cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/55fd0ed9756dc98de2e84ae6ddf543b7
title: BioCON Biomass Cover and SLA 1998-2022

description: The data and code in this package are associated with the analysis for a manuscript titled "Elevated CO2 and N gradually weaken the influence of diversity on ecosystem stability". The files include 25 years of data on species specific cover and biomass, total aboveground biomass, and realized species richness from 260 plots of the BioCON experiment. It also includes treatment specific means of specific leaf area (SLA) for plant species in BioCON.
DOI: 10.48577/jpl.af87rw
title: The Impact of Differences in Retrieval Algorithms between Processing Centers on GNSS Radio Occultation Refractivity Retrievals in the Planetary Boundary Layer

description: GNSS radio occultation (GNSS RO) performance in the planetary boundary layer is strongly dependent on retrieval algorithms. In this work, we explore how differences in retrieval methodology across three major processing centers of GNSS RO data—NASA JPL, ROM SAF, and UCAR—impact refractivity retrievals in the planetary boundary layer. Using a shared base of occultations from the FORMOSAT-3/COSMIC-1 GNSS RO mission, we identify key differences between the three processing 5 centers that are especially strong in the regions of frequent super-refraction. We find that the minimum penetration height allowed by each processing center is correlated with the amount of super-refraction, resulting in poorer penetration and higher refractivity biases in the Tropics. We found JPL to have the most conservative minimum height in this region at 1 km, followed by ROM SAF (640 m), and UCAR (420 km). We identify two key geopotential heights — 0.8 km and 2.6 km — to sample the global distribution of inter-center refractivity bias, finding differences of 0.3–0.5% in the Tropics. We also find 10 negative refractivity biases of up to −4% relative to ERA5 reanalysis in regions of persistent high stratocumulus coverage, and areas along the descending branch of the Hadley circulation, with negligible bias along the intertropical convergence zone. A comparison to ERA5 also reveals areas of weak (0.2–0.5%) positive refractivity biases in polar regions. We hypothesize potential causes for these biases based on truncation schemes, radio-holographic filtering choices, and quality control, and identify findings deserving of further investigation.
DOI: 10.48577/jpl.eybtec
title: Mapping Magnetic Fields from Clouds to Cores with PRIMAger

description: High-resolution, wide-area mapping of magnetic field geometries within molecular clouds, and the star20 forming filaments and cores within them, is crucial in order to understand the role of magnetic fields in the star formation process. We therefore propose an unbiased survey of star-forming molecular clouds within 0.5 kpc of the Earth in polarized light with the PRIMAger Polarimetry Imager. We will map magnetic fields over entire molecular clouds at linear resolutions of ∼ 10−3−10−2 pc (∼ 103−104 au) in PRIMAger Bands PPI1 – PPI4, thereby resolving magnetic field structure both within individual star-forming filaments and cores, and in the most diffuse regions of molecular clouds. These multi-wavelength polarimetric observations will allow us to systematically investigate both the wide range of open questions about the role of magnetic fields in star formation and the evolution of the interstellar medium, and interstellar dust grain properties. The time required to map the area observed by the Herschel Gould Belt Survey (160 deg2 ) to the cirrus confusion limit in polarized light is 1325 hours. This will give a 5-σ detection of 20% polarized low-density cirrus emission, with surface brightnesses in polarized intensity of 1.0–2.4 MJy/sr across the PRIMAger bands, and will ensure detection of polarized emission at all higher column densities. This time estimate can be simply scaled up in order to map magnetic fields in larger sample of molecular clouds, including more distant regions of higher-mass star formation
DOI: 10.48577/jpl.gbn47s
title: Impact of solar activity on short period ionospheric oscillations

description: We study the characteristics of the Earth’s ionosphere short period oscillations over multiple years spanning two solar maxima. We analyze the ground-based Global Navigation Satellite System (GNSS) vertical total electron content (TEC) at locations close to the magnetic equator in South America and India, as well as in North America, from January 2000 through December 2017. We use Morlet wavelets to spectrally analyze the ionospheric TEC variability over solar cycles 23 and 24 and find that the ionosphere exhibits dominant modes of oscillation at the same periods (5–6 days) as planetary waves. Interestingly, these oscillations manifest as 2–3 TECU fluctuations, are enhanced during spring, and show strong covariations with the 10.7 cm solar flux. We hypothesize that, once these short-period oscillations are formed, the solar activity drives their strength with distinct enhancements during solar maxima and pronounced suppressions at the transition from the solar cycle 23 to 24.
DOI: 10.48577/jpl.89j0m8
title: Tropical Inundation Does Not Explain 2020-2022 Methane Surge

