cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20459244
title: Data for all block level analysis plots (Fig. 2B,C, Fig. 3A-D, Fig. S5A,B, Fig.S6C-F)

description: The structure contains data used for generating Fig.2b,c, Fig. S5a,b, Fig. S6c-f. Fig.3a-d. The “.mat” file contains a cell structure with two Variables. Blk.Vec contains the values for each individual learning blocks after the TUS, while Blk.Vec0 contains similar values but for the base line and before the TUS application. <br> Each of the two variables mentioned above is a chain of 4 cell structures containing 5 different parameters. The structure is: <br> Blk.Vec {1,Parameter} {1,Monkey} {Motivational_context,TUS_Condition} {1,Cognitive_Load} Blk.Vec0 {1,Parameter} {1,Monkey} {Motivational_context,TUS_Condition} {1,Cognitive_Load} <br> Cell parameters are: <br> “Parameter”: 1,2,3. “Trial to criterion”,”information sampling”, and “Post_Learning Accuracy”. “Monkey”: 1,2,3. “Monkey W”, “Monkey I”, both monkeys. “Motivational_context”: 1,2. “Gain-Loss”, “Gain-only” “TUS_Condition”: 1,2,3,4. “Sham-ACC”, “TUS-ACC”, “Sham-STR”, “TUS-STR”. “Cognitive_Load”: 1,2,3.
DOI: 10.6084/m9.figshare.20459244.v2
title: Data for all block level analysis plots (Fig. 2B,C, Fig. 3A-D, Fig. S5A,B, Fig.S6C-F)

description: The structure contains data used for generating Fig.2b,c, Fig. S5a,b, Fig. S6c-f. Fig.3a-d. The “.mat” file contains a cell structure with two Variables. Blk.Vec contains the values for each individual learning blocks after the TUS, while Blk.Vec0 contains similar values but for the base line and before the TUS application. <br> Each of the two variables mentioned above is a chain of 4 cell structures containing 5 different parameters. The structure is: <br> Blk.Vec {1,Parameter} {1,Monkey} {Motivational_context,TUS_Condition} {1,Cognitive_Load} Blk.Vec0 {1,Parameter} {1,Monkey} {Motivational_context,TUS_Condition} {1,Cognitive_Load} <br> Cell parameters are: <br> “Parameter”: 1,2,3. “Trial to criterion”,”information sampling”, and “Post_Learning Accuracy”. “Monkey”: 1,2,3. “Monkey W”, “Monkey I”, both monkeys. “Motivational_context”: 1,2. “Gain-Loss”, “Gain-only” “TUS_Condition”: 1,2,3,4. “Sham-ACC”, “TUS-ACC”, “Sham-STR”, “TUS-STR”. “Cognitive_Load”: 1,2,3.
DOI: 10.6084/m9.figshare.20459244.v1
title: Data for all block level analysis plots (e.g. Fig. 2B,C, Fig. 3A-D)

description: The structure contains data used for generating Fig.2b,c, Fig. S5a,b, Fig. S6c-f. Fig.3a-d. The “.mat” file contains a cell structure with two Variables. Blk.Vec contains the values for each individual learning blocks after the TUS, while Blk.Vec0 contains similar values but for the base line and before the TUS application. <br> Each of the two variables mentioned above is a chain of 4 cell structures containing 5 different parameters. The structure is: <br> Blk.Vec {1,Parameter} {1,Monkey} {Motivational_context,TUS_Condition} {1,Cognitive_Load} Blk.Vec0 {1,Parameter} {1,Monkey} {Motivational_context,TUS_Condition} {1,Cognitive_Load} <br> Cell parameters are: <br> “Parameter”: 1,2,3. “Trial to criterion”,”information sampling”, and “Post_Learning Accuracy”. “Monkey”: 1,2,3. “Monkey W”, “Monkey I”, both monkeys. “Motivational_context”: 1,2. “Gain-Loss”, “Gain-only” “TUS_Condition”: 1,2,3,4. “Sham-ACC”, “TUS-ACC”, “Sham-STR”, “TUS-STR”. “Cognitive_Load”: 1,2,3.
DOI: 10.5517/ccdc.csd.cc2bplxk
title: CCDC 2165719: Experimental Crystal Structure Determination

