cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e175902
title: School choice, local discretion, and stratification: Evidence from inter-district open enrollment in Metro Detroit

description: In this study, I use longitudinal data on inter-district open enrollment patterns and policies to estimate the effect of local decisions over open enrollment policy on non-resident enrollment in Wayne County, Michigan—which includes Detroit and parts of the metropolitan Detroit area. I find that when districts set more restrictive open enrollment policies, they enroll fewer new non-resident students, driven by a decrease in new Black, Hispanic, and low-income non-resident students specifically. When districts allow inter-district open enrollment, racial and socioeconomic segregation within those districts remain unchanged, and racial isolation slightly increases. My findings suggest that regulating enrollment policies to reduce discretionary exclusion can increase access to inter-district choice for some low-income and racially minoritized students, but that these kinds of policy changes are unlikely to reduce racial segregation and socioeconomic stratification more broadly.<br><br>This research result used data structured and maintained by the MERI-Michigan Education Data Center (MEDC). MEDC data are modified for analysis purposes using rules governed by MEDC and are not identical to those data collected and maintained by the Michigan Department of Education (MDE) and/or Michigan’s Center for Educational Performance and Information (CEPI). Results, information, and opinions solely represent the analysis, information, and opinions of the author and are not endorsed by, or reflect the views or positions of, grantors, MDE, and CEPI or any employee thereof. All errors are my own.
DOI: 10.3886/e175902v1
title: School choice, local discretion, and stratification: Evidence from inter-district open enrollment in Metro Detroit

description: In this study, I use longitudinal data on inter-district open enrollment patterns and policies to estimate the effect of local decisions over open enrollment policy on non-resident enrollment in Wayne County, Michigan—which includes Detroit and parts of the metropolitan Detroit area. I find that when districts set more restrictive open enrollment policies, they enroll fewer new non-resident students, driven by a decrease in new Black, Hispanic, and low-income non-resident students specifically. When districts allow inter-district open enrollment, racial and socioeconomic segregation within those districts remain unchanged, and racial isolation slightly increases. My findings suggest that regulating enrollment policies to reduce discretionary exclusion can increase access to inter-district choice for some low-income and racially minoritized students, but that these kinds of policy changes are unlikely to reduce racial segregation and socioeconomic stratification more broadly.<br><br>This research result used data structured and maintained by the MERI-Michigan Education Data Center (MEDC). MEDC data are modified for analysis purposes using rules governed by MEDC and are not identical to those data collected and maintained by the Michigan Department of Education (MDE) and/or Michigan’s Center for Educational Performance and Information (CEPI). Results, information, and opinions solely represent the analysis, information, and opinions of the author and are not endorsed by, or reflect the views or positions of, grantors, MDE, and CEPI or any employee thereof. All errors are my own.
DOI: 10.13026/drts-zb06
title: GOSSIS-1-eICU, the eICU-CRD subset of the Global Open Source Severity of Illness Score (GOSSIS-1) dataset

description: GOSSIS-1 is a modern, free, open-source in-hospital mortality prediction
algorithm for critical care patients, achieving excellent discrimination and
calibration across three countries (Australia, New Zealand and the USA).
GOSSIS-1 was developed on two large datasets of critical care patients. This
project contains the USA subset of patients derived from the eICU
Collaborative Research Database (eICU-CRD). The dataset, which we call
GOSSIS-1-eICU, consists of 131,051 unique patients from 204 hospitals from ICU
admissions discharged in 2014-15. The code to create the dataset from eICU-CRD
and generate GOSSIS-1 predictions are also available. This project contains:
1) the derived dataset from eICU-CRD, 2) the dataset with required missing
data imputed and 3) the GOSSIS-1 in-hospital predictions (probabilities. The
`patientunitstayid` and `hospitalid` eICU-CRD identifiers are included to
allowing linking back to eICU-CRD. Training and test sets are identified to
allow for direct comparisons of performance.
DOI: 10.13026/gbmg-a531
title: GOSSIS-1-eICU, the eICU-CRD subset of the Global Open Source Severity of Illness Score (GOSSIS-1) dataset

description: GOSSIS-1 is a modern, free, open-source in-hospital mortality prediction
algorithm for critical care patients, achieving excellent discrimination and
calibration across three countries (Australia, New Zealand and the USA).
GOSSIS-1 was developed on two large datasets of critical care patients. This
project contains the USA subset of patients derived from the eICU
Collaborative Research Database (eICU-CRD). The dataset, which we call
GOSSIS-1-eICU, consists of 131,051 unique patients from 204 hospitals from ICU
admissions discharged in 2014-15. The code to create the dataset from eICU-CRD
and generate GOSSIS-1 predictions are also available. This project contains:
1) the derived dataset from eICU-CRD, 2) the dataset with required missing
data imputed and 3) the GOSSIS-1 in-hospital predictions (probabilities. The
`patientunitstayid` and `hospitalid` eICU-CRD identifiers are included to
allowing linking back to eICU-CRD. Training and test sets are identified to
allow for direct comparisons of performance.
DOI: 10.6084/m9.figshare.3761562.v2285
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.3886/e155661
title: Race, geography, and school choice policy: A critical analysis of Detroit students’ suburban school choices

description: The purpose of this study is to advance our thinking about race and racism in geospatial analyses of school choice policy. To do so, we present a critical race spatial analysis of Detroit students’ suburban school choices. To frame our study, we describe the racial and spatial dynamics of school choice, drawing in particular on the concepts of opportunity hoarding and predatory landscapes. We find that Detroit students’ suburban school choices were circumscribed by racial geography and concentrated in just a handful of schools and districts. We also find notable differences between students in different racial groups. For all Detroit exiters, their schools were significantly more segregated and lower quality than those of their suburban peers. We propose future directions for research on families’ school choices as well as school and district behavior at the intersection of race, geography, and school choice policy.<br><br>This research result used data structured and maintained by the MERI-Michigan Education Data Center (MEDC). MEDC data are modified for analysis purposes using rules governed by MEDC and are not identical to those data collected and maintained by the Michigan Department of Education (MDE) and/or Michigan’s Center for Educational Performance and Information (CEPI). Results, information, and opinions solely represent the analysis, information, and opinions of the author and are not endorsed by, or reflect the views or positions of, grantors, MDE, and CEPI or any employee thereof. All errors are my own.<br>
description: This research result used data structured and maintained by the MERI-Michigan Education Data Center (MEDC). MEDC data is modified for analysis purposes using rules governed by MEDC and are not identical to those data collected and maintained by the Michigan Department of Education (MDE) and/or Michigan’s Center for Educational Performance and Information (CEPI). Results, information and opinions solely represent the analysis, information and opinions of the author(s) and are not endorsed by, or reflect the views or positions of, grantors, MDE and CEPI or any employee thereof.<b></b>
DOI: 10.3886/e155661v2
title: Race, geography, and school choice policy: A critical analysis of Detroit students’ suburban school choices

description: The purpose of this study is to advance our thinking about race and racism in geospatial analyses of school choice policy. To do so, we present a critical race spatial analysis of Detroit students’ suburban school choices. To frame our study, we describe the racial and spatial dynamics of school choice, drawing in particular on the concepts of opportunity hoarding and predatory landscapes. We find that Detroit students’ suburban school choices were circumscribed by racial geography and concentrated in just a handful of schools and districts. We also find notable differences between students in different racial groups. For all Detroit exiters, their schools were significantly more segregated and lower quality than those of their suburban peers. We propose future directions for research on families’ school choices as well as school and district behavior at the intersection of race, geography, and school choice policy.<br><br>This research result used data structured and maintained by the MERI-Michigan Education Data Center (MEDC). MEDC data are modified for analysis purposes using rules governed by MEDC and are not identical to those data collected and maintained by the Michigan Department of Education (MDE) and/or Michigan’s Center for Educational Performance and Information (CEPI). Results, information, and opinions solely represent the analysis, information, and opinions of the author and are not endorsed by, or reflect the views or positions of, grantors, MDE, and CEPI or any employee thereof. All errors are my own.<br>
description: This research result used data structured and maintained by the MERI-Michigan Education Data Center (MEDC). MEDC data is modified for analysis purposes using rules governed by MEDC and are not identical to those data collected and maintained by the Michigan Department of Education (MDE) and/or Michigan’s Center for Educational Performance and Information (CEPI). Results, information and opinions solely represent the analysis, information and opinions of the author(s) and are not endorsed by, or reflect the views or positions of, grantors, MDE and CEPI or any employee thereof.<b></b>
DOI: 10.5281/zenodo.6871428
title: SARS-CoV-2 virus seems to induce O-glycosylation and other metabolic dysfunctions which might predispose new-onset diabetes

description: COVID-2019 pandemic has caused huge damage to human health, and some individuals are more prone to severe manifestations, such as type-2 diabetic patients. The correlation between diabetes and severe manifestations have been under investigation, and molecular findings may highlight some pathophysiological pathways. The present study aimed to identify plasma metabolites in patients affected by type-2 diabetes and COVID-19, concomitantly, using an untargeted metabolomic approach. We employed high-resolution mass spectrometry analysis to annotate 10 metabolites, with N-acetyl-D-glucosamine among them; it is involved with O-glycosylation, which is enhanced in diabetic conditions. SARS-CoV-2 Spike proteins depend on glycosylation, what seems to be overstimulated upon infection. Interestingly, N-acetyl-D-glucosamine was found in part of the non-diabetic COVID-19 patients, indicating that SARS-CoV-2 might induce new-onset diabetes through glycosylation. Besides, the identified biomarkers seem to be all interconnected and represent a global perception of metabolic alterations in a Diabetic-COVID-19 scenario, highlighting new targets for further pharmacological interventions.
DOI: 10.5281/zenodo.6871427
title: SARS-CoV-2 virus seems to induce O-glycosylation and other metabolic dysfunctions which might predispose new-onset diabetes

description: COVID-2019 pandemic has caused huge damage to human health, and some individuals are more prone to severe manifestations, such as type-2 diabetic patients. The correlation between diabetes and severe manifestations have been under investigation, and molecular findings may highlight some pathophysiological pathways. The present study aimed to identify plasma metabolites in patients affected by type-2 diabetes and COVID-19, concomitantly, using an untargeted metabolomic approach. We employed high-resolution mass spectrometry analysis to annotate 10 metabolites, with N-acetyl-D-glucosamine among them; it is involved with O-glycosylation, which is enhanced in diabetic conditions. SARS-CoV-2 Spike proteins depend on glycosylation, what seems to be overstimulated upon infection. Interestingly, N-acetyl-D-glucosamine was found in part of the non-diabetic COVID-19 patients, indicating that SARS-CoV-2 might induce new-onset diabetes through glycosylation. Besides, the identified biomarkers seem to be all interconnected and represent a global perception of metabolic alterations in a Diabetic-COVID-19 scenario, highlighting new targets for further pharmacological interventions.
DOI: 10.5281/zenodo.6871472
title: DS.RFSAT.OBJECT-3D-SCANS

description: This dataset contains the DS.RFSAT.OBJECT-3D-SCANS, as referred to in the <em>ARCH Data Management Plan</em> deliverable (D1.3). It contains SOLELY the 3D models produced by RFSAT Limited from aerial drone images captured by Hamburg City. Models processing was done using PIX4D v4 engine linked to from RFSAT autonomous scanning software (SWSCAN). Comparative models have been produced also with Agisoft Metashape engine. <strong>NOTE</strong>: this is a preliminary version with updated to be published shortly.
DOI: 10.5281/zenodo.6871471
title: DS.RFSAT.OBJECT-3D-SCANS

description: This dataset contains the DS.RFSAT.OBJECT-3D-SCANS, as referred to in the <em>ARCH Data Management Plan</em> deliverable (D1.3). It contains SOLELY the 3D models produced by RFSAT Limited from aerial drone images captured by Hamburg City. Models processing was done using PIX4D v4 engine linked to from RFSAT autonomous scanning software (SWSCAN). Comparative models have been produced also with Agisoft Metashape engine. <strong>NOTE</strong>: this is a preliminary version with updated to be published shortly.
DOI: 10.5281/zenodo.6871927
title: Blank

description: Blank Description
description: Notes
DOI: 10.5281/zenodo.6871926
title: Blank

description: Blank Description
description: Notes
DOI: 10.25452/figshare.plus.20289759
title: CERTS Microgrid Test Bed Demonstration Data

description: The objective of the CERTS Microgrid Test Bed Demonstration, which was hosted by American Electric Power, was to enhance the ease of integrating small energy sources into a microgrid. The project accomplished this objective by developing and demonstrating three advanced techniques, collectively referred to as the CERTS Microgrid concept, that significantly reduce the level of custom field engineering needed to operate microgrids consisting of small generating sources. The techniques comprising the CERTS Microgrid concept are: (1) a method for effecting automatic and seamless transitions between grid-connected and islanded modes of operation; (2) an approach to electrical protection within the microgrid that does not depend on high fault currents; and (3) a method for microgrid control that achieves voltage and frequency stability under islanded conditions without requiring high-speed communications. The test data is grouped by each phase of the project. Each phase of the project has an associated .xls file that contains a log of the test set-up, comments and the folder where the data is stored. The waveform data for each test is stored in a .parquet file. These files can be easily imported using many standard programming languages including MATLAB and using the pandas library in Python. The columns in each file are - Datetime_ns: The datetime stamp of the measurement in nanoseconds. - EventTime_s: The timestamp trigger of the recorded event in seconds. The meter typically records 0.25 seconds before the event and up to 5 seconds after the event trigger - Va: The instantaneous voltage measured on Phase A as a % of the nominal voltage - Vb: The instantaneous voltage measured on Phase B as a % of the nominal voltage - Vc: The instantaneous voltage measured on Phase C as a % of the nominal voltage - Ia: The instantaneous current though Phase A in amps - Ib: The instantaneous current though Phase B in amps - Ic: The instantaneous current though Phase C in amps The data 'Datetime_ns' is timezone agnostic and can be converted to human-readable format for cross-referencing with the test logs using the pandas function pandas.Timestamp.
DOI: 10.6084/m9.figshare.20346510
title: EdU-Click on Gut Bacterial Isolates

description: Flow cytometry data on bacterial isolates oringating from the human gut, incubated for three hours with (or without) 20 uM 5-ethnyl-2'-deoxyuridine (EdU), a thymidine analogue, in a 1:1 ratio of reduced Brain Heart Infusion (rBHI) broth and fecal slurry (FS), to capture the actively replicating cells. <br> The bacterial cells underwent a click reaction with Alexa Fluor 647 azide (APC channel) to make newly replicated bacteria fluoresece. They were also stained with Sybr Green I nucleic acid stain (FITC channel) to capture all bacteria. <br> In the file names: AM = <em>Akkermansia muciniphila</em> BC = <em>Bacteroides caccae</em> BLI = <em>Bifidobacterium longum </em>subsp. <em>infantis</em> BF = <em>Bacteroides fragilis</em> CB = <em>Clostridium beijerinckii</em> EC = <em>Escherichia coli </em>K12 EF = <em>Enterococcus faecalis</em> PI = <em>Prevotella intermedia</em> RB = <em>Ruminococcus bromii</em>
DOI: 10.6084/m9.figshare.20346510.v1
title: EdU-Click on Gut Bacterial Isolates

description: Flow cytometry data on bacterial isolates oringating from the human gut, incubated for three hours with (or without) 20 uM 5-ethnyl-2'-deoxyuridine (EdU), a thymidine analogue, in a 1:1 ratio of reduced Brain Heart Infusion (rBHI) broth and fecal slurry (FS), to capture the actively replicating cells. <br> The bacterial cells underwent a click reaction with Alexa Fluor 647 azide (APC channel) to make newly replicated bacteria fluoresece. They were also stained with Sybr Green I nucleic acid stain (FITC channel) to capture all bacteria. <br> In the file names: AM = <em>Akkermansia muciniphila</em> BC = <em>Bacteroides caccae</em> BLI = <em>Bifidobacterium longum </em>subsp. <em>infantis</em> BF = <em>Bacteroides fragilis</em> CB = <em>Clostridium beijerinckii</em> EC = <em>Escherichia coli </em>K12 EF = <em>Enterococcus faecalis</em> PI = <em>Prevotella intermedia</em> RB = <em>Ruminococcus bromii</em>
DOI: 10.18112/openneuro.ds004217.v1.0.0
title: Interacting representations of mental states and traits
DOI: 10.5281/zenodo.6871982
title: The antique genetic plight of the Mediterranean monk seal (Monachus monachus)

description: This repository contains all the scripts and most of the intermediary files necessary to replicate the analyses of the preprint "The antique genetic plight of the Mediterranean monk seal (<em>Monachus monachus</em>)" available at: https://biorxiv.org/cgi/content/short/2021.12.23.473149v3 This version of the data has been revised according to two rounds of review by two reviewers' comments and suggestions during a submission process at Proceedings of the Royal Society B. Within each of the different zipped folders a readme.txt file briefly explains how the analyses are organized. We thank all the collectors and museums listed in Table S1 for providing access to genetic samples. We are grateful to Sophie Courjal and the staff of the “Plateau technique - Biologie moléculaire et microbiologie” at EDB for their assistance during lab work, to P. Kyritsis [Archipelagos], for his logistic help, to I. Carvalho for early comments on the manuscript and two anonymous reviewers who significantly helped to improve the manuscript. This work was funded by the Fondation Prince Albert II de Monaco (project “Génétique de la conservation du phoque moine de Méditerranée”). The Genotoul bioinformatics (Bioinfo Genotoul) platforms provided computing resources. JS was supported by PANGO-GO (ANR-17-CE02-0001), and LABEX TULIP (ANR-10-LABX-0041).
description: Abstract Disentangling the impact of Late Quaternary climate change from human activities can have crucial implications on the conservation of endangered species. We investigated the population genetics and demography of the Mediterranean monk seal (Monachus monachus), one of the world's most endangered marine mammals, through an unprecedented dataset encompassing historical (extinct) and extant populations from the eastern North Atlantic to the entire Mediterranean Basin. We show that Western-Sahara/Mauritania (Cabo Blanco), Madeira, Western Mediterranean (historical range) and Eastern Mediterranean regions segregate in four populations. This structure is likely the consequence of recent drift, combined with long-term isolation by distance (R2 = 0.7), resulting from prevailing short-distance (&lt; 500 km) and infrequent long-distance dispersal (&lt; 1,500 km). All populations (Madeira especially), show high levels of inbreeding and low levels of genetic diversity, seemingly declining since historical time, but surprisingly not being impacted by the 1997 massive die-off in Cabo Blanco. Approximate Bayesian Computation analyses support scenarios combining local extinctions and a major effective population size decline in all populations during Antiquity. Our results suggest that the early densification of human populations around the Mediterranean Basin coupled to the development of seafaring techniques were the main drivers of the decline of Mediterranean monk seals.