description: Atmospheric methane is rapidly increasing, and the role of tropical wetlands in the global methane budget 12 raises concerns about a potential climate-wetland-methane feedback. Here we use Cyclone Global 13 Navigation Satellite System observations to map monthly inundation dynamics across the tropics (37.4 ºS 14 to 37.4 ºN) at high resolution (0.01º × 0.01º) over five years (2018–2023), including cloud and vegetation15 covered regions. Our results reveal large seasonal and interannual variations in wetland extent not captured 16 in traditional inundation products, with strong El Niño-Southern Oscillation correlations. Tropical wetland 17 models driven by these inundation observations, independent precipitation products, or constrained by total 18 water observations do not show growth in wetland methane emissions from 2020-2022. This suggests the 19 2020-2022 growth in methane is best explained by other methane source or sink mechanisms, or that non20 hydrological controls are more important in tropical wetland methane emissions than currently understood.
DOI: 10.48577/jpl.nouwlw
title: The Star Formation in Radio Survey: Adding 90GHz Data to 3–33GHz Observations of Star-forming Regions in Nearby Galaxies

description: We present new 90 GHz continuum imaging of 119 star-forming regions in 30 nearby galaxies observed with the bolometer array MUSTANG-2 operating on the Robert C. Byrd Green Bank Telescope as part of the Star Formation in Radio Survey. The 90 GHz data were combined with 3, 15, and 33 GHz data taken previously by the Karl G. Jansky Very Large Array to decompose radio spectra on scales ranging from ≈ 0.6 − 2.4 kpc with a median of ≈0.8 kpc into its synchrotron, free-free, and thermal dust emission components. This was done using three different fitting scenarios: (i) a simple power law fit, (ii) Markov Chain Monte Carlo (MCMC) fitting with thermal dust and 90 GHz data, and (iii) MCMC fitting without thermal dust and 90 GHz data. For these three cases, we find a median thermal fraction at 33 GHz of (i) 86 ± 2.7% with a scatter of 20%, (ii) 76 ± 2.8% with a scatter of 25%, and (iii) 84 ± 2.1% with a scatter of 18%. In the case of including 90 GHz data (second scenario), we find an average 10% lower median thermal fraction estimate at 33 GHz. We conclude that a simple power law fit of data from 3 to 33 GHz gives reliable thermal fraction estimates to within 10%. Furthermore, the 33 GHz emission is dominated by free-free emission, not thermal dust, on sub-kiloparsec scales.
DOI: 10.15468/dl.g5a2g4
title: Occurrence Download

description: A dataset containing 50 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, India, Myanmar, Nepal, Bhutan, Bangladesh, Pakistan, Afghanistan)",
 "TaxonKey is Pseudophilotes vicrama (Moore, 1865)"
 ]
}

 The dataset includes 50 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048986-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15675906
title: Indian Stroke  Speech Corpus

description: ================================================================

Indian Stroke Speech Corpus

==================================================================

Indian stroke speech corpus consists of  speech  recorded  from  50  stroke patients  and  50  healthy  controls. 

These  recordings are  carried out in Indian hospital  environments. The  development of the  speech  corpus  has  been  carried out in collaboration wit h all india institute of  medical  science (AIIMS) Managalagiri

The  details  of the  corpus  is  avaialble  in the published  paper titled "Analysis and development of clinically recorded dysarthric speech corpus for patients affected with various stroke conditions"

The  corpus  consists of  three tyes of  utterances: 5 Vowels, 3 words and  10  sentences (in Telugu). Due  to  the  severity  of  stroke,  some patients could not  utterred all sentences.    

 

 

 

 

Researchers interested in dysarthric speech classification are encouraged to utilize the corpus for academic research purposes.

 

If  you are  using  the dataset  please cite the paper  in  the  following format:

 

Oindrila Banerjee, K.V.N. Sita Mahalakshmi, M.V.S. Jyothi, D. Govind, U.K. Rakesh, A. Rajeev, K. Samudravijaya, Akhilesh Kumar Dubey, Suryakanth V. Gangashetty,

Analysis and development of clinically recorded dysarthric speech corpus for patients affected with various stroke conditions,

Neuroscience Informatics, Volume 5, Issue 2, 2025,

100198, ISSN 2772-5286,

https://doi.org/10.1016/j.neuri.2025.100198. 

