cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10055337
title: LOGÍSTICA REVERSA DE EMBALAGENS DE DEFENSIVOS AGRÍCOLAS: UM CAMINHO PARA O DESENVOLVIMENTO SUSTENTÁVEL

description: O meio ambiente tem ao longo do tempo sofrido grandes impactos e a maior parte destes oriundos da ação humana. Por várias vezes, diversos países se reuniram com o objetivo de encontrar alternativas para que as produções acontecessem sem impactar o meio ambiente, surgindo nesse contexto a necessidade de promoção do desenvolvimento sustentável. O descarte das embalagens de defensivos agrícolas sempre foi um fator de preocupação devido aos seus problemas de contaminação ambiental e humana, sendo assim foi criada a Lei 9.974/2000 e Decreto 4.074/2002 como forma de orientar o correto descarte dessas embalagens. Ainda como forma de intensificar a necessidade do descarte correto, o Brasil cria em 2010 a Lei nº 12.305 sobre a Política Nacional dos Resíduos Sólidos (PNRS), que determina diretrizes para o descarte correto dos resíduos. Visando a necessidade do descarte correto das embalagens de defensivos agrícolas, em 2002, no Brasil, criou-se também o Instituto Nacional de Processamento de Embalagens Vazias (inpEV), que desenvolve o processo de Logística Reversa, o qual tem apresentado importantes resultados que mostram sua ecoeficiência. O objetivo deste estudo é evidenciar a logística reversa de embalagens vazias de defensivos agrícolas realizadas no Brasil, pontuando os aspectos sustentáveis dessa prática. A metodologia usada foi de Revisão de Literatura, em que foi abrangido o tema, definida as palavras-chave, realizada a seleção dos estudos que fundamentaram a estrutura deste trabalho. Os resultados encontrados apontam a logítica reversa das embalagens vazias de defensivos agrícolas como uma perspectiva forte para a promoção do desenvolvimento sustentável, no entanto, existem estudos que evidenciam que algumas embalagens ainda são descartadas de forma incorreta, especialmente em agriculturas familiares.
DOI: 10.5281/zenodo.10055336
title: LOGÍSTICA REVERSA DE EMBALAGENS DE DEFENSIVOS AGRÍCOLAS: UM CAMINHO PARA O DESENVOLVIMENTO SUSTENTÁVEL

description: O meio ambiente tem ao longo do tempo sofrido grandes impactos e a maior parte destes oriundos da ação humana. Por várias vezes, diversos países se reuniram com o objetivo de encontrar alternativas para que as produções acontecessem sem impactar o meio ambiente, surgindo nesse contexto a necessidade de promoção do desenvolvimento sustentável. O descarte das embalagens de defensivos agrícolas sempre foi um fator de preocupação devido aos seus problemas de contaminação ambiental e humana, sendo assim foi criada a Lei 9.974/2000 e Decreto 4.074/2002 como forma de orientar o correto descarte dessas embalagens. Ainda como forma de intensificar a necessidade do descarte correto, o Brasil cria em 2010 a Lei nº 12.305 sobre a Política Nacional dos Resíduos Sólidos (PNRS), que determina diretrizes para o descarte correto dos resíduos. Visando a necessidade do descarte correto das embalagens de defensivos agrícolas, em 2002, no Brasil, criou-se também o Instituto Nacional de Processamento de Embalagens Vazias (inpEV), que desenvolve o processo de Logística Reversa, o qual tem apresentado importantes resultados que mostram sua ecoeficiência. O objetivo deste estudo é evidenciar a logística reversa de embalagens vazias de defensivos agrícolas realizadas no Brasil, pontuando os aspectos sustentáveis dessa prática. A metodologia usada foi de Revisão de Literatura, em que foi abrangido o tema, definida as palavras-chave, realizada a seleção dos estudos que fundamentaram a estrutura deste trabalho. Os resultados encontrados apontam a logítica reversa das embalagens vazias de defensivos agrícolas como uma perspectiva forte para a promoção do desenvolvimento sustentável, no entanto, existem estudos que evidenciam que algumas embalagens ainda são descartadas de forma incorreta, especialmente em agriculturas familiares.
DOI: 10.5281/zenodo.10055383
title: ProtoMet Innovative Training Network GA n° 813873 (UniTN)

description: Data collected during Anju's doctoral thesis work through the ITN program ProtoMet
DOI: 10.5281/zenodo.10055384
title: ProtoMet Innovative Training Network GA n° 813873 (UniTN)

description: Data collected during Anju's doctoral thesis work through the ITN program ProtoMet
DOI: 10.5522/04/24428044
title: Generalized Machine Learning Equalization in Coherent Receivers

