cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21942/uva.24949077
title: Full 6.5-day CW measurement for Drift Detection

description: Full 6.5-day CW measurement for Drift Detection
<br>
 
<strong>Experiment:</strong><br>

-The dataset was captured using the Chipwhisperer CW308, with the target device being STM32F3 with an ARM Cortex-M4<br>
<br>
<br>
-The target device performed an AES-128 encryption while we measured the leakage traces<br>
<br>
<br>
<br>
-The experiment lasted for approximately 6.5 days <br>
<br>
<br>
<br>
-The data is organized in parts of 100k traces each. Each 100k-sized part was captured in approx. 38 minutes. Each trace has 5k time samples (features). The original experiment has a total of 254 parts of 100k traces each. <br>
<br>
<br>
-Every 100k-trace data part is called traces<strong>i</strong>.mat and comes together with label<strong>i</strong>.mat, for indexes i = 1, 2, ..., 254<br>
<br>
-The label<strong>i</strong>.mat is the value of a single sboxoutput of AES-128 i.e. the label ranges in the set {0,1,...,255}. We assume that successfully recovering the sboxoutput implies successfully recovering the respective key byte of AES-128.<br>
<br>
<br>
We also have a reduced version of the dataset available here: 
<br>
https://doi.org/10.21942/uva.25061858<br>

<br>
<br>
DOI: 10.21942/uva.24949077.v10
title: Full 6.5-day CW measurement for Drift Detection

description: Full 6.5-day CW measurement for Drift Detection
<br>
 
<strong>Experiment:</strong><br>

-The dataset was captured using the Chipwhisperer CW308, with the target device being STM32F3 with an ARM Cortex-M4<br>
<br>
<br>
-The target device performed an AES-128 encryption while we measured the leakage traces<br>
<br>
<br>
<br>
-The experiment lasted for approximately 6.5 days <br>
<br>
<br>
<br>
-The data is organized in parts of 100k traces each. Each 100k-sized part was captured in approx. 38 minutes. Each trace has 5k time samples (features). The original experiment has a total of 254 parts of 100k traces each. <br>
<br>
<br>
-Every 100k-trace data part is called traces<strong>i</strong>.mat and comes together with label<strong>i</strong>.mat, for indexes i = 1, 2, ..., 254<br>
<br>
-The label<strong>i</strong>.mat is the value of a single sboxoutput of AES-128 i.e. the label ranges in the set {0,1,...,255}. We assume that successfully recovering the sboxoutput implies successfully recovering the respective key byte of AES-128.<br>
<br>
<br>
We also have a reduced version of the dataset available here: 
<br>
https://doi.org/10.21942/uva.25061858<br>

<br>
<br>
DOI: 10.21942/uva.24949077.v9
title: Long CW measurement

description: Long Chipwhisperer measurement
DOI: 10.6084/m9.figshare.21507406.v1
title: Additional file 10 of Carbohydrates and ginsenosides in shenmai injection jointly improve hematopoietic function during chemotherapy-induced myelosuppression in mice

description: Additional file 10: Table S7. GO analysis of DEGs uniquely regulated in the S+Rg group.
DOI: 10.6084/m9.figshare.9974285.v1
title: Gene annotation

description: The .gff3 file for gene annotation.
DOI: 10.3886/e192024v1
title: Efficiency and Equilibrium Selection in an Allocation Problem

description: We study two
variants of an allocation problem where two parties lay proportional claims to
an asset, and an arbiter has a final say on allocation. The two variants we
study vary by the incentives to the arbiter. In one variant, the arbiter is
incentivized proportional to the payoff to the lowest paid claimant and in the
other, the arbiter is incentivized proportional to the payoff to the highest
paid claimant. While neither incentive scheme changes the set of equilibria,
they alter expected payoff in off-equilibrium outcomes, with implications for
equilibrium selection. Accordingly, the first variant leads to egalitarian
claims whereas the second leads to claims of the entire pot, and subsequently
to high incidence of impasse. A level-k model of bounded rationality fits the
observed outcomes. Thus, in bargaining with multiple equilibria, the level-k
model is useful in designing arbiter incentives to maximize efficiency.
DOI: 10.15151/esrf-es-1497222458
title: IBS BAG

