cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48322/eg0n-fn33
title: MMS 4 Digital Signal Processor (DSP) Double Probe (ADP, SDP), Electric Field Power Spectral Density, Level 2 (L2), Slow Mode, 16 s Data
title: MMS4_DSP_SLOW_L2_EPSD

description: The MMS electric field power spectral density (EPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Axial Double Probe (ADP) and Spin-Plane Double Probe (SDP). This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 56 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The averaging process has 48-bit accuracy to maximize the dynamic range. The amplitudes undergo a pseudo-logarithmic compression to an 8-bit number representing over 120 dB of dynamic range at ~5% precision.
DOI: 10.48322/gnbq-p392
title: MMS 4 Digital Signal Processor (DSP) Search Coil Magnetometer (SCM), Magnetic Field Power Spectral Density, Level 2 (L2), Fast Mode, 2 s Data
title: MMS4_DSP_FAST_L2_BPSD

description: The MMS magnetic field power spectral density (BPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Search Coil Magnetometer (SCM) in the SCM123 coordinate system, see SCM data product guide for details, https://lasp.colorado.edu/mms/sdc/public/datasets/fields/. This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 88 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The DSP and SCM instrument papers can be found at https://link.springer.com/article/10.1007/s11214-014-0115-x and https://link.springer.com/article/10.1007/s11214-014-0096-9, respectively. The DSP and SCM data product guides can be found at https://lasp.colorado.edu/mms/sdc/public/datasets/fields/.
DOI: 10.48322/b9vr-gb77
title: MMS 4 Digital Signal Processor (DSP) Search Coil Magnetometer (SCM), Magnetic Field Power Spectral Density, Level 2 (L2), Slow Mode, 16 s Data
title: MMS4_DSP_SLOW_L2_BPSD

description: The MMS magnetic field power spectral density (BPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Search Coil Magnetometer (SCM) in the SCM123 coordinate system (scm1 = - x sensor; scm2 = -z sensor; scm3 = -y sensor). This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 88 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The averaging process has 48-bit accuracy to maximize the dynamic range. The amplitudes undergo a pseudo-logarithmic compression to an 8-bit number representing over 120 dB of dynamic range at ~5% precision.
DOI: 10.48322/j5ge-t096
title: MMS 4 Electric Double Probe (EDP) Spacecraft Potential, Level 2 (L2), Burst Mode, 0.1220703125 ms Data
title: MMS4_EDP_BRST_L2_SCPOT

description: Electric Double Probe, Dual Probe Spacecraft Potential, Level 2, Burst Mode Data. Spacecraft potential is etstimatrd by averaging measurements of the probe-to-spacecraft potential from several probes.
DOI: 10.48322/zpw3-4w58
title: MMS 4 Electric Double Probe (EDP) Three-Dimensional Electric Field, Level 2 (L2), Burst Mode, 0.1220703125 ms Data
title: MMS4_EDP_BRST_L2_DCE

description: Electric Double Probe, Three-Dimensional Electric Field, Level 2, Burst Mode Data
DOI: 10.48322/g4hf-3b35
title: MMS 4 Electric Double Probe (EDP) Axial Double Probe, Spin Plane Double Probe (ADP-SDP) Three-Dimensional HMFE Electric Field, Level 2 (L2), Burst Mode, 0.01525878906 ms Data
title: MMS4_EDP_BRST_L2_HMFE

description: Electric Double Probe, Three-Dimensional HMFE Electric Field, Level 2, Burst Mode, Level 1B AC Electric Field Data
DOI: 10.48322/194m-pq72
title: MMS 4 Electric Double Probe (EDP) Spacecraft Potential, Level 2 (L2), Fast Mode, 31.25 ms Data
title: MMS4_EDP_FAST_L2_SCPOT

description: Electric Double Probe, Dual Probe Spacecraft Potential, Level 2, Fast Survey Data. Spacecraft potential is etstimatrd by averaging measurements of the probe-to-spacecraft potential from several probes.
DOI: 10.48322/dwae-wd67
title: MMS 4 Electric Double Probe (EDP) Three-Dimensional Electric Field, Level 2 (L2), Fast Mode, 31.25 ms Data
title: MMS4_EDP_FAST_L2_DCE

description: Electric Double Probe, Quick-Look Three-Dimensional Electric Field, Level 2, Fast Survey Data
DOI: 10.48322/3wwj-0924
title: MMS 4 Electric Double Probe (EDP) Three-Dimensional Electric Field, Level 2 (L2), Slow Mode, 0.125 s Data
title: MMS4_EDP_SLOW_L2_DCE