description: Dataset underpinning manuscript "Generalized Machine Learning Equalization in Coherent Receivers". <br> "graph_data.xlsx" is an excel spreadsheet containing the graph data. There are two sheets, "Launch Power" which contains the data in Fig 2a, and "Dispersion" containing the data in Fig 2b. In each sheet, the first column is the X axis and further columns are the Y values. <br> "train_data.pt" and "val_data.pt" contain the training and validation data used in this paper (testing data was generated at test time). Both were generated in Pytorch 2.1, and can be accessed using the built in "torch.load" function supplied by Pytorch. Each file contains a list of dictionaries with keys "rx_training_sequece", "rx_data_sequence", and "tx_data_symbols", denoting the received training sequence, received data sequence, and transmitted symbols respectively, with the corresponding values being complex tensors. <br> "training_sequence.pt" contains the pulse-shaped training sequence, generated in Pytorch 2.1 and can be accessed in the same way as above, containing a complex tensor.
DOI: 10.5522/04/24428044.v1
title: Generalized Machine Learning Equalization in Coherent Receivers

description: Dataset underpinning manuscript "Generalized Machine Learning Equalization in Coherent Receivers". <br> "graph_data.xlsx" is an excel spreadsheet containing the graph data. There are two sheets, "Launch Power" which contains the data in Fig 2a, and "Dispersion" containing the data in Fig 2b. In each sheet, the first column is the X axis and further columns are the Y values. <br> "train_data.pt" and "val_data.pt" contain the training and validation data used in this paper (testing data was generated at test time). Both were generated in Pytorch 2.1, and can be accessed using the built in "torch.load" function supplied by Pytorch. Each file contains a list of dictionaries with keys "rx_training_sequece", "rx_data_sequence", and "tx_data_symbols", denoting the received training sequence, received data sequence, and transmitted symbols respectively, with the corresponding values being complex tensors. <br> "training_sequence.pt" contains the pulse-shaped training sequence, generated in Pytorch 2.1 and can be accessed in the same way as above, containing a complex tensor.
DOI: 10.5281/zenodo.10055426
title: BIOLOGIK FAOL MODDALAR SINTEZI VA TIBBIYOTDA QO'LLANILISHI
DOI: 10.5281/zenodo.10055425
title: BIOLOGIK FAOL MODDALAR SINTEZI VA TIBBIYOTDA QO'LLANILISHI
DOI: 10.5281/zenodo.10055380
title: Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence.

description: Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence. 

SPSS DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).
EXCEL DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).
Data of Project factorial.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).
Data Project reliability.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).
FIGURES. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (Figure 1.jpeg, Figure 2.jpeg, Figure 3 and Figure 4.jpeg).
DOI: 10.5281/zenodo.7973260
title: Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence.

description: <strong>Data availability</strong>. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence. SPSS DATA. The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15). (spss data.sav) FIGURES. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (Figure 1.jpeg, Figure 3.jpeg)
DOI: 10.5281/zenodo.7973379
title: Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence.

description: <strong>Data availability</strong>. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence. SPSS DATA. The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15). (spss data.sav) FIGURES. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (Figure 1.jpeg, Figure 2 and Figure 3.jpeg)
DOI: 10.5281/zenodo.7971327
title: Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence.

description: Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence. 

SPSS DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).
EXCEL DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).
Data of Project factorial.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).
Data Project reliability.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).
FIGURES. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (Figure 1.jpeg, Figure 2.jpeg, Figure 3 and Figure 4.jpeg).
DOI: 10.5281/zenodo.8005514
title: Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence.

description: <strong>Data availability</strong>. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence. SPSS DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15). EXCEL DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15). Data of Project factorial.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel). Data Project reliability.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel). FIGURES. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (Figure 1.jpeg, Figure 2.jpeg and Figure 3.jpeg).
DOI: 10.5281/zenodo.8075442
title: Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence.

description: <strong>Data availability</strong>. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence. SPSS DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15). EXCEL DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15). Data of Project factorial.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel). Data Project reliability.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel). FIGURES. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (Figure 1.jpeg, Figure 2.jpeg and Figure 3.jpeg).
DOI: 10.18112/openneuro.ds003216.v3.0.11
title: Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset
DOI: 10.3886/e194562
title: Cluster Randomized Pragmatic Clinical Trial Testing Behavioral Economic Implementation Strategies to Improve Tobacco Treatment for Patients With Cancer Who Smoke