description: The IBS BAG includes in this proposal IBS teams and external groups from CEA-Cadarache (St Paul lez Durance) and IRBA (Brétigny-sur-Orge). We study structure-function relationships of proteins emphasizing in human health, biotechnology and methodology. Most of the projects are a continuation of the MX-2398 proposal and can be grouped into different topics as follows: host-pathogen interaction, new drug targets, fundamental cellular mechanisms, membrane proteins, biotechnological applications and methodology. Some of these projects use integrative approaches (X-ray crystallography, SAXS, NMR (and cryo-EM), together with other biophysical studies) with the aim to fully understand the complexity of the biological macromolecular systems on study.
DOI: 10.3886/e192024v2
title: Efficiency and Equilibrium Selection in an Allocation Problem

description: We study two
variants of an allocation problem where two parties lay proportional claims to
an asset, and an arbiter has a final say on allocation. The two variants we
study vary by the incentives to the arbiter. In one variant, the arbiter is
incentivized proportional to the payoff to the lowest paid claimant and in the
other, the arbiter is incentivized proportional to the payoff to the highest
paid claimant. While neither incentive scheme changes the set of equilibria,
they alter expected payoff in off-equilibrium outcomes, with implications for
equilibrium selection. Accordingly, the first variant leads to egalitarian
claims whereas the second leads to claims of the entire pot, and subsequently
to high incidence of impasse. A level-k model of bounded rationality fits the
observed outcomes. Thus, in bargaining with multiple equilibria, the level-k
model is useful in designing arbiter incentives to maximize efficiency.
DOI: 10.21942/uva.25061858.v4
title: CW measurement for Drift Detection

description:  
-The dataset was captured using the Chipwhisperer CW308 with the target device being STM32F3 with an ARM Cortex-M4
<br>
-The target device performed an AES-128 encryption while we measured the traces<br>

-The experiment lasted for approximately 6.5 days <br>

-The data is organized in parts of 100k traces each. Each 100k-sized part was captured in approx. 36 minutes. Each trace has 5k time samples (features). We have a total of 254 parts of 100k traces each. 
<br>
-Every 100k-trace data part is called traces<strong>i</strong>.mat and comes together with label<strong>i</strong>.mat, for i = 1, 2, ..., 254
<br>
-The label<strong>i</strong>.mat is the value of a single sboxoutput of AES-128
<br>
 
-To limit the amout of data to process we limited the uploaded traces<strong>i</strong>.mat to subsets. We have the following sets uploaded here:
<strong>set 1:</strong> traces and labels with index i = 0 until 10
<strong>set 2: </strong>traces and labels with index i = 245 until 254
<strong>set 3: </strong>traces and labels with index i = 0, 15, 30, 45, ...., 240 = 0:15:240
DOI: 10.6084/m9.figshare.21508702.v1
title: Additional file 5 of Anthocyanin improves kidney function in diabetic kidney disease by regulating amino acid metabolism

description: Additional file 5: Table S4. (A) The identified serum metabolites under negative model in CT, DKD, and ANT groups. (B) The identified serum metabolites under positive model in CT, DKD, and ANT groups.
DOI: 10.6084/m9.figshare.21508702
title: Additional file 5 of Anthocyanin improves kidney function in diabetic kidney disease by regulating amino acid metabolism

description: Additional file 5: Table S4. (A) The identified serum metabolites under negative model in CT, DKD, and ANT groups. (B) The identified serum metabolites under positive model in CT, DKD, and ANT groups.
DOI: 10.6084/m9.figshare.21508699.v1
title: Additional file 4 of Anthocyanin improves kidney function in diabetic kidney disease by regulating amino acid metabolism

description: Additional file 4: Table S3. (A) The identified kidney metabolites under negative model in CT, DKD, and ANT groups. (B) The identified kidney metabolites under positive model in CT, DKD, and ANT groups.
DOI: 10.6084/m9.figshare.21508696
title: Additional file 3 of Anthocyanin improves kidney function in diabetic kidney disease by regulating amino acid metabolism

description: Additional file 3: Table S2. The identified serum proteins in CT, DKD, and ANT groups.
DOI: 10.6084/m9.figshare.21508699
title: Additional file 4 of Anthocyanin improves kidney function in diabetic kidney disease by regulating amino acid metabolism

description: Additional file 4: Table S3. (A) The identified kidney metabolites under negative model in CT, DKD, and ANT groups. (B) The identified kidney metabolites under positive model in CT, DKD, and ANT groups.
DOI: 10.3886/e192521v1
title: Partitioned Promises in a Trust Game

description: A workable dataset on the project, named Partitioned Promises in a Trust Game
DOI: 10.5519/qd.z5yvp6yl
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (371504 records)
DOI: 10.6084/m9.figshare.21508693.v1
title: Additional file 2 of Anthocyanin improves kidney function in diabetic kidney disease by regulating amino acid metabolism

