraction and avoidance. Where sleeping sites are shared, individuals balance the opportunity for interaction with new social partners against their need for sleep. By expanding the network of connections within animal populations, such night-time social interactions may have important, yet largely unexplored, impacts on critical behavioural and ecological processes. Here, using GPS and tri-axial accelerometry to track the movements and sleeping patterns of wild olive baboon groups (Papio anubis), we show that sharing sleeping sites disrupts sleep but appears to catalyse social tolerance and coordinated movement between groups. Individual baboons experienced shorter and more fragmented sleep when groups shared a sleeping site. After sharing sleeping sites, however, otherwise independent groups showed a strong pattern of spatial attraction, moving cohesively for up to 3 days. Our findings highlight the influence of night-time social interactions on daytime social relationships and demonstrate how a population’s reliance on, and need to share, limiting resources can drive the emergence of intergroup tolerance.
DOI: 10.18419/darus-1763/1
title: DSC08631.JPG
DOI: 10.18419/darus-1763/2
title: DSC08628.ARW
DOI: 10.18419/darus-1763/3
title: DSC08627.JPG
DOI: 10.18419/darus-1763/4
title: DSC08634.ARW
DOI: 10.15468/dl.jr3uqp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lissotriton vulgaris (Linnaeus, 1758)",
 "Year is 14",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007205-241107131044228/datasets/export for details.
DOI: 10.18419/darus-1763/5
title: DSC08625.JPG
DOI: 10.18419/darus-1763/6
title: DSC08635.JPG
DOI: 10.18419/darus-1763/7
title: DSC08631.ARW
DOI: 10.18419/darus-1763/8
title: DSC08629.ARW
DOI: 10.18419/darus-1763/9
title: DSC08633.ARW
DOI: 10.18419/darus-1763/10
title: DSC08627.ARW
DOI: 10.18419/darus-1763/11
title: DSC08630.ARW
DOI: 10.18419/darus-1763/12
title: DSC08628.JPG
DOI: 10.18419/darus-1763/13
title: DSC08637.ARW
DOI: 10.18419/darus-1763/14
title: DSC08622.ARW
DOI: 10.18419/darus-1763/15
title: DSC08625.ARW
DOI: 10.18419/darus-1763/16
title: DSC08636.ARW
DOI: 10.18419/darus-1763/17
title: DSC08634.JPG
DOI: 10.18419/darus-1763/18
title: DSC08622.JPG
DOI: 10.18419/darus-1763/19
title: DSC08629.JPG
DOI: 10.18419/darus-1763/20
title: DSC08632.JPG
DOI: 10.18419/darus-1763/21
title: DSC08626.ARW
DOI: 10.18419/darus-1763/22
title: DSC08638.ARW
DOI: 10.18419/darus-1763/23
title: DSC08626.JPG
DOI: 10.18419/darus-1763/24
title: DSC08632.ARW
DOI: 10.18419/darus-1763/25
title: DSC08630.JPG
DOI: 10.18419/darus-1763/26
title: DSC08636.JPG
DOI: 10.18419/darus-1763/27
title: DSC08635.ARW
DOI: 10.18419/darus-1763/28
title: DSC08633.JPG
DOI: 10.18419/darus-1763/29
title: DSC08638.JPG
DOI: 10.18419/darus-1763
title: 2D pictures of MÜ47-18

description: &lt;p&gt;Dataset contains all pictures (.jpg; .arw) that were taken during the &lt;a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=49056&version=DRAFT"&gt;2D digitisation strategy&lt;/a&gt; for this object.&lt;/p&gt; &lt;p&gt;Order of data: object was digitised according to a digitisation schema visible within the Dataset&lt;/p&gt;&lt;ul&gt;&lt;li&gt;characteristic view,&lt;/li&gt; &lt;li&gt;front view,&lt;/li&gt; &lt;li&gt;left view,&lt;/li&gt; &lt;li&gt;back view,&lt;/li&gt; &lt;li&gt;right view,&lt;/li&gt;&lt;li&gt;bottom view,&lt;/li&gt; &lt;li&gt;upward view, &lt;/li&gt;&lt;li&gt;details (details 1, details 2, ..., details n)&lt;/li&gt;&lt;/ul&gt;&lt;p&gt; This schema is indicated within the pictures by inventory label and then labels according to view.&lt;/p&gt;&lt;p&gt; Further information one can find in the document: Setting and workflow for basic development of 2D.&lt;/p&gt;&lt;p&gt;This dataset is part of the digital gyroscope collection. For more information about 
the collection and its creation, please see the documentation dataset:
&lt;i&gt;Niklaus, Maria, 2021, "Information about the Gyrolog data repository", 
&lt;a href="https://doi.org/10.18419/darus-821"&gt;doi: 10.18419/darus-821&lt;/a&gt;, DaRUS.&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;To refer to the whole collection: Wagner, Jörg F.; Ceranski, Beate; Fritsch, Dieter; Mammadov, Gasim; Niklaus, Maria; Schweizer, Timo; Simon, Sven; Zhan, Kun, 2021, "Digital Gyroscope Collection Created by the Project 'Gyrolog'", &lt;a href="https://doi.org/10.18419/darus-gyrolog"&gt;doi: 10.18419/darus-gyrolog&lt;/a&gt;, DaRUS.&lt;/b&gt;&lt;/p&gt;
description: rate gyro
DOI: 10.18419/darus-1763/30
title: DSC08637.JPG