description: <b>PURPOSE</b>Few cancer centers systematically engage patients with evidence-based tobacco treatment despite its positive effect on quality of life and survival. Implementation strategies directed at patients, clinicians, or both may increase tobacco use treatment (TUT) within oncology.<br><br><b>METHODS</b>We conducted a four-arm cluster-randomized pragmatic trial across 11 clinical sites comparing the effect of strategies informed by behavioral economics on TUT engagement during oncology encounters with cancer patients. We delivered electronic health record (EHR)–based nudges promoting TUT across four nudge conditions: patient only, clinician only, patient and clinician, or usual care. Nudges were designed to counteract cognitive biases that reduce TUT engagement. The primary outcome was TUT penetration, defined as the proportion of patients with documented TUT referral or a medication prescription in the EHR. Generalized estimating equations were used to estimate the parameters of a linear model.<br><br><b>RESULTS</b>From June 2021 to July 2022, we randomly assigned 246 clinicians in 95 clusters, and collected TUT penetration data from their encounters with 2,146 eligible patients who smoke receiving oncologic care. Intent-to-treat (ITT) analysis showed that the clinician nudge led to a significant increase in TUT penetration versus usual care (35.6% v 13.5%; OR = 3.64; 95% CI, 2.52 to 5.24; P&lt; .0001). Completer-only analysis (N = 1,795) showed similar impact (37.7% clinician nudge v 13.5% usual care; OR = 3.77; 95% CI, 2.73 to 5.19; P &lt; .0001). Clinician type affected TUT penetration, with physicians less likely to provide TUT than advanced practice providers (ITT OR = 0.67; 95% CI, 0.51 to 0.88; P = .004).<br><br><b>CONCLUSION</b>EHR nudges, informed by behavioral economics and aimed at oncology clinicians, appear to substantially increase TUT penetration. Adding patient nudges to the implementation strategy did not affect TUT penetration rates.
DOI: 10.3886/e194562v1
title: Cluster Randomized Pragmatic Clinical Trial Testing Behavioral Economic Implementation Strategies to Improve Tobacco Treatment for Patients With Cancer Who Smoke

description: <b>PURPOSE</b>Few cancer centers systematically engage patients with evidence-based tobacco treatment despite its positive effect on quality of life and survival. Implementation strategies directed at patients, clinicians, or both may increase tobacco use treatment (TUT) within oncology.<br><br><b>METHODS</b>We conducted a four-arm cluster-randomized pragmatic trial across 11 clinical sites comparing the effect of strategies informed by behavioral economics on TUT engagement during oncology encounters with cancer patients. We delivered electronic health record (EHR)–based nudges promoting TUT across four nudge conditions: patient only, clinician only, patient and clinician, or usual care. Nudges were designed to counteract cognitive biases that reduce TUT engagement. The primary outcome was TUT penetration, defined as the proportion of patients with documented TUT referral or a medication prescription in the EHR. Generalized estimating equations were used to estimate the parameters of a linear model.<br><br><b>RESULTS</b>From June 2021 to July 2022, we randomly assigned 246 clinicians in 95 clusters, and collected TUT penetration data from their encounters with 2,146 eligible patients who smoke receiving oncologic care. Intent-to-treat (ITT) analysis showed that the clinician nudge led to a significant increase in TUT penetration versus usual care (35.6% v 13.5%; OR = 3.64; 95% CI, 2.52 to 5.24; P&lt; .0001). Completer-only analysis (N = 1,795) showed similar impact (37.7% clinician nudge v 13.5% usual care; OR = 3.77; 95% CI, 2.73 to 5.19; P &lt; .0001). Clinician type affected TUT penetration, with physicians less likely to provide TUT than advanced practice providers (ITT OR = 0.67; 95% CI, 0.51 to 0.88; P = .004).<br><br><b>CONCLUSION</b>EHR nudges, informed by behavioral economics and aimed at oncology clinicians, appear to substantially increase TUT penetration. Adding patient nudges to the implementation strategy did not affect TUT penetration rates.
DOI: 10.6084/m9.figshare.24461143
title: <b>​sheep</b>

description: 62 local sheep in the northeastern Tarim Basin
DOI: 10.6084/m9.figshare.24461143.v1
title: <b>​sheep</b>

description: 62 local sheep in the northeastern Tarim Basin
DOI: 10.5281/zenodo.10055272
title: Structural role for DNA ligase IV in promoting the fidelity of non-homologous end joining

description: This repository contains the original, uncropped .tif files for gel images in Stinson et al., Nature Communications (2023). File names in the repository correspond to figure panels in the published manuscript.
DOI: 10.5281/zenodo.10055271
title: Structural role for DNA ligase IV in promoting the fidelity of non-homologous end joining

description: This repository contains the original, uncropped .tif files for gel images in Stinson et al., Nature Communications (2023). File names in the repository correspond to figure panels in the published manuscript.
DOI: 10.17909/xw7n-9730
title: CEERS data for Keypaper VIII

description: Includes NIRSpec and NIRCam WFSS galaxies from CEERS surveys.
DOI: 10.21227/mzny-8c77
title: BVI-Lowlight: Fully registered datasets for low-light image and video enhancement