description: Additional file 2: Table S1. The identified kidney proteins in CT, DKD, and ANT groups.
DOI: 10.5281/zenodo.10125579
title: Community Package Lasry Dataset

description: The Community package (https://github.com/SoloveyMaria/community) is an R package designed for the analysis of single-cell RNA sequencing data, specifically for inferring interactions between different cell types. The dataset provided here is compatible with the Community tool, allowing for direct utilization. 
The dataset associated with this research has undergone peer review and has been published in the journal Nature Cancer. The publication can be accessed via the following link: https://doi.org/10.1038/s43018-022-00480-0. For access to the raw data, please visit: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE185381. It's important to note that the data in this repository has undergone batch correction and normalization, and the corresponding metadata has been appropriately adjusted. This processed data serves as the input for the Community tool.
DOI: 10.5281/zenodo.10125580
title: Community Package Lasry Dataset

description: The Community package (https://github.com/SoloveyMaria/community) is an R package designed for the analysis of single-cell RNA sequencing data, specifically for inferring interactions between different cell types. The dataset provided here is compatible with the Community tool, allowing for direct utilization. 
The dataset associated with this research has undergone peer review and has been published in the journal Nature Cancer. The publication can be accessed via the following link: https://doi.org/10.1038/s43018-022-00480-0. For access to the raw data, please visit: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE185381. It's important to note that the data in this repository has undergone batch correction and normalization, and the corresponding metadata has been appropriately adjusted. This processed data serves as the input for the Community tool.
DOI: 10.5281/zenodo.10513005
title: Community Package Lasry Dataset

description: The Community package (https://github.com/SoloveyMaria/community) is an R package designed for the analysis of single-cell RNA sequencing data, specifically for inferring interactions between different cell types. The dataset provided here is compatible with the Community tool, allowing for direct utilization. 
The dataset associated with this research has undergone peer review and has been published in the journal Nature Cancer. The publication can be accessed via the following link: https://doi.org/10.1038/s43018-022-00480-0. For access to the raw data, please visit: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE185381. It's important to note that the data in this repository has undergone batch correction and normalization, and the corresponding metadata has been appropriately adjusted. This processed data serves as the input for the Community tool.
DOI: 10.5281/zenodo.10619771
title: Community Package Lasry Dataset

description: The Community package (https://github.com/SoloveyMaria/community) is an R package designed for the analysis of single-cell RNA sequencing data, specifically for inferring interactions between different cell types. The dataset provided here is compatible with the Community tool, allowing for direct utilization. 
The dataset associated with this research has undergone peer review and has been published in the journal Nature Cancer. The publication can be accessed via the following link: https://doi.org/10.1038/s43018-022-00480-0. For access to the raw data, please visit: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE185381. It's important to note that the data in this repository has undergone batch correction and normalization, and the corresponding metadata has been appropriately adjusted. This processed data serves as the input for the Community tool.
DOI: 10.6084/m9.figshare.21507406
title: Additional file 10 of Carbohydrates and ginsenosides in shenmai injection jointly improve hematopoietic function during chemotherapy-induced myelosuppression in mice

description: Additional file 10: Table S7. GO analysis of DEGs uniquely regulated in the S+Rg group.
DOI: 10.6084/m9.figshare.21508696.v1
title: Additional file 3 of Anthocyanin improves kidney function in diabetic kidney disease by regulating amino acid metabolism

description: Additional file 3: Table S2. The identified serum proteins in CT, DKD, and ANT groups.
DOI: 10.6084/m9.figshare.19936183
title: Temperature and atmospheric pressure may be considered as predictors for the occurrence of bacillary dysentery in Guangzhou, Southern China

description: Introduction The control of bacillary dysentery (BD) remains a big challenge for China. Methods Negative binomial multivariable regression was used to study relationships between meteorological variables and the occurrence of BD during the period of 2006-2012. Results Each 1°C rise of temperature corresponded to an increase of 3.60% (95%CI, 3.03% to 4.18%) in the monthly number of BD cases, whereas a 1 hPa rise in atmospheric pressure corresponded to a decrease in the number of BD cases by 2.85% (95%CI = 3.34% to 2.37% decrease). Conclusions Temperature and atmospheric pressure may be considered as predictors for the occurrence of BD in Guangzhou.
DOI: 10.6084/m9.figshare.19936183.v1
title: Temperature and atmospheric pressure may be considered as predictors for the occurrence of bacillary dysentery in Guangzhou, Southern China

