cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/pmwdb7d74s
title: Risk Preference and Efficiency: A Two-Stage SNP-GMM and Stochastic-Frontier Approach

description: This dataset and code (Python and Stata) are associated with the article "Risk Preference and Efficiency: A Two-Stage SNP-GMM and Stochastic-Frontier Approach in the Renewable Sector".
DOI: 10.17632/pmwdb7d74s.1
title: Risk Preference and Efficiency: A Two-Stage SNP-GMM and Stochastic-Frontier Approach

description: This dataset and code (Python and Stata) are associated with the article "Risk Preference and Efficiency: A Two-Stage SNP-GMM and Stochastic-Frontier Approach in the Renewable Sector".
DOI: 10.17632/mnhkn6rrx8
title: NIFTY50 Employee Productivity FY2024

description: This dataset provides a comprehensive analysis of employee productivity across all companies included in the NIFTY-50 index for the financial year 2023–24. The objective of the dataset is to evaluate how efficiently India’s largest listed companies utilize their human capital to generate revenue and profits. As Indian companies do not disclose a direct measure of employee productivity, the dataset relies on widely accepted financial proxies derived from publicly available and audited disclosures, making it suitable for academic and analytical use.

The dataset covers 50 companies across diverse sectors, including banking and financial services, information technology, FMCG, pharmaceuticals, automobiles, oil and gas, power utilities, metals, cement, telecom, insurance, retail, infrastructure, and consumer technology. This broad coverage allows for both firm-level and sector-level analysis of productivity patterns within the Indian corporate landscape. All financial data has been taken from FY2023–24 annual reports, investor presentations, and NSE filings, ensuring reliability and consistency. No third-party estimates or assumptions have been incorporated.

Key variables included in the dataset are company name, sector classification, total revenue, profit after tax (PAT), total number of employees, revenue per employee, and PAT per employee. Revenue and PAT are expressed in ₹ crore, while productivity indicators are calculated in ₹ lakh per employee. Revenue per employee serves as a measure of operational output generated by each employee, while PAT per employee reflects the profitability contribution of the workforce after accounting for costs and taxes. These metrics are commonly used in equity research, strategic analysis, and academic studies to assess human-capital efficiency.

The dataset highlights significant variation in productivity across sectors. Asset-heavy industries such as oil, power, and utilities tend to exhibit very high revenue per employee due to large-scale operations and capital intensity, whereas IT services and consumer-oriented firms display comparatively lower but more human-capital-driven productivity levels. As a result, intra-sector comparisons are more meaningful than direct cross-sector comparisons, and the dataset should be interpreted with this context in mind. Loss-making firms are also included, allowing productivity challenges to be observed through negative PAT per employee values.

While the dataset offers a robust view of employee productivity using standardized proxies, it has certain limitations. It does not account for differences in outsourcing, automation, or contractual labor, and employee headcounts are based on reported figures as of the financial year end.
DOI: 10.17632/mnhkn6rrx8.1
title: NIFTY50 Employee Productivity FY2024

description: This dataset provides a comprehensive analysis of employee productivity across all companies included in the NIFTY-50 index for the financial year 2023–24. The objective of the dataset is to evaluate how efficiently India’s largest listed companies utilize their human capital to generate revenue and profits. As Indian companies do not disclose a direct measure of employee productivity, the dataset relies on widely accepted financial proxies derived from publicly available and audited disclosures, making it suitable for academic and analytical use.

The dataset covers 50 companies across diverse sectors, including banking and financial services, information technology, FMCG, pharmaceuticals, automobiles, oil and gas, power utilities, metals, cement, telecom, insurance, retail, infrastructure, and consumer technology. This broad coverage allows for both firm-level and sector-level analysis of productivity patterns within the Indian corporate landscape. All financial data has been taken from FY2023–24 annual reports, investor presentations, and NSE filings, ensuring reliability and consistency. No third-party estimates or assumptions have been incorporated.

Key variables included in the dataset are company name, sector classification, total revenue, profit after tax (PAT), total number of employees, revenue per employee, and PAT per employee. Revenue and PAT are expressed in ₹ crore, while productivity indicators are calculated in ₹ lakh per employee. Revenue per employee serves as a measure of operational output generated by each employee, while PAT per employee reflects the profitability contribution of the workforce after accounting for costs and taxes. These metrics are commonly used in equity research, strategic analysis, and academic studies to assess human-capital efficiency.