description: Electric Double Probe, Quick-Look Three-Dimensional Electric Field, Level 2, Slow Survey Data
DOI: 10.48322/t44j-x227
title: MMS 4 Electric Double Probe (EDP) Spacecraft Potential, Level 2 (L2), Slow Mode, 0.125 s Data
title: MMS4_EDP_SLOW_L2_SCPOT

description: Electric Double Probe, Dual Probe Spacecraft Potential, Level 2, Slow Survey Data. Spacecraft potential is etstimatrd by averaging measurements of the probe-to-spacecraft potential from several probes.
DOI: 10.48322/257w-3j79
title: MMS 4 Flux Gate Magnetometer (FGM) DC Magnetic Field, Level 2 (L2), Burst Mode, 128 Sample/s, v4/5 Data
title: MMS4_FGM_BRST_L2

description: The Fluxgate Magnetometers (FGM) on Magnetospheric Multiscale consist of a traditional Analog Fluxgate Magnetometer (AFG) and a Digital Fluxgate magnetometer (DFG). The dual magnetometers are operated as a single instrument providing a single intercalibrated data product. Range changes occur at different times on the two instruments so the gains checked each periapsis can be carried out unambiguously to apoapsis. Cross correlation of calibration parameters can separate causes of the any apparent calibration changes. Use of Electron Drift Instrument (EDI) to determine the field along the rotation axis allows accurate monitoring of the zero levels along the rotation axis. Prior to launch the magnetometers were calibrated at the Technical University, Braunschweig, except for the AFG magnetometers on MMS3 and MMS4, which were calibrated at UCLA. Both sets of sensors are operated for the entire MMS orbit, with slow survey (8 samples per second) outside of the Region of Interest (ROI), and fast survey (16 samples per second) inside the ROI. Within the ROI, burst mode data (128 samples per second) are also acquired. A detailed description of the MMS fluxgate magnetometers, including science objectives, instrument description, calibration, magnetic cleanliness program, and data flow can be found at http://link.springer.com/article/10.1007%2Fs11214-014-0057-3 (DOI 10.1007/s11214-014-0057-3). Additional information can also be found at http://www-spc.igpp.ucla.edu/ssc/mms (UCLA), and http://www.iwf.oeaw.ac.at/de/forschung/erdnaher-weltraum/mms/dfg (IWF, Graz). For the purpose of creating a unified FGM Level 2 data product, burst mode data is taken from DFG and survey mode data is taken from AFG. Because AFG and DFG are cross-calibrated on an orbit-averaged basis, small differences in offset may be observed between Level 2 burst and survey mode data. Consequently, any differences are within the error of the measurement. Based on preliminary analysis of the data, the absolute error within the Region of Interest (ROI) is estimated to be no more than 0.1 nT in the spin-plane, 0.15 nT along the spin-axis and 0.2 nT in total magnitude.
DOI: 10.48322/kcms-y548
title: MMS 4 Electric Double Probe (EDP) Axial Double Probe, Spin Plane Double Probe (ADP-SDP) Electric Double Probe, High-Frequency Electric Field Spectra, Level 2 (L2), Survey Mode, 16 s Data
title: MMS4_EDP_SRVY_L2_HFESP

description: Electric Double Probe, High-Frequency AC Electric Field Spectra, Level 2, Survey Data, Level 1B AC Electric Field Data
DOI: 10.48322/50p5-d131
title: MMS 4 Flux Gate Magnetometer (FGM) DC Magnetic Field, Level 2 (L2), Survey Mode, 8 or 16 Sample/s, v4/5 Data
title: MMS4_FGM_SRVY_L2