description: Related Article: Leonardo Straminelli, Francesco Vicentini, Antonio Di Sabato, Carmela Maria Montone, Chiara Cavaliere, Kari Rissanen, Francesca Leonelli, Fabrizio Vetica|2022|J.Org.Chem.|87|10454|doi:10.1021/acs.joc.2c01046
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.20459232
title: Data for interaction image plots (Fig. 2D, Fig. S5C)

description: The “.mat” file contains 3 cell structure for plotting Fig.2d and Fig. S5c. The variable “IMagePlot” has 3 cells and as below: <br> IMagePlot{1, Monkey }{Icond,1}(Mn-SE:Mn+SE,Cognitive_Load) <br> “Monkey”= 1,2,3. “Monkey W”, “Monkey I”, both monkeys. “Motivational_context”= 1,2. “Gain-Loss”, “Gain-only” Icond= 1,3,5,7. “Sham-ACC”, “TUS-ACC”, “Sham-STR”, and “TUS-STR” for “Gain_Loss” context. Icond= 2,4,6,8. “Sham-ACC”, “TUS-ACC”, “Sham-STR”, and “TUS-STR” for “Gain_Only” context. “Cognitive_Load”= 1,2,3. <br> Mn : Mean across blocks SE : Standard error of the mean Mn-SE:Mn+SE is a 10x1 vector containing values for visualizing the gradient from lower to higher mean + standard error of the mean.
DOI: 10.6084/m9.figshare.20459232.v2
title: Data for interaction image plots (Fig. 2D, Fig. S5C)

description: The “.mat” file contains 3 cell structure for plotting Fig.2d and Fig. S5c. The variable “IMagePlot” has 3 cells and as below: <br> IMagePlot{1, Monkey }{Icond,1}(Mn-SE:Mn+SE,Cognitive_Load) <br> “Monkey”= 1,2,3. “Monkey W”, “Monkey I”, both monkeys. “Motivational_context”= 1,2. “Gain-Loss”, “Gain-only” Icond= 1,3,5,7. “Sham-ACC”, “TUS-ACC”, “Sham-STR”, and “TUS-STR” for “Gain_Loss” context. Icond= 2,4,6,8. “Sham-ACC”, “TUS-ACC”, “Sham-STR”, and “TUS-STR” for “Gain_Only” context. “Cognitive_Load”= 1,2,3. <br> Mn : Mean across blocks SE : Standard error of the mean Mn-SE:Mn+SE is a 10x1 vector containing values for visualizing the gradient from lower to higher mean + standard error of the mean.
DOI: 10.6084/m9.figshare.20459232.v1
title: Data for interaction image plots (Fig. 2D)

description: The “.mat” file contains 3 cell structure for plotting Fig.2d and Fig. S5c. The variable “IMagePlot” has 3 cells and as below: <br> IMagePlot{1, Monkey }{Icond,1}(Mn-SE:Mn+SE,Cognitive_Load) <br> “Monkey”= 1,2,3. “Monkey W”, “Monkey I”, both monkeys. “Motivational_context”= 1,2. “Gain-Loss”, “Gain-only” Icond= 1,3,5,7. “Sham-ACC”, “TUS-ACC”, “Sham-STR”, and “TUS-STR” for “Gain_Loss” context. Icond= 2,4,6,8. “Sham-ACC”, “TUS-ACC”, “Sham-STR”, and “TUS-STR” for “Gain_Only” context. “Cognitive_Load”= 1,2,3. <br> Mn : Mean across blocks SE : Standard error of the mean Mn-SE:Mn+SE is a 10x1 vector containing values for visualizing the gradient from lower to higher mean + standard error of the mean.
DOI: 10.6084/m9.figshare.20459223
title: Data for plotting curves (Fig.1F,G, Fig. S1, Fig. S2, Fig. S4A,B, Fig. S6A,B, and Fig. S8).