description: Introduction The control of bacillary dysentery (BD) remains a big challenge for China. Methods Negative binomial multivariable regression was used to study relationships between meteorological variables and the occurrence of BD during the period of 2006-2012. Results Each 1°C rise of temperature corresponded to an increase of 3.60% (95%CI, 3.03% to 4.18%) in the monthly number of BD cases, whereas a 1 hPa rise in atmospheric pressure corresponded to a decrease in the number of BD cases by 2.85% (95%CI = 3.34% to 2.37% decrease). Conclusions Temperature and atmospheric pressure may be considered as predictors for the occurrence of BD in Guangzhou.
DOI: 10.6084/m9.figshare.21507409.v1
title: Additional file 11 of Carbohydrates and ginsenosides in shenmai injection jointly improve hematopoietic function during chemotherapy-induced myelosuppression in mice

description: Additional file 11: Table S8. KEGG pathway analysis of Model, S+Rg, S, and Rg groups.
DOI: 10.6084/m9.figshare.13120611
title: Additional file 2 of High throughput single-cell detection of multiplex CRISPR-edited gene modifications

description: Table S1. Sequences of sgRNAs used in this study. (XLSX 35 kb)
DOI: 10.6084/m9.figshare.13120617
title: Additional file 3 of High throughput single-cell detection of multiplex CRISPR-edited gene modifications

description: Table S2. Primers used for CRISPR-seq bulk NGS. (XLSX 49 kb)
DOI: 10.6084/m9.figshare.13120626
title: Additional file 6 of High throughput single-cell detection of multiplex CRISPR-edited gene modifications

description: Table S5. Primer sequences and genomic locations of amplified regions by Mission Bio Tapestri. (XLSX 62 kb)
DOI: 10.6084/m9.figshare.21507433
title: Additional file 7 of Carbohydrates and ginsenosides in shenmai injection jointly improve hematopoietic function during chemotherapy-induced myelosuppression in mice

description: Additional file 7: Table S4. Common DEGs in S and S+Rg groups.
DOI: 10.6084/m9.figshare.13120611.v1
title: Additional file 2 of High throughput single-cell detection of multiplex CRISPR-edited gene modifications

description: Table S1. Sequences of sgRNAs used in this study. (XLSX 35 kb)
DOI: 10.6084/m9.figshare.13120620.v1
title: Additional file 4 of High throughput single-cell detection of multiplex CRISPR-edited gene modifications

description: Table S3. Sequences of primers used for sgRNA amplification by Fluidigm Biomark. (XLSX 9 kb)
DOI: 10.6084/m9.figshare.21507436
title: Additional file 8 of Carbohydrates and ginsenosides in shenmai injection jointly improve hematopoietic function during chemotherapy-induced myelosuppression in mice

description: Additional file 8: Table S5. DEGs uniquely regulated in the S+Rg group.
DOI: 10.6084/m9.figshare.13120623
title: Additional file 5 of High throughput single-cell detection of multiplex CRISPR-edited gene modifications

description: Table S4. On and off-target genomic loci analyzed in this study. (XLSX 75 kb)
DOI: 10.6084/m9.figshare.13120623.v1
title: Additional file 5 of High throughput single-cell detection of multiplex CRISPR-edited gene modifications

description: Table S4. On and off-target genomic loci analyzed in this study. (XLSX 75 kb)
DOI: 10.57754/fdat.98546-hfv72
title: Experimental data and modelling code for bedrock incision and channelization by dry rockfall

description: This repository contains two folders, relating to laboratory data and a cellular model described in the following paper:
Beer, A.R., Fischer, J.N., Ulizio, T.P., Ma, Z., Sun, Z., and Lamb, M.P., 2024. A mechanistic model and experiments on bedrock incision and channelization by rockfall. Journal of Geophysical Research: Earth Surface, doi:10.1029/2023JF007504
DOI: 10.21942/uva.25061858
title: Reduced CW measurement for Drift Detection