description: Low-light images and video footage often exhibit issues due to the interplay of various parameters such as aperture, shutter speed, and ISO settings. These interactions can lead to distortions, especially in extreme lighting conditions. This distortion is primarily caused by the inverse relationship between decreasing light intensity and increasing photon noise, which gets amplified with higher sensor gain. Additionally, secondary characteristics like white balance and color effects can also be adversely affected and may require post-processing correction. These distortions not only impact the perceived quality of the images but also pose significant challenges for machine learning tasks, including classification and object detection. This is particularly evident when considering the susceptibility of deep learning networks to adversarial examples.The BVI-Lowlight datasets offer fully registered low-light content alongside their corresponding clean and normal light condition ones. This dataset includes both images and videos, enabling the use of supervised learning approaches and performance evaluation through objective metrics such as PSNR and SSIM.
DOI: 10.6084/m9.figshare.24461536
title: burgers.npz

description: burgers.npz
DOI: 10.6084/m9.figshare.24050244
title: <b>Supplemental Material for Manuscript Title: </b>Deletion of the Aryl Hydrocarbon Receptor in Endothelial Cells Improves Ischemic Angiogenesis in Chronic Kidney Disease.

description: Supplemental material for paper entitled: <b>Deletion of the Aryl Hydrocarbon Receptor in Endothelial Cells Improves Ischemic Angiogenesis in Chronic Kidney Disease</b><br>Item Descriptions:1) Source Data file2) Supplemental Figures3) Supplemental Dataset #1
DOI: 10.6084/m9.figshare.24050244.v1
title: <b>Activation of the Aryl Hydrocarbon Receptor in Endothelial Cells Impairs Ischemic Angiogenesis in Chronic Kidney Disease</b>

description: Source Data for manuscript entitled: <b>Activation of the Aryl Hydrocarbon Receptor in Endothelial Cells Impairs Ischemic Angiogenesis in Chronic Kidney Disease</b>
DOI: 10.18112/openneuro.ds004662.v1.0.1
title: Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA
DOI: 10.5066/p9xrpcsc
title: Formation tops data from the stratigraphic cross sections of the Lewis Shale in the eastern part of the Southwestern Wyoming Province, Wyoming and Colorado

description: This data release contains data associated with the USGS SIM publication "Stratigraphic Cross Sections of the Lewis Shale in the eastern part of the Southwestern Wyoming Province, Wyoming and Colorado". North-to-southeast (A?A?), west-to-east (B?B?), and southwest-to-northeast (C?C?) cross sections were created from 105 wells throughout the eastern part of the Southwestern Wyoming Province. The cross sections highlight 15 clinothems within the Lewis Shale, Fox Hills Sandstone, and Lance Formation progradational system. Data include well information and depths to key stratigraphic surfaces for 105 wells within the three cross sections. Well information includes the cross section identification, API #, well name, latitude in decimals, longitude in decimals, projection datum, county, state, Kelly Bushing elevation in feet, section, township, and range. Wells 19*, 26*, 69* and 70* are intersection points between the three sections. The stratigraphic data are all listed in measured depth relative to the Kelly bushing elevation and include the depth to the top of the Almond Formation, depth to the top and base of the informal Asquith marker, depth to the tops of 15 flooding surfaces which define the series of clinothems within the Lewis Shale, depth the top of the Lewis Shale, and depth to the top of the Fox Hills Sandstone.
DOI: 10.18112/openneuro.ds004662.v1.1.0
title: Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA
DOI: 10.6084/m9.figshare.24282517
title: Data from: Salinization, warming, and loss of water clarity inhibit vertical mixing of small urban ponds

description: Code and data for characterizing water column mixing regimes in ponds and comparing the influence of maximum salinity, surface temperature, Secchi depth, maximum depth, and surface area by generalized linear regression.Pond_mixing_regime_profile_data.csv contains depth profile data with rows corresponding to unique entries and columns corresponding to Site (sampling location name), Date (sampling date), Time (sampling time), Event (sampling month), Parameter (water quality parameter), Units (water quality parameter unit), Depth (measurement depth), and Value (measurement value).Pond_mixing_regime_site_data.csv contains site data with rows corresponding to sampling locations with columns corresponding to Site (sampling location name), Sample_Description (sampling location name distinguishing location), Latitude (sampling location latitude), Longitude (sampling location longitude), Subwatershed (sampling location subwatershed), D-Chloride (dissolved chloride concentration), T-Sodium (total sodium concentration), T-Phosphorus (total phosphorus concentration), Surface.area (sampling location surface area in m2).Code presented in Pond_mixing_regime_R_code.R.Pond_mixing_regime_Metadata contains additional details on the dataset.<br>