 

 

-----------------Note-----------------

-->Currently, the shared corpus is limited to use for classification between dysarthric speech and healthy controls. The labels indicating stroke severity, based on the NIHSS scale, will be released in due course.

---> The  preliminary, dysarthric  speech classsification results are  reported  in  our  published  paper

--> For  further clarifications you can  mail me in the following  email addresses:

 

Email : d_govind@kluniversity.in

govinddmenon@gmail.com

 

Name: Dr. Govind D

Professor

Department of  Computer Science and Engineering

K. L. University, Vaddeswaram, A.P, India

--------------------------------
DOI: 10.5281/zenodo.15675907
title: Indian Stroke  Speech Corpus

description: ================================================================

Indian Stroke Speech Corpus

==================================================================

Indian stroke speech corpus consists of  speech  recorded  from  50  stroke patients  and  50  healthy  controls. 

These  recordings are  carried out in Indian hospital  environments. The  development of the  speech  corpus  has  been  carried out in collaboration wit h all india institute of  medical  science (AIIMS) Managalagiri

The  details  of the  corpus  is  avaialble  in the published  paper titled "Analysis and development of clinically recorded dysarthric speech corpus for patients affected with various stroke conditions"

The  corpus  consists of  three tyes of  utterances: 5 Vowels, 3 words and  10  sentences (in Telugu). Due  to  the  severity  of  stroke,  some patients could not  utterred all sentences.    

 

 

 

 

Researchers interested in dysarthric speech classification are encouraged to utilize the corpus for academic research purposes.

 

If  you are  using  the dataset  please cite the paper  in  the  following format:

 

Oindrila Banerjee, K.V.N. Sita Mahalakshmi, M.V.S. Jyothi, D. Govind, U.K. Rakesh, A. Rajeev, K. Samudravijaya, Akhilesh Kumar Dubey, Suryakanth V. Gangashetty,

Analysis and development of clinically recorded dysarthric speech corpus for patients affected with various stroke conditions,

Neuroscience Informatics, Volume 5, Issue 2, 2025,

100198, ISSN 2772-5286,

https://doi.org/10.1016/j.neuri.2025.100198. 

 

 

-----------------Note-----------------

-->Currently, the shared corpus is limited to use for classification between dysarthric speech and healthy controls. The labels indicating stroke severity, based on the NIHSS scale, will be released in due course.

---> The  preliminary, dysarthric  speech classsification results are  reported  in  our  published  paper

--> For  further clarifications you can  mail me in the following  email addresses:

 

Email : d_govind@kluniversity.in

govinddmenon@gmail.com

 

Name: Dr. Govind D

Professor

Department of  Computer Science and Engineering

K. L. University, Vaddeswaram, A.P, India

--------------------------------
DOI: 10.5281/zenodo.15675949
title: Group Emotion Recognition
DOI: 10.5281/zenodo.15676017
title: DisturbanceSizeScaling

description: This folder holds the data required to make our Bayesian models in the paper The Impact of Disturbance on Tree Size Distributions in the United States. The link to the github code to use with this data is https://doi.org/10.5281/zenodo.15664089.

# FIA_Disturbance_DataClean_TRTCDThis csv file selects just those plots that were "treated" according to the FIA classification system. Possible treatments are

| Code | Description ||------|-------------|| 00   | **No observable treatment**. || 10   | **Cutting** – The removal of one or more trees from a stand. || 20   | **Site preparation** – Clearing, slash burning, chopping, disking, bedding, or other practices clearly intended to prepare a site for either natural or artificial regeneration. || 30   | **Artificial regeneration** – Following a disturbance or treatment (usually cutting), a new stand where at least 50 percent of the live trees present resulted from planting or direct seeding. || 40   | **Natural regeneration** – Following a disturbance or treatment (usually cutting), a new stand where at least 50 percent of the live trees present (of any size) were established through the growth of existing trees and/or natural seeding or sprouting. || 50   | **Other silvicultural treatment** – The use of fertilizers, herbicides, girdling, pruning, or other activities (not covered by codes 10-40) designed to improve the commercial value of the residual stand; or chaining, which is a practice used on woodlands to encourage wildlife forage. |

We use this file to know which plots to remove from our data. We want to fit a Bayesian model to only those plots that had no observable treatment.