The dataset highlights significant variation in productivity across sectors. Asset-heavy industries such as oil, power, and utilities tend to exhibit very high revenue per employee due to large-scale operations and capital intensity, whereas IT services and consumer-oriented firms display comparatively lower but more human-capital-driven productivity levels. As a result, intra-sector comparisons are more meaningful than direct cross-sector comparisons, and the dataset should be interpreted with this context in mind. Loss-making firms are also included, allowing productivity challenges to be observed through negative PAT per employee values.

While the dataset offers a robust view of employee productivity using standardized proxies, it has certain limitations. It does not account for differences in outsourcing, automation, or contractual labor, and employee headcounts are based on reported figures as of the financial year end.
DOI: 10.17632/crwytdnsdt
title: P2X3-NAM

description: Numeric data for patch clamp recording and MD simulations
DOI: 10.17632/crwytdnsdt.2
title: P2X3-NAM

description: Numeric data for patch clamp recording and MD simulations
DOI: 10.34740/kaggle/dsv/14677534
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.17632/gv9x8kjxgd
title: NMR data of CPT prodrugs for CES1/CES2 cascade activation

description: 1H NMR and 13C NMR data of CPT-SA-C6 to CPT-SA-C12 and CPT-SA.
DOI: 10.17632/gv9x8kjxgd.1
title: NMR data of CPT prodrugs for CES1/CES2 cascade activation

description: 1H NMR and 13C NMR data of CPT-SA-C6 to CPT-SA-C12 and CPT-SA.
DOI: 10.17632/7p5ymsyjs4
title: Experimental Dataset for Thermoshock-Damaged RC Columns Rehabilitated with Grouting and GFRP Confinement

description: This dataset contains the processed experimental data from axial monotonic and cyclic loading tests on reinforced concrete columns subjected to thermoshock damage and rehabilitated using injection grouting and GFRP confinement. The files include load–displacement responses, peak load values, stiffness degradation, ductility indices, and energy dissipation parameters for control, damaged, and strengthened specimens. The dataset supports the findings reported in the associated manuscript on post-fire structural rehabilitation.
DOI: 10.17632/7p5ymsyjs4.1
title: Experimental Dataset for Thermoshock-Damaged RC Columns Rehabilitated with Grouting and GFRP Confinement

description: This dataset contains the processed experimental data from axial monotonic and cyclic loading tests on reinforced concrete columns subjected to thermoshock damage and rehabilitated using injection grouting and GFRP confinement. The files include load–displacement responses, peak load values, stiffness degradation, ductility indices, and energy dissipation parameters for control, damaged, and strengthened specimens. The dataset supports the findings reported in the associated manuscript on post-fire structural rehabilitation.
DOI: 10.17632/d2grcmxwfs
title: Population, morphological characteristics and CaCO3 production of planktonic foraminifera at Site U1486 since ~46ka

description: Here, we provide quantified CaCO3 production of planktonic foraminifera at IODP Site U1486, which is located at 2.37 ºS, 144.60 ºE with a water depth of 1332 m, in the western tropical Pacific since 46 ka. This dataset aims to reveal the mechanisms of planktonic foraminiferal calcification and the CaCO3 pump in the western tropical Pacific since 46 ka, as well as their climatic effects.
The U1486 file contains stratigraphic and chronological information for Site U1486. 
The Assemblage file contains planktonic foraminiferal assemblage count data. The planktonic foraminiferal assemblage count data were generated by counting of about 300 specimens in the &gt;150-μm size fraction. A total of 48 samples containing an average of 357 specimens apiece was analyzed.
The Shell flux flie contains planktonic foraminiferal flux data. Planktonic foraminiferal shell flux (tests cm−2 kyr−1) refer to the absolute number of exported foraminiferal shells per unit area per unit time. 
The Shell area file contains mean individual shell silhouette area data. The silhouette area was measured by using images taken at a known magnification with a Leica S9D microscope and integrated image analysis software (LAMOS Pro).
The Shell area density file contains mean individual area density area data. The mean area density is the ratio of the shell weight (µg) to its silhouette area (µm2), and is representative of calcification density. Shells were weighed using a Sartorius MSA6.6S-0CE-DM microbalance (precision &lt;1 μg).
The Shell MAR file contains planktonic foraminiferal mass accumulation rate data. Shell MAR (mg cm−2 kyr−1) refer to the dry mass of exported foraminiferal shells per unit area per unit time.
This dataset encompasses 18 planktonic foraminiferal species (&gt; 150 μm), including Globigerinoides ruber, Neogloboquadrina dutertrei, Globigerinita glutinata, Pulleniatina obliquiloculata, Trilobatus sacculifer, Trilobatus trilobus, Globigerina falconensis, Globigerina bulloides, Globigerinella siphonifera, Globorotalia tumida, Globorotalia menardii, Globorotalia ungulata, Globigerinella calida, Globigerina rubescens, Globigerinoides tenellus, Globorotalia crassaformis, Globigerinoides conglobatus and Orbulina universa.
DOI: 10.17026/ar/gng7am
title: Plangebied sportpark Naastenbest te Best, gemeente Best .
title: Archeologisch vooronderzoek: een bureauonderzoek en inventariserend veldonderzoek (verkennend booronderzoek).
title: RAAP Rapport 7032