description: The Fluxgate Magnetometers (FGM) on Magnetospheric Multiscale consist of a traditional Analog Fluxgate Magnetometer (AFG) and a Digital Fluxgate magnetometer (DFG). The dual magnetometers are operated as a single instrument providing a single intercalibrated data product. Range changes occur at different times on the two instruments so the gains checked each periapsis can be carried out unambiguously to apoapsis. Cross correlation of calibration parameters can separate causes of the any apparent calibration changes. Use of Electron Drift Instrument (EDI) to determine the field along the rotation axis allows accurate monitoring of the zero levels along the rotation axis. Prior to launch the magnetometers were calibrated at the Technical University, Braunschweig, except for the AFG magnetometers on MMS3 and MMS4, which were calibrated at UCLA. Both sets of sensors are operated for the entire MMS orbit, with slow survey (8 samples per second) outside of the Region of Interest (ROI), and fast survey (16 samples per second) inside the ROI. Within the ROI, burst mode data (128 samples per second) are also acquired. A detailed description of the MMS fluxgate magnetometers, including science objectives, instrument description, calibration, magnetic cleanliness program, and data flow can be found at http://link.springer.com/article/10.1007%2Fs11214-014-0057-3 (DOI 10.1007/s11214-014-0057-3). Additional information can also be found at http://www-spc.igpp.ucla.edu/ssc/mms (UCLA), and http://www.iwf.oeaw.ac.at/de/forschung/erdnaher-weltraum/mms/dfg (IWF, Graz). For the purpose of creating a unified FGM Level 2 data product, burst mode data is taken from DFG and survey mode data is taken from AFG. Because AFG and DFG are cross-calibrated on an orbit-averaged basis, small differences in offset may be observed between Level 2 burst and survey mode data. Consequently, any differences are within the error of the measurement. Based on preliminary analysis of the data, the absolute error within the Region of Interest (ROI) is estimated to be no more than 0.1 nT in the spin-plane, 0.15 nT along the spin-axis and 0.2 nT in total magnitude.
DOI: 10.48322/89rs-0f95
title: MMS 4 Search Coil Magnetometer (SCM) AC Magnetic Field Level 2 (L2), High Speed Burst Mode, 16384 Sample/s Data
title: MMS4_SCM_BRST_L2_SCHB

description: Search Coil Magnetometer (SCM) AC Magnetic Field (16384 samples/s), Level 2, High Speed Burst Mode Data. The tri-axial Search-Coil Magnetometer with its associated preamplifier measures three-dimensional magnetic field fluctuations. The analog magnetic waveforms measured by the SCM are digitized and processed inside the Digital Signal Processor (DSP), collected and stored by the Central Instrument Data Processor (CIDP) via the Fields Central Electronics Box (CEB). Prior to launch, all SCM Flight models were calibrated by LPP team members at the National Magnetic Observatory, Chambon-la-Foret (Orleans). Once per orbit, each SCM transfer function is checked thanks to the onboard calibration signal provided by the DSP. The SCM is operated for the entire MMS orbit in survey mode. Within scientific Regions Of Interest (ROI), burst mode data are also acquired as well as high speed burst mode data. This SCM data set corresponds to the AC magnetic field waveforms in nanoTesla and in the GSE frame. The SCM instrument paper for SCM can be found at http://link.springer.com/article/10.1007/s11214-014-0096-9 and the SCM data product guide at https://lasp.colorado.edu/mms/sdc/public/datasets/fields/.
DOI: 10.3886/e208370
title: Data and Code for: Raising the Stakes: Physician Facility Investments and Provider Agency

description: Principal-agent problems often extend beyond what can be directly addressed through conventional incentive arrangements. We examine a context where physicians are likely under-incentivized to minimize total medical costs until their private financial interests align with those of patients. Leveraging novel data on physician ownership of ambulatory surgery centers––i.e., same-day facilities––we show that these equity holdings cause a substitution away from higher cost, rival settings that lowers Medicare spending by 10-40% per physician. We find no clear evidence of perverse behavior following these investments. Instead, our findings demonstrate how entrepreneurial activity can indirectly limit principal-agent problems and improve efficiency. <br>
description: N/A
description: Response Rates: All Medicare FFS and Florida patients with an outpatient procedure.
description: Presence of Common Scales: N/A
description: Medicare fee-for-service patients and Florida outpatient discharge patients. . Smallest Geographic Unit: Individual physician
description: Medicare Fee-for-service Claims Data from 2013--2019We used the Carrier files, Outpatient files, and Enrollment files of a 100% sample of Medicare Fee- for-Service (FFS) claims data to construct the outcome measures. The data contain individual-level PHI data, so they are confidential and cannot be posted online. Researchers interested in using these data should apply to the Centers for Medicare and Medicaid Services (CMS).The code is designed to access these data via a Subversion repository we designed for this project. Researchers wishing to replicate our results will need to obtain their own copy of the data by applying for a Data Use Agreement through the Research Data Assistance Center (ResDAC). More information on obtaining the restricted data through ResDAC can be found at this location: https://www.resdac.org/research-identifiable-files-rif-requests. Florida dataFlorida discharge data is from the Florida Agency for Health Care Administration (AHCA) https://ahca.myflorida.com/ .
DOI: 10.3886/e208370v1
title: Data and Code for: Raising the Stakes: Physician Facility Investments and Provider Agency