# slope_TPA_UNADJ_timesincedist_allvars_ecoregionThis csv file selects just those plots that we found had significant impacts of disturbance (animal grazing and ground fire). We look at the effect of time since disturbance on the size-abundance slope. The calculated slope for each plot is based on data that has been corrected using the TPA_UNADJ factor.

# slope_TPAUNADJcorrectedThis csv file filters plots to those that fit our criteria as specified in our paper. The calculated slope for each plot is based on data that has been corrected using the TPA_UNADJ factor.

# slope_xmin12.7cm_xmax50cm_recentclip.csvThis csv file holds information about each plot that is useful for the actual modeling process (e.g., Disturbance Type)

# tnc_adjacencymatrixThis csv file is an adjacency matrix that is used to tell brms which ecoregions are adjacent to which ecoregions. It provides the data necessary to set up the spatial autocorrelation model
DOI: 10.5281/zenodo.15676018
title: DisturbanceSizeScaling

description: This folder holds the data required to make our Bayesian models in the paper The Impact of Disturbance on Tree Size Distributions in the United States. The link to the github code to use with this data is https://doi.org/10.5281/zenodo.15664089.

# FIA_Disturbance_DataClean_TRTCDThis csv file selects just those plots that were "treated" according to the FIA classification system. Possible treatments are

| Code | Description ||------|-------------|| 00   | **No observable treatment**. || 10   | **Cutting** – The removal of one or more trees from a stand. || 20   | **Site preparation** – Clearing, slash burning, chopping, disking, bedding, or other practices clearly intended to prepare a site for either natural or artificial regeneration. || 30   | **Artificial regeneration** – Following a disturbance or treatment (usually cutting), a new stand where at least 50 percent of the live trees present resulted from planting or direct seeding. || 40   | **Natural regeneration** – Following a disturbance or treatment (usually cutting), a new stand where at least 50 percent of the live trees present (of any size) were established through the growth of existing trees and/or natural seeding or sprouting. || 50   | **Other silvicultural treatment** – The use of fertilizers, herbicides, girdling, pruning, or other activities (not covered by codes 10-40) designed to improve the commercial value of the residual stand; or chaining, which is a practice used on woodlands to encourage wildlife forage. |

We use this file to know which plots to remove from our data. We want to fit a Bayesian model to only those plots that had no observable treatment.

# slope_TPA_UNADJ_timesincedist_allvars_ecoregionThis csv file selects just those plots that we found had significant impacts of disturbance (animal grazing and ground fire). We look at the effect of time since disturbance on the size-abundance slope. The calculated slope for each plot is based on data that has been corrected using the TPA_UNADJ factor.

# slope_TPAUNADJcorrectedThis csv file filters plots to those that fit our criteria as specified in our paper. The calculated slope for each plot is based on data that has been corrected using the TPA_UNADJ factor.

# slope_xmin12.7cm_xmax50cm_recentclip.csvThis csv file holds information about each plot that is useful for the actual modeling process (e.g., Disturbance Type)

# tnc_adjacencymatrixThis csv file is an adjacency matrix that is used to tell brms which ecoregions are adjacent to which ecoregions. It provides the data necessary to set up the spatial autocorrelation model
DOI: 10.6084/m9.figshare.29329448
title: TT KPI data from the pilot and control groups of TT Scenario 1 - focus on Max Gross Income

description: Исследование эмпирически обосновывает применение кластерных стратегий для управления рентабельностью и потерями в федеральных FMCG-сетях России. На основе адаптированного подхода A/B-тестирования в офлайн ритейле в 117 торговых точках (ТТ) сети «Магнит» (октябрь 2022 – июнь 2023) исследованы два сценария кластерных стратегий: 1) стратегия «максимизации Валовой доходности (ВД)» и 2) стратегия оптимального баланса между «ВД и розничным товарооборотом (РТО)». Изменения включали кластерную оптимизацию ассортиментных матриц (АМ) с заменой неликвидных SKU категории FRESH на высокомаржинальные безалкогольные/слабоалкогольные напитки (БАН/САН) в холодильных горках (ХГ) и оценку влияния на KPI (EBITDA, товарные запасы, доступность).The study empirically substantiates the use of cluster strategies to manage profitability and losses in Russia's federal FMCG networks. Based on an adapted A/B testing approach in offline retail in 117 Magnit retail outlets (October 2022 - June 2023), two scenarios of cluster strategies were investigated.: 1) the strategy of "maximizing Gross profitability (GD)" and 2) the strategy of optimal balance between "GD and retail turnover (RTO)". The changes included cluster optimization of assortment matrices (AM) with the replacement of illiquid SKUs of the FRESH category with high-margin soft/low-alcohol beverages (BAN/SAN) in refrigerated slides (HC) and an assessment of the impact on KPIs (EBITDA, inventories, availability).
DOI: 10.6084/m9.figshare.29329448.v1
title: TT KPI data from the pilot and control groups of TT Scenario 1 - focus on Max Gross Income