description: The structure contains mean and standard errors of the mean used to plot curves in Fig.1F,G, Fig. S1, Fig. S2, Fig. S4A,B, Fig. S6A,B, and Fig. S8. The “.mat” file contains 4 cell structures. <br> Mean_line: mean. MeanSE_line: standard error of the mean. <br> For cognitive load conditions: Dim.Mean_Line {1,Parameter} {1,Monkey} {1, Cognitive_Load} Dim.MeanSE_Line {1,Parameter} {1,Monkey} {1, Cognitive_Load } <br> For motivational context conditions: Tok.Mean_Line {1,Parameter} {1,Monkey} {1, Motivational_context } Tok.MeanSE_Line {1,Parameter} {1,Monkey} {1, Motivational_context } <br> For gain-loss conditions: TUSGL.Mean_Line {1,Parameter} {1,Monkey} {1, TUS_Condition } TUSGL.MeanSE_Line {1,Parameter} {1,Monkey} {1, TUS_Condition } <br> For gain-only conditions: <br> TUSG.Mean_Line {1,Parameter} {1,Monkey} {1, TUS_Condition } TUSG.MeanSE_Line {1,Parameter} {1,Monkey} {1, TUS_Condition } <br> Cell parameters are: “Parameter”: 1,2,3. “GLM_Fit”,”information sampling”, and “Accuracy”, “Monkey”: 1,2,3. “Monkey W”, “Monkey I”, both monkeys. “Motivational_context”: 1,2. “Gain-Loss”, “Gain-only” “TUS_Condition”: 1,2,3,4. “Sham-ACC”, “TUS-ACC”, “Sham-STR”, “TUS-STR”. “Cognitive_Load”: 1,2,3. <br>
DOI: 10.6084/m9.figshare.20459223.v1
title: Data for plotting curves (Fig. 1F,G).

description: The structure contains mean and standard errors of the mean used to plot curves in Fig.1F,G, Fig. S1, Fig. S2, Fig. S4A,B, Fig. S6A,B, and Fig. S8. The “.mat” file contains 4 cell structures. <br> Mean_line: mean. MeanSE_line: standard error of the mean. <br> For cognitive load conditions: Dim.Mean_Line {1,Parameter} {1,Monkey} {1, Cognitive_Load} Dim.MeanSE_Line {1,Parameter} {1,Monkey} {1, Cognitive_Load } <br> For motivational context conditions: Tok.Mean_Line {1,Parameter} {1,Monkey} {1, Motivational_context } Tok.MeanSE_Line {1,Parameter} {1,Monkey} {1, Motivational_context } <br> For gain-loss conditions: TUSGL.Mean_Line {1,Parameter} {1,Monkey} {1, TUS_Condition } TUSGL.MeanSE_Line {1,Parameter} {1,Monkey} {1, TUS_Condition } <br> For gain-only conditions: <br> TUSG.Mean_Line {1,Parameter} {1,Monkey} {1, TUS_Condition } TUSG.MeanSE_Line {1,Parameter} {1,Monkey} {1, TUS_Condition } <br> Cell parameters are: “Parameter”: 1,2,3. “GLM_Fit”,”information sampling”, and “Accuracy”, “Monkey”: 1,2,3. “Monkey W”, “Monkey I”, both monkeys. “Motivational_context”: 1,2. “Gain-Loss”, “Gain-only” “TUS_Condition”: 1,2,3,4. “Sham-ACC”, “TUS-ACC”, “Sham-STR”, “TUS-STR”. “Cognitive_Load”: 1,2,3. <br>
DOI: 10.6084/m9.figshare.20459106
title: Data for GTI plots (Fig 3F and Fig. S11)

description: The cell structure GTI (Gross token income) contains the mean and the standard error of the means used to plot Fig. 3F, and Fig. S11. The structure is as below: <br> GTI.Mean{1,Monkey} {1, TUS_Condition } GTI.SE{1,Monkey} {1, TUS_Condition } <br> “Monkey”: 1,2,3. “Monkey W”, “Monkey I”, both monkeys. “TUS_Condition”: 1,2,3,4. “Sham-ACC”, “TUS-ACC”, “Sham-STR”, “TUS-STR”.
DOI: 10.6084/m9.figshare.20459106.v2
title: Data for GTI plots (Fig 3F and Fig. S11)

description: The cell structure GTI (Gross token income) contains the mean and the standard error of the means used to plot Fig. 3F, and Fig. S11. The structure is as below: <br> GTI.Mean{1,Monkey} {1, TUS_Condition } GTI.SE{1,Monkey} {1, TUS_Condition } <br> “Monkey”: 1,2,3. “Monkey W”, “Monkey I”, both monkeys. “TUS_Condition”: 1,2,3,4. “Sham-ACC”, “TUS-ACC”, “Sham-STR”, “TUS-STR”.
DOI: 10.6084/m9.figshare.20459106.v1
title: Data for GTI plots (Fig 3F)