description: Principal-agent problems often extend beyond what can be directly addressed through conventional incentive arrangements. We examine a context where physicians are likely under-incentivized to minimize total medical costs until their private financial interests align with those of patients. Leveraging novel data on physician ownership of ambulatory surgery centers––i.e., same-day facilities––we show that these equity holdings cause a substitution away from higher cost, rival settings that lowers Medicare spending by 10-40% per physician. We find no clear evidence of perverse behavior following these investments. Instead, our findings demonstrate how entrepreneurial activity can indirectly limit principal-agent problems and improve efficiency. <br>
description: N/A
description: Response Rates: All Medicare FFS and Florida patients with an outpatient procedure.
description: Presence of Common Scales: N/A
description: Medicare fee-for-service patients and Florida outpatient discharge patients. . Smallest Geographic Unit: Individual physician
description: Medicare Fee-for-service Claims Data from 2013--2019We used the Carrier files, Outpatient files, and Enrollment files of a 100% sample of Medicare Fee- for-Service (FFS) claims data to construct the outcome measures. The data contain individual-level PHI data, so they are confidential and cannot be posted online. Researchers interested in using these data should apply to the Centers for Medicare and Medicaid Services (CMS).The code is designed to access these data via a Subversion repository we designed for this project. Researchers wishing to replicate our results will need to obtain their own copy of the data by applying for a Data Use Agreement through the Research Data Assistance Center (ResDAC). More information on obtaining the restricted data through ResDAC can be found at this location: https://www.resdac.org/research-identifiable-files-rif-requests. Florida dataFlorida discharge data is from the Florida Agency for Health Care Administration (AHCA) https://ahca.myflorida.com/ .
DOI: 10.5281/zenodo.18022929
title: Lista do corpus e planilha de trechos referentes ao estudo sobre a representação discursiva de Yglésio Moyses no podcast Abrindo o Verbo

description: Planilha 1: Trata-se de conteúdo acessível publicamente na internet, utilizado exclusivamente para fins de análise discursivo-crítica, sem redistribuição do material audiovisual integral.

 

Planilha 2: Os excertos correspondem apenas aos trechos citados e analisados no artigo A representação discursiva de Yglésio Moysés (PRTB) no podcast “Abrindo o Verbo”: uma análise sob a ótica da ADC, acompanhados de referência temporal precisa (timecode), sem redistribuição do conteúdo integral do vídeo.
DOI: 10.5281/zenodo.17832579
title: DATASET - Heterozygous TREM2 (p.W44X) and PSEN1 (p.A431T) mutations in two Peruvian Families with Familial Alzheimer's Disease: Expanding the Genetic Landscape in Underrepresented Populations

description: This record contains alignment data and figures related to the manuscript: "Heterozygous TREM2 (p.W44X) and PSEN1 (p.A431T) mutations in two Peruvian Families with Familial Alzheimer’s Disease: Expanding the Genetic Landscape in Underrepresented Populations".

Abstract

Alzheimer’s disease (AD) accounts for up to 70% of all dementia cases, affecting an estimated 23–35 million people worldwide. According to the World Health Organization (WHO), the number of AD cases in Latin America, including Peru, is expected to quadruple by 2050. However, these populations remain underrepresented in research, diagnostics, and care. Early-onset Alzheimer’s disease (EOAD), characterised by symptom onset before the age of 65, has been shown to have a strong genetic component, making it valuable for genetic studies. Identifying EOAD-associated mutations in underrepresented populations is crucial for uncovering pathogenic variants that may provide new insights into the disease’s mechanisms. In this article, we present two Peruvian families with early and late onset AD in whom Whole-Exome sequencing (WES) revealed heterozygous variants associated with AD.
DOI: 10.15468/u89ezy
title: Digital Catalogue of Biodiversity of Poland — Animalia: Mollusca

description: The dataset presents a chapter of the &lt;a href='https://www.gbif.org/dataset/search?keyword=Digital%20Catalogue%20of%20Biodiversity%20of%20Poland'&gt;Digital Catalogue of Biodiversity of Poland&lt;/a&gt;, comprising taxa of (infra)specific level, reported from Poland in the present administrative boundaries of the country. In this instance of the Catalogue, the list includes only taxa of confirmed occurrence, ignoring doubtful records that need verification as well as synonyms and other not valid names. The occurrence of each species is supported by a published reference.Authorship: Bivalvia - Paweł Koperski; Gastropoda - Małgorzata Proćków, Elżbieta Kuźnik-Kowalska (terrestrial fauna), Paweł Koperski (freshwater and marine fauna).
DOI: 10.5281/zenodo.18023175
title: Risk Models for Les Argilliez pilot site of TRIQUETRA

description: The list of risk quantification models produced for the pilot site of Les Argilliez in the context of TRIQUETRA, including: 



chemica analysis

geomorphological maps

projected changes.