cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35078/n0tlsd/pn6rum
title: Joaquim Valadares A.jpg

description: :unav
DOI: 10.35078/e11gnq/4cmtdz
title: Nadir A.jpg

description: :unav
DOI: 10.35078/9snyyt/w4ri92
title: Nova Fazenda I B.jpg

description: :unav
DOI: 10.35078/n0tlsd/azym4a
title: Felicidade A.jpg

description: :unav
DOI: 10.35078/e11gnq/1ycumk
title: Ipanema B.jpg

description: :unav
DOI: 10.35078/9snyyt/bm7qit
title: Aliança B.jpg

description: :unav
DOI: 10.35078/n0tlsd/iocc0c
title: Santo Machado A.jpg

description: :unav
DOI: 10.35078/e11gnq/lehmxz
title: Cemig Usina A.jpg

description: :unav
DOI: 10.35078/9snyyt/e8nfmx
title: Alta Clara B.jpg

description: :unav
DOI: 10.35078/n0tlsd/fltaru
title: Pedra Redonda I B.jpg

description: :unav
DOI: 10.35078/e11gnq/xp0rmg
title: Barruada B.jpg

description: :unav
DOI: 10.35078/n0tlsd/lqopap
title: Boa Vista do Muquirana B.jpg

description: :unav
DOI: 10.35078/9snyyt/xlxzzk
title: Milagrosa B.jpg

description: :unav
DOI: 10.35078/e11gnq/xa87gf
title: Vital M. Glória B.jpg

description: :unav
DOI: 10.35078/9snyyt/nvvupa
title: Bertópolis B.jpg

description: :unav
DOI: 10.35078/n0tlsd/lptprw
title: Principe A.jpg

description: :unav
DOI: 10.35078/e11gnq/yfbhqi
title: José Pereira B.jpg

description: :unav
DOI: 10.35078/9snyyt/7h0ty9
title: Mimosa B.jpg

description: :unav
DOI: 10.35078/n0tlsd/nhpvyu
title: Ilha do Vasconcelo B.jpg

description: :unav
DOI: 10.35078/e11gnq/6axisi
title: João de A. Morais A.jpg

description: :unav
DOI: 10.35078/9snyyt/x54tt1
title: José Ferraz B.jpg

description: :unav
DOI: 10.35078/n0tlsd/bwyxg1
title: Batalha B.jpg

description: :unav
DOI: 10.35078/e11gnq/fsnbbg
title: Francisco Anizio B.jpg

description: :unav
DOI: 10.35078/9snyyt/a6r9lw
title: Altamira B.jpg

description: :unav
DOI: 10.35078/n0tlsd/gmcryr
title: Derneval Soares A.jpg

description: :unav
DOI: 10.35078/9snyyt/8nuphx
title: Bahia B.jpg

description: :unav
DOI: 10.35078/e11gnq/ow8cdw
title: Eurico Gonçalves A.jpg

description: :unav
DOI: 10.35078/n0tlsd/4s4dws
title: José Juca A.jpg

description: :unav
DOI: 10.35078/e11gnq/5xcsq8
title: Raimundo M. de Souza B.jpg

description: :unav
DOI: 10.35078/n0tlsd/lhcxts
title: Coqueiros B.jpg

description: :unav
DOI: 10.35078/9snyyt/c1slv8
title: Palestina A.jpg

description: :unav
DOI: 10.35078/n0tlsd/t4fbyw
title: José Malaquias B.jpg

description: :unav
DOI: 10.35078/9snyyt/eojdev
title: Bom Retiro A.jpg

description: :unav
DOI: 10.35078/e11gnq/fnnvsw
title: Olinto F. de Carvalho B.jpg

description: :unav
DOI: 10.35078/9snyyt/c88pq4
title: Juarez M. Fontes B.jpg

description: :unav
DOI: 10.35078/n0tlsd/wrmr17
title: Codorna A.jpg

description: :unav
DOI: 10.35078/e11gnq/2haelc
title: José Menezes B.jpg

description: :unav
DOI: 10.35078/n0tlsd/91lqta
title: Oledino Amorim B.jpg

description: :unav
DOI: 10.35078/9snyyt/xitb74
title: Provisão B.jpg

description: :unav
DOI: 10.35078/e11gnq/gimizz
title: João Basilio Filho A.jpg

description: :unav
DOI: 10.35078/9snyyt/y0vfxu
title: Morro Redondo B.jpg

description: :unav
DOI: 10.35078/e11gnq/cccxtk
title: Benvindo F. de Carvalho C.jpg