description: The cell structure GTI (Gross token income) contains the mean and the standard error of the means used to plot Fig. 3F, and Fig. S11. The structure is as below: <br> GTI.Mean{1,Monkey} {1, TUS_Condition } GTI.SE{1,Monkey} {1, TUS_Condition } <br> “Monkey”: 1,2,3. “Monkey W”, “Monkey I”, both monkeys. “TUS_Condition”: 1,2,3,4. “Sham-ACC”, “TUS-ACC”, “Sham-STR”, “TUS-STR”.
DOI: 10.5281/zenodo.6977314
title: Historic manuscript page images with noisy labels

description: Images of digitised manuscript pages sourced from https://iiif.biblissima.fr/collections/. This dataset aims to facilitate experiments using existing data/metadata to train computer vision models. In particular, using 'noisy' labels in some capacity. Each image is taken from a page of a manuscript listed on https://iiif.biblissima.fr/collections/. Each example includes the labels included in the IIIF manifests for these images. The data includes the following columns: image: an IIIF URL for the image manifest_url: A URL for the IIIF manifest for the image license: for each image label: the text found in the manifest 'label' field. attribution: which institution the image comes from
DOI: 10.5281/zenodo.6786969
title: Historic manuscript page images with noisy labels

description: Images of digitised manuscript pages sourced from https://iiif.biblissima.fr/collections/. This dataset aims to facilitate experiments using existing data/metadata to train computer vision models. In particular, using 'noisy' labels in some capacity. Each image is taken from a page of a manuscript listed on https://iiif.biblissima.fr/collections/. Each example includes the labels included in the IIIF manifests for these images. The data includes the following columns: image: an IIIF URL for the image manifest_url: A URL for the IIIF manifest for the image license: for each image label: the text found in the manifest 'label' field. attribution: which institution the image comes from
DOI: 10.6084/m9.figshare.20425920.v2
title: ITEM D:Student Evaluations of Mentors 2019

description: Student evaluations of mentor 2019
DOI: 10.6084/m9.figshare.20425920
title: ITEM D:Student Evaluations of Mentors 2019

description: Student evaluations of mentor 2019
DOI: 10.6084/m9.figshare.20425920.v1
title: Student Evaluations of Mentors 2019

description: Student evaluations of mentor 2019
DOI: 10.5281/zenodo.6977505
title: Supplementary data: "Evaluación de Tierras: Elaboración de un modelo de aptitud de uso agrícola para Kernza (Thinopyrum intermedium) en agroecosistemas con distinto grado de artificialización en el Partido de Azul, Provincia de Buenos Aires, Argentina" (II) /"Land Evaluation: an agricultural suitability model for Kernza (Thinopyrum intermedium) in agroecosystems with different land uses intensities in Azul distric, Buenos Aires, Argentina. (II)

description: Decision trees for the land suitability model for Kernza.
DOI: 10.5281/zenodo.6977504
title: Supplementary data: "Evaluación de Tierras: Elaboración de un modelo de aptitud de uso agrícola para Kernza (Thinopyrum intermedium) en agroecosistemas con distinto grado de artificialización en el Partido de Azul, Provincia de Buenos Aires, Argentina" (II) /"Land Evaluation: an agricultural suitability model for Kernza (Thinopyrum intermedium) in agroecosystems with different land uses intensities in Azul distric, Buenos Aires, Argentina. (II)

description: Decision trees for the land suitability model for Kernza.
DOI: 10.6084/m9.figshare.20459274
title: Data sheet of TUS simulation results used in (Fig 2A, Fig. S3)

description: The data contains simulation results used for the plots Fig. 2A, Fig. S3.
DOI: 10.6084/m9.figshare.20459274.v1
title: Data sheet of TUS simulation results used in (Fig 2A, Fig. S3)

description: The data contains simulation results used for the plots Fig. 2A, Fig. S3.
DOI: 10.6073/pasta/330cac9308b9dc4d3ecf3cb3eec7f1d8
title: Soil chemical and physical property data for Green Lakes Valley, 1982 - 1983.