description: &lt;p&gt; Aanleiding
&lt;p&gt; In opdracht van Kybys B.V. heeft RAAP in februari en maart 2024 een archeologisch vooronderzoek in
de vorm van een bureauonderzoek en inventariserend veldonderzoek (verkennend booronderzoek)
uitgevoerd voor het plangebied sportpark Naastenbest te Best in de gemeente Best. In het plangebied
is voorgenomen het bestaande natuurgrasveld om te bouwen naar een kunstgrasveld. Het onderzoek
vond plaats in het kader van een omgevingsvergunning.
&lt;p&gt; Resultaten
&lt;p&gt; Op basis van het bureauonderzoek is een archeologische verwachting gesteld voor nederzettingsresten
en sporen van begraving uit de periode neolithicum – vroege middeleeuwen en sporen van de
landbouw uit de periode late middeleeuwen – nieuwe tijd. Op basis van het veldwerk kon de
archeologische verwachting gehandhaafd blijven.
&lt;p&gt; Advies
&lt;p&gt; Op basis van de resultaten van dit onderzoek blijkt dat in het plangebied geen archeologische resten
bedreigd worden. De voorgenomen ingrepen beperken zich in diepte tot de ligging van de huidige
drainagebuizen. Volgens de opdrachtgever komen deze op een diepte van 50 to 60 cm -mv te liggen.
Indien er onder het bestaande drainagesysteem niet verder gegraven wordt is er naar het inzicht van
RAAP geen reden om archeologisch vervolgonderzoek uit te voeren. Het archeologisch niveau wordt
door de voorgenomen ingrepen namelijk niet getroffen. Daarom wordt in het kader van de voorgenomen
bodemingrepen geen vervolgstap uit het proces van de Archeologische Monumentenzorg (AMZ)
noodzakelijk geacht.
&lt;p&gt; Daarnaast wordt geadviseerd om de archeologische dubbelbestemming voor het plangebied te
behouden. Het plangebied is dus na goedkeuring van onderhavig onderzoek niet gevrijwaard van
toekomstig onderzoek als er in de toekomst dieper reikende ingrepen zouden plaatsvinden.
DOI: 10.17632/d2grcmxwfs.3
title: Population, morphological characteristics and CaCO3 production of planktonic foraminifera at Site U1486 since ~46ka

description: Here, we provide quantified CaCO3 production of planktonic foraminifera at IODP Site U1486, which is located at 2.37 ºS, 144.60 ºE with a water depth of 1332 m, in the western tropical Pacific since 46 ka. This dataset aims to reveal the mechanisms of planktonic foraminiferal calcification and the CaCO3 pump in the western tropical Pacific since 46 ka, as well as their climatic effects.
The U1486 file contains stratigraphic and chronological information for Site U1486. 
The Assemblage file contains planktonic foraminiferal assemblage count data. The planktonic foraminiferal assemblage count data were generated by counting of about 300 specimens in the &gt;150-μm size fraction. A total of 48 samples containing an average of 357 specimens apiece was analyzed.
The Shell flux flie contains planktonic foraminiferal flux data. Planktonic foraminiferal shell flux (tests cm−2 kyr−1) refer to the absolute number of exported foraminiferal shells per unit area per unit time. 
The Shell area file contains mean individual shell silhouette area data. The silhouette area was measured by using images taken at a known magnification with a Leica S9D microscope and integrated image analysis software (LAMOS Pro).
The Shell area density file contains mean individual area density area data. The mean area density is the ratio of the shell weight (µg) to its silhouette area (µm2), and is representative of calcification density. Shells were weighed using a Sartorius MSA6.6S-0CE-DM microbalance (precision &lt;1 μg).
The Shell MAR file contains planktonic foraminiferal mass accumulation rate data. Shell MAR (mg cm−2 kyr−1) refer to the dry mass of exported foraminiferal shells per unit area per unit time.
This dataset encompasses 18 planktonic foraminiferal species (&gt; 150 μm), including Globigerinoides ruber, Neogloboquadrina dutertrei, Globigerinita glutinata, Pulleniatina obliquiloculata, Trilobatus sacculifer, Trilobatus trilobus, Globigerina falconensis, Globigerina bulloides, Globigerinella siphonifera, Globorotalia tumida, Globorotalia menardii, Globorotalia ungulata, Globigerinella calida, Globigerina rubescens, Globigerinoides tenellus, Globorotalia crassaformis, Globigerinoides conglobatus and Orbulina universa.
DOI: 10.17632/3y8458h6pg
title: Creative problem solving in a jumping spider