description:  
<strong>Experiment:</strong><br>

-The dataset was captured using the Chipwhisperer CW308, with the target device being STM32F3 with an ARM Cortex-M4<br>
<br>
-The target device performed an AES-128 encryption while we measured the leakage traces<br>
<br>
-The experiment lasted for approximately 6.5 days <br>
<br>
-The data is organized in parts of 100k traces each. Each 100k-sized part was captured in approx. 38 minutes. Each trace has 5k time samples (features). The original experiment has a total of 254 parts of 100k traces each. <br>
<br>
-Every 100k-trace data part is called traces<strong>i</strong>.mat and comes together with label<strong>i</strong>.mat, for indexes i = 1, 2, ..., 254<br>
 <br>
-The label<strong>i</strong>.mat is the value of a single sboxoutput of AES-128 i.e. the label ranges in the set {0,1,...,255}. We assume that successfully recovering the sboxoutput implies successfully recovering the respective key byte of AES-128.<br>
<br>
-To limit the amout of data to process, we limited the uploaded traces<strong>i</strong>.mat to certain subsets of the original experiment (denotes as Sets 1, 2, 3). The sets are described here:<br>
<br>
<strong>Set 1: </strong>traces<strong>i</strong>.mat and label<strong>i</strong>.mat with index i = 0 until 10 <br>
Set 1 contains the data that is close to the beginning of the the experiment<br>
(approximately the first 7 hours of the experiment)<br>

<strong>Set 2: </strong>traces<strong>i</strong>.mat and label<strong>i</strong>.mat with index i = 245 until 254<br>
Set 2 contains the data that is close to the end of the the experiment<br>
(approximately the last 6.3 hours of the experiment)<br>
<br>
<br>
<strong>Set 3: </strong>traces<strong>i</strong>.mat  and label<strong>i</strong>.mat with index i = 0, 15, 30, 45, ...., 240 = 0:15:240<br>
Set 3 contains data through the whole 6.5-day experiment in steps of approximately 10 hours. That is, traces0.mat and label0.mat are observed at hour 0. Then, traces15.mat and label15.mat are observed at hour 10. Then, traces30.mat and label30.mat are observed at hour 20 etc.<br>
<br>
<br>
The full 6.5-day experiment that contains all tracesi.mat and all labelsi.mat for index i = 0,1,2,...,254 is available here (210 GBytes):<br>

https://doi.org/10.21942/uva.24949077
DOI: 10.21942/uva.25061858.v6
title: Reduced CW measurement for Drift Detection

description:  
<strong>Experiment:</strong><br>

-The dataset was captured using the Chipwhisperer CW308, with the target device being STM32F3 with an ARM Cortex-M4<br>
<br>
-The target device performed an AES-128 encryption while we measured the leakage traces<br>
<br>
-The experiment lasted for approximately 6.5 days <br>
<br>
-The data is organized in parts of 100k traces each. Each 100k-sized part was captured in approx. 38 minutes. Each trace has 5k time samples (features). The original experiment has a total of 254 parts of 100k traces each. <br>
<br>
-Every 100k-trace data part is called traces<strong>i</strong>.mat and comes together with label<strong>i</strong>.mat, for indexes i = 1, 2, ..., 254<br>
 <br>
-The label<strong>i</strong>.mat is the value of a single sboxoutput of AES-128 i.e. the label ranges in the set {0,1,...,255}. We assume that successfully recovering the sboxoutput implies successfully recovering the respective key byte of AES-128.<br>
<br>
-To limit the amout of data to process, we limited the uploaded traces<strong>i</strong>.mat to certain subsets of the original experiment (denotes as Sets 1, 2, 3). The sets are described here:<br>
<br>
<strong>Set 1: </strong>traces<strong>i</strong>.mat and label<strong>i</strong>.mat with index i = 0 until 10 <br>
Set 1 contains the data that is close to the beginning of the the experiment<br>
(approximately the first 7 hours of the experiment)<br>

<strong>Set 2: </strong>traces<strong>i</strong>.mat and label<strong>i</strong>.mat with index i = 245 until 254<br>
Set 2 contains the data that is close to the end of the the experiment<br>
(approximately the last 6.3 hours of the experiment)<br>
<br>
<br>
<strong>Set 3: </strong>traces<strong>i</strong>.mat  and label<strong>i</strong>.mat with index i = 0, 15, 30, 45, ...., 240 = 0:15:240<br>
Set 3 contains data through the whole 6.5-day experiment in steps of approximately 10 hours. That is, traces0.mat and label0.mat are observed at hour 0. Then, traces15.mat and label15.mat are observed at hour 10. Then, traces30.mat and label30.mat are observed at hour 20 etc.<br>
<br>
<br>
The full 6.5-day experiment that contains all tracesi.mat and all labelsi.mat for index i = 0,1,2,...,254 is available here (210 GBytes):<br>

https://doi.org/10.21942/uva.24949077