description: The genesis and water chemistry of two soil catenas and mineralogy of eolian dust were studied to evaluate the capacity of these soils to buffer acid conditions. Field and laboratory analyses of 50 soil pits and three eolian dust traps indicated that the surface horizons are mainly eolian dust accumulation and the subsurface horizons differed in texture, organic carbon percentage, bulk density, pH, concentration of soluble and exchangeable ions, dissolved organic carbon, and distribution and concentration of clay materials. The chemistry and mineralogy of the eolian dust resemble those of the surface horizons. Calcite, which increases soil buffering capacity, was found in the eolian dust. The eolian calcite probably comes from western Colorado, where limestone exposures are common. This natural liming process may reduce effects of acid rain on the aquatic system.
DOI: 10.6073/pasta/e7bc0c3b3ebd9073f7941c433103b44d
title: Bulk density, organic matter content, and total carbon, nitrogen, and phosphorus content of soils collected from Acomastylis rossii and Kobresia myosuroides paired plots.

description: This study was initiated to examine bulk density and organic matter content differences between soils where Acomastylis rossii and Kobresia myosuroides were present. A subset of samples were also measured for total C, N, and P. Paired plots were randomly selected in locations where Kobresia and Acomastylis populations were adjacent to one another. Soil samples were collected from 35 plots, 22 and 13 of which had southerly and northerly aspects, respectively. Soil cores were removed with 3.5-cm interior diameter PVC pipe that was driven into the tundra by use of a rubber mallet. The minimum depth of individual cores was 9 cm, and these depths were recorded at the time of removal (15 July, 19 July, 22 July, and 9 August 1993).
DOI: 10.5281/zenodo.6514932
title: GNSS refractometry data from Davos Weissfluhjoch, Switzerland in 2016/17

description: This data is collected for the feasibility study on snow water equivalent (SWE) retrieval using the GNSS refractometry method, described in Steiner et al. (2020). GNSS Rinex data (1s sampling interval, multi-system, multi-frequency) from two high-end geodetic receivers (base=WJLR and rover=WJL0) are available for the complete 2016/17 season for the Swiss Alpine test sites Davos Weissfluhjoch, operated by the WSL Institute for Snow and Avalanche Research SLF (WSL SLF), Switzerland. SWE reference data from a snow pillow, snow scale, and manual observations were provided by the WSL SLF. The data processing and the SWE estimation results are revealed in Steiner et al. (2018, 2020, 2022).
description: {"references": ["Steiner, L.; Meindl, M.; Fierz, C.; Geiger, A. An assessment of sub snow GPS for quantification of snow water equivalent. The Cryosphere 2018, 12, 3161\u20133175. doi:10.5194/tc-12-3161-2018.349", "Steiner, L.; Meindl, M.; Marty, C.; Geiger, A. Impact of GPS Processing on the Estimation of Snow Water Equivalent Using Refracted GPS Signals. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 123\u2013135. doi:10.1109/TGRS.2019.2934016.352", "Steiner, L.; Studemann, G.; Grimm, D.; Marty, C.; Leinss, S. Near Real-Time Snow Water Equivalent Observation Using GNSS Refractometry and RTKLib. 2022, submitted to Sensors.", "Steiner, L. (2022). Snow water equivalent estimation based on GNSS refractometry using the biased up-component and post processing in RTKLIB, www.github.com/lasteine/GNSS_Refractometry_SWE"]}
DOI: 10.5281/zenodo.6514931
title: GNSS refractometry data from Davos Weissfluhjoch, Switzerland in 2016/17

description: This data is collected for the feasibility study on snow water equivalent (SWE) retrieval using the GNSS refractometry method, described in Steiner et al. (2020). GNSS Rinex data (1s sampling interval, multi-system, multi-frequency) from two high-end geodetic receivers (base=WJLR and rover=WJL0) are available for the complete 2016/17 season for the Swiss Alpine test sites Davos Weissfluhjoch, operated by the WSL Institute for Snow and Avalanche Research SLF (WSL SLF), Switzerland. SWE reference data from a snow pillow, snow scale, and manual observations were provided by the WSL SLF. The data processing and the SWE estimation results are revealed in Steiner et al. (2018, 2020, 2022).
description: {"references": ["Steiner, L.; Meindl, M.; Fierz, C.; Geiger, A. An assessment of sub snow GPS for quantification of snow water equivalent. The Cryosphere 2018, 12, 3161\u20133175. doi:10.5194/tc-12-3161-2018.349", "Steiner, L.; Meindl, M.; Marty, C.; Geiger, A. Impact of GPS Processing on the Estimation of Snow Water Equivalent Using Refracted GPS Signals. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 123\u2013135. doi:10.1109/TGRS.2019.2934016.352", "Steiner, L.; Studemann, G.; Grimm, D.; Marty, C.; Leinss, S. Near Real-Time Snow Water Equivalent Observation Using GNSS Refractometry and RTKLib. 2022, submitted to Sensors.", "Steiner, L. (2022). Snow water equivalent estimation based on GNSS refractometry using the biased up-component and post processing in RTKLIB, www.github.com/lasteine/GNSS_Refractometry_SWE"]}
DOI: 10.5281/zenodo.6977517
title: Dataset for Stack overflow Manual Results about challenges in developing Spark applications

description: This dataset contains Stack Overflow manual study results for the paper "An Empirical Study on the Challenges that Developers Encounter When Developing Apache Spark Applications". the <em>data</em> folder contains the <em>Stackoverflow Manual Results.csv </em>file that is the manual analysis result for the Stack Overflow posts. The CSV file contains information on the classification of the data, the reasons and the number of views, etc. the <em>scripts</em> folder contains the python and SQL files that are used for data collection and data analysis. <em>query_data.sql</em> is used to collect data from the Stack Exchange website. <em>sample.py</em> is used to sample data for the manual analysis in the paper. <em>common_issue.py</em> is used to study the percentage of common issues in rq1. <em>popularity.py </em>is used to calculate the average of normalized view counts in rq2. <em>popularity_difficulty.py</em> is used to calculate the average of raw view counts and the median hours to receive an answer in rq2. <em>root_cuase.py</em> is used to study the percentage of root causes in rq3.
DOI: 10.5281/zenodo.6977516
title: Dataset for Stack overflow Manual Results about challenges in developing Spark applications

description: This dataset contains Stack Overflow manual study results for the paper "An Empirical Study on the Challenges that Developers Encounter When Developing Apache Spark Applications". the <em>data</em> folder contains the <em>Stackoverflow Manual Results.csv </em>file that is the manual analysis result for the Stack Overflow posts. The CSV file contains information on the classification of the data, the reasons and the number of views, etc. the <em>scripts</em> folder contains the python and SQL files that are used for data collection and data analysis. <em>query_data.sql</em> is used to collect data from the Stack Exchange website. <em>sample.py</em> is used to sample data for the manual analysis in the paper. <em>common_issue.py</em> is used to study the percentage of common issues in rq1. <em>popularity.py </em>is used to calculate the average of normalized view counts in rq2. <em>popularity_difficulty.py</em> is used to calculate the average of raw view counts and the median hours to receive an answer in rq2. <em>root_cuase.py</em> is used to study the percentage of root causes in rq3.
DOI: 10.6084/m9.figshare.19686690
title: Data from: Effects of habitat degradation on bird functional diversity: A field test in the Valdivian rainforest

description: Original data from: Fontúrbel, F.E., J.F. Betancurt-Grisales, A.M. Vargas-Daza & G.J. Castaño-Villa. 2022. Effects of habitat degradation on bird functional diversity: A field test in the Valdivian rainforest. Forest Ecology and Management 522: 120466. <br> Please refer to the readme.txt file for further information.
DOI: 10.5281/zenodo.6427023
title: The MicroRESUS study: An observational study to examine the effects of circulatory shock and resuscitation on microcirculatory function and mitochondrial respiration after cardiovascular surgery.

description: Post-cardiotomy shock (PCS) occurs in up to 5% of cardiovascular surgeries and has an in-hospital mortality rate as high as 75%. Physiologic derangements are often multifactorial, including ischemic-reperfusion injury leading to low cardiac output, vasoplegia, hemorrhage, or pericardial tamponade. Resuscitation strategies often target the normalization of systemic hemodynamics, however, practices to achieve standard macrocirculatory goals often fail to reverse microcirculation abnormalities or address cytopathic hypoxia. Perioperative shock may be the result of an imbalance between oxygen delivery and oxygen demand or could be the result of impaired oxygen utilization. This study will examine differences in microcirculatory function and mitochondrial respiration in patients with circulatory shock after cardiovascular surgery.