description: Dataset from laboratory experimental data collection of nest construction behaviour to test Toxeus maxillosus females’ behavioral flexibility and innovativeness. 
Experimental Design:  
Spiders were introduced into experimental apparatuses the day after mating (10:00–11:00 am). Nest building processes were monitored over seven days within each experimental apparatus. Once completion of a visible three-dimensional ellipsoidal prototype nest, spiders were transferred to new test conditions.
Data Collection:
Time allocation in nest construction and nest morphological parameters obtained via videos (SONY FDR-AX60) and photographs (Nikon D750) (Supplementary Figure S2).
File Structure: 
Single .xlsx file with two integrated worksheets:
1. nesting_time: Temporal variables (units: hours) 
 * latency: Time from entering the apparatus to the start of constructing the nest;
 * nest.duration: Nest construction duration, the accumulated time spent to construct the nest cell and frame structure;
 * non.nest: Non-nest construction time, sum of the time in which the spider did not construct nest since nest construction started, including sleeping/grooming/eating.
 * total.time: Cumulative duration from introduction to nest prototype completion.
2. nest_size: Morphometric variables 
 * in.L, in.W, in.H: Inner nest cell length, width, height (cm).
 * in.AB, out.AB: Inner nest cell and outer frame projected areas (cm², ImageJ).
 * area_ratio: out.AB / in.AB (frame-to-cell area ratio).
 * ellipsoidal_volume: Nest cell considered as an ellipsoid, and its volume calculated as (4π/3) × (in.L/2) × (in.W/2) × (in.H/2) (cm³).
Shared parameters:
* family.id: Maternal family line
* mom.id: Unique female identifier
* group: Different treatment (Tube-Control; Corner-Challenge 1; Leaves-Challenge 2; Dome-Challenge 3)
DOI: 10.17632/3y8458h6pg.1
title: Creative problem solving in a jumping spider

description: Dataset from laboratory experimental data collection of nest construction behaviour to test Toxeus maxillosus females’ behavioral flexibility and innovativeness. 
Experimental Design:  
Spiders were introduced into experimental apparatuses the day after mating (10:00–11:00 am). Nest building processes were monitored over seven days within each experimental apparatus. Once completion of a visible three-dimensional ellipsoidal prototype nest, spiders were transferred to new test conditions.
Data Collection:
Time allocation in nest construction and nest morphological parameters obtained via videos (SONY FDR-AX60) and photographs (Nikon D750) (Supplementary Figure S2).
File Structure: 
Single .xlsx file with two integrated worksheets:
1. nesting_time: Temporal variables (units: hours) 
 * latency: Time from entering the apparatus to the start of constructing the nest;
 * nest.duration: Nest construction duration, the accumulated time spent to construct the nest cell and frame structure;
 * non.nest: Non-nest construction time, sum of the time in which the spider did not construct nest since nest construction started, including sleeping/grooming/eating.
 * total.time: Cumulative duration from introduction to nest prototype completion.
2. nest_size: Morphometric variables 
 * in.L, in.W, in.H: Inner nest cell length, width, height (cm).
 * in.AB, out.AB: Inner nest cell and outer frame projected areas (cm², ImageJ).
 * area_ratio: out.AB / in.AB (frame-to-cell area ratio).
 * ellipsoidal_volume: Nest cell considered as an ellipsoid, and its volume calculated as (4π/3) × (in.L/2) × (in.W/2) × (in.H/2) (cm³).
Shared parameters:
* family.id: Maternal family line
* mom.id: Unique female identifier
* group: Different treatment (Tube-Control; Corner-Challenge 1; Leaves-Challenge 2; Dome-Challenge 3)
DOI: 10.5281/zenodo.18414894
title: Data and code from: Fungivorous microarthropod richness related to stagnation of litter decay in nitrogen impacted Quercus robur forests

description: This study examined the relationship between the microarthropod community and plant litter decomposition in a 21-month litterbag experiment using Quercus robur in 9 forests vulnerable to nitrogen deposition.
DOI: 10.5281/zenodo.18414895
title: Data and code from: Fungivorous microarthropod richness related to stagnation of litter decay in nitrogen impacted Quercus robur forests

description: This study examined the relationship between the microarthropod community and plant litter decomposition in a 21-month litterbag experiment using Quercus robur in 9 forests vulnerable to nitrogen deposition.
DOI: 10.15468/dl.ewy74q
title: Occurrence Download

description: A dataset containing 9182 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Nothobranchius venustus, Pseudogobiopsis rubrimaculosa, Schistura hiranyakeshi, Leporinus villasboasorum, Leporinus enyae, Hyphessobrycon pinnistriatus, Planiliza ordensis, Planiliza tade, Neolissochilus kaladanensis, Gymnogeophagus pseudolabiatus, Gymnogeophagus mekinos, Gymnogeophagus constellatus, Gymnogeophagus missioneiro, Gymnogeophagus lipokarenos, Gymnogeophagus jaryi, Gymnogeophagus terrapurpura, Gymnogeophagus taroba, Gymnogeophagus peliochelynion, Cobitis herzegoviniensis, Teleocichla preta, Hyphessobrycon margitae, Odontocharacidium varii, Sinorhodeus microlepis, Paratanakia chii, Aenigmachanna gollum, Centropomus irae, Fluviphylax gouldingi, Fluviphylax wallacei, Pseudomugil luminatus, Pseudocorynopoma stanleyi, Pimelodella humeralis, Galaxias ornatus, Galaxias aequipinnis, Galaxias arcanus, Galaxias brevissimus, Galaxias gunaikurnai, Galaxias lanceolatus, Galaxias longifundus, Galaxias mcdowalli, Galaxias mungadhan, Galaxias oliros, Galaxias supremus, Galaxias tantangara, Galaxias terenasus, Chaetostoma floridablancaensis, Physoschistura chulabhornae, Psalidodon rutilus, Psalidodon powelli, Tetragonopterus franciscoensis, Tetragonopterus kuluene, Tetragonopterus ommatus, Tetragonopterus juruena, Varicharax nigrolineatus, Luciobarbus lanigarensis, Luciobarbus numidiensis, Luciobarbus chelifensis, Luciobarbus mascarensis, Corydoras fulleri, Homatula anteridorsalis, Homatula cryptoclathrata, Homatula nigra, Homatula berezowskii, Homatula wenshanensis, Homatula guanheensis, Vanmanenia microcephala, Vanmanenia pseudostriata, Careproctus maslenikovae, Vanmanenia orcucampus, Mylossoma albiscopum, Exostoma gaoligongense, Barbus fucini, Plataplochilus mimus, Plataplochilus pulcher, Pungitius modestus, Barbus samniticus, Parotocinclus, Badis kaladanensis, Hypostomus robertsoni, Hypostomus pastinhai, Hypostomus bimbai, Moenkhausia andrica, Mylossoma unimaculatum, Opsariichthys macrolepis, Sarcocheilichthys vittatus, Leptobotia citrauratea, Leptobotia brachycephala, Pangio bhujia, Aphaniops kruppi, Hyphessobrycon zoe, Ituglanis goya, Melanotaenia sneideri, Johnius taiwanensis, Leptobotia bellacauda, Microphysogobio luhensis, Hyphessobrycon klausanni, Eleotris bosetoi, Paratrygon orinocensis, Paratrygon parvaspina, Hylopanchax multisquamatus, Hylopanchax thysi)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 9182 records from 109 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002118-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/mtdj7p2hh2
title: Supplemental Materials: Machine Learning Applications in Androgenetic Alopecia: A Systematic Review

description: Supplemental tables for "Machine Learning Applications in Androgenetic Alopecia: A Systematic Review" including search strategy, summary of included studies and risk of bias.
DOI: 10.5281/zenodo.18432267
title: HouseInc - D3.4 Multinational Database of Housing Market Regulations

description: This database includes two components: 1) a list of all relevant legal acts concerning rent control, protection from eviction, housing rationing, stimulation of housing construction, and housing benefits; and 2) numerical coding that reflects the degree of intensity of each of these five policies. For example, the index of rent control equal to 0 means that there is no rent control, while the index being equal to 1 means that rents for most or all private rental dwellings are frozen.
DOI: 10.17632/mtdj7p2hh2.1
title: Supplemental Materials: Machine Learning Applications in Androgenetic Alopecia: A Systematic Review

description: Supplemental tables for "Machine Learning Applications in Androgenetic Alopecia: A Systematic Review" including search strategy, summary of included studies and risk of bias.