description: :unav
DOI: 10.35078/n0tlsd/dpmtsx
title: Manoel Leoné A.jpg

description: :unav
DOI: 10.35078/9snyyt/kkmnhr
title: Bainho A.jpg

description: :unav
DOI: 10.35078/n0tlsd/yxxr8f
title: Horto Brauna A.jpg

description: :unav
DOI: 10.35078/9snyyt/b0fqaz
title: Uberlândia A.jpg

description: :unav
DOI: 10.35078/e11gnq/p3k0n4
title: Pindaíba B.jpg

description: :unav
DOI: 10.35078/9snyyt/ggwbcl
title: Esplanada A.jpg

description: :unav
DOI: 10.35078/n0tlsd/jfvnln
title: Boa Sorte B.jpg

description: :unav
DOI: 10.35078/e11gnq/jmftyc
title: José D. de Oliveira B.jpg

description: :unav
DOI: 10.35078/9snyyt/sny75k
title: Aliança A.jpg

description: :unav
DOI: 10.35078/n0tlsd/t6kpww
title: Zé Eduardo B.jpg

description: :unav
DOI: 10.35078/e11gnq/r5nbri
title: Maria C. de Souza B.jpg

description: :unav
DOI: 10.35078/n0tlsd/x4unkh
title: Efigênio Modesto B.jpg

description: :unav
DOI: 10.35078/9snyyt/ibitcs
title: Maravilha I A.jpg

description: :unav
DOI: 10.35078/e11gnq/6vj9fo
title: Gil Vieira A.jpg

description: :unav
DOI: 10.35078/n0tlsd/qxov2r
title: Concesso B.jpg

description: :unav
DOI: 10.35078/9snyyt/jeowdy
title: Pedrosa B.jpg

description: :unav
DOI: 10.35078/e11gnq/nadoqe
title: Hélio de B. Carvalho B.jpg

description: :unav
DOI: 10.5281/zenodo.6597282
title: Dataset for "Best organic farming deployment scenarios for pest control: a modeling approach"

description: <strong>Organic Farming (OF) has been expanding recently around the world in response to growing consumer demand and as a response to environmental concerns. Its share of agricultural landscapes is expected to increase in the future. The effect of OF expansion on pest abundance and Conservation Biological Control (CBC) in organic and conventional fields is difficult to predict. Given the inherent complexity of CBC and the lack of CBC data across situations of OF expansion, and the probable interactions with landscape context, modeling is a useful tool to understand and forecast how pests and their control may vary during OF expansion. Here, we used a neutral spatially explicit landscape model simulating pests and predators interacting on an agricultural landscape to investigate the impact of different organic deployment strategies on the CBC. We modeled three spatial strategies of conversion of conventional fields to organic fields (Random, Isolated fields first, and Grouped fields first) in landscapes contrasting in terms of semi-natural habitat areas fragmentation. We performed simulations considering organic farming deployment, different initializations of population dynamics and various combinations of pesticides effects on pests and predators depending on the land use. </strong> <strong>Our results showed that the organic farming deployment affected more predator densities than pest densities for most combinations of landscape types and deployment strategies. Its impact was also generally stronger in organic than conventional fields and in landscapes with large amounts of fragmented seminatural habitats. Based on pest densities and the predator to pest ratio, our results suggest that a progressive organic conversion with a focus on isolated conventional fields could help promote CBC. Careful landscape planning of OF deployment appeared most necessary when OF pesticides had a low efficiency, and in landscapes with low quantities of semi-natural habitats. </strong> <strong>This daset contains simulation outputs and the R script that was used to describe, display and analyse data. The model itself can be found at https://doi.org/10.17605/OSF.IO/Z2QCX</strong>
description: Version 2: R script edited so that comments are understandable, translated into English and overall cleaned up.
DOI: 10.17863/cam.54903
title: Data for: Advancing predictions of protein stability in the solid state

description: Raw data underpinning the manuscript
DOI: 10.17863/cam.60430
title: Research Data supporting ''Terahertz pulsed imaging as a new method for investigating the liquid transport kinetics of α-alumina powder compacts''

description: Raw experimental data that was analysed and interpreted for this manuscript.
DOI: 10.17863/cam.44163
title: Research data for: Tracking Solid State Dynamics in Spray-Dried Protein Powders at Infrared and Terahertz Frequencies

description: Terahertz time-domain spectra and FT infrared spectra.
DOI: 10.5281/zenodo.6583422
title: A TripAdvisor Dataset for Dyadic Context Analysis

description: There are many contexts where dyadic data are present. In social networks, users are linked to a variety of items, defining interactions. In the social platform of TripAdvisor, users are linked to restaurants by means of reviews posted by them. Using the information of these interactions, we can get valuable insights for forecasting, proposing tasks related to recommender systems, sentiment analysis, text-based personalisation or text summarisation, among others. Furthermore, in the context of TripAdvisor there is a scarcity of public datasets and lack of well-known benchmarks for model assessment. We present six new TripAdvisor datasets from the restaurants of six different cities: London, New York, New Delhi, Paris, Barcelona and Madrid. <strong>If you use this data, please cite the following paper under submission process</strong> (