description: Исследование эмпирически обосновывает применение кластерных стратегий для управления рентабельностью и потерями в федеральных FMCG-сетях России. На основе адаптированного подхода A/B-тестирования в офлайн ритейле в 117 торговых точках (ТТ) сети «Магнит» (октябрь 2022 – июнь 2023) исследованы два сценария кластерных стратегий: 1) стратегия «максимизации Валовой доходности (ВД)» и 2) стратегия оптимального баланса между «ВД и розничным товарооборотом (РТО)». Изменения включали кластерную оптимизацию ассортиментных матриц (АМ) с заменой неликвидных SKU категории FRESH на высокомаржинальные безалкогольные/слабоалкогольные напитки (БАН/САН) в холодильных горках (ХГ) и оценку влияния на KPI (EBITDA, товарные запасы, доступность).The study empirically substantiates the use of cluster strategies to manage profitability and losses in Russia's federal FMCG networks. Based on an adapted A/B testing approach in offline retail in 117 Magnit retail outlets (October 2022 - June 2023), two scenarios of cluster strategies were investigated.: 1) the strategy of "maximizing Gross profitability (GD)" and 2) the strategy of optimal balance between "GD and retail turnover (RTO)". The changes included cluster optimization of assortment matrices (AM) with the replacement of illiquid SKUs of the FRESH category with high-margin soft/low-alcohol beverages (BAN/SAN) in refrigerated slides (HC) and an assessment of the impact on KPIs (EBITDA, inventories, availability).
DOI: 10.2908/tgs00005
title: Produit intérieur brut régional (SPA par habitant), par région NUTS 2
title: Regional gross domestic product (PPS per inhabitant) by NUTS 2 region
title: Regionales Bruttoinlandsprodukt (KKS je Einwohner), nach NUTS-2-Region

description: Das BIP (Bruttoinlandsprodukt) ist ein Indikator für die Produktionstätigkeit eines Landes oder einer Region. Es entspricht dem Gesamtwert der produzierten Waren und Dienstleistungen abzüglich des Werts der Waren und Dienstleistungen, die bei ihrer Produktion als Vorleistungen verwendet werden. Wird das BIP in KKS (Kaufkraftstandards) ausgedrückt, so werden die Unterschiede der Preisniveaus zwischen den Ländern ausgeschaltet. Berechnungen auf Basis je Einwohner ermöglichen der Vergleich von Volkswirtschaften und Regionen von erheblich unterschiedlicher absoluter Größe. Das regionale BIP je Einwohner in KKS ist die zentrale Variable zur Festlegung der Förderfähigkeit der NUTS Ebene 2 Regionen im Rahmen der Strukturpolitik der Europäischen Union.
description: GDP (gross domestic product) is an indicator of the output of a country or a region. It reflects the total value of all goods and services produced less the value of goods and services used for intermediate consumption in their production. Expressing GDP in PPS (purchasing power standards) eliminates differences in price levels between countries. Calculations on a per inhabitant basis allow for the comparison of economies and regions significantly different in absolute size. GDP per inhabitant in PPS is the key variable for determining the eligibility of NUTS 2 regions in the framework of the European Union's structural policy.
description: Le PIB (produit intérieur brut) est un indicateur de la production d'un pays ou d'une région. Il reflète la valeur totale de tous les biens et services produits, diminué de la valeur des biens et services utilisés dans la consommation intermédiaire pour leur production. En exprimant le PIB en SPA (standards de pouvoir d'achat), on élimine les différences de niveaux de prix entre les pays. Les calculs par habitant permettent de comparer des économies et des régions présentant d'importantes différences en taille absolue. Le PIB régional par habitant en SPA est la variable principale pour déterminer si les régions du niveau NUTS 2 sont susceptibles de bénificier d'une aide dans la cadre de la politique structurelle de l'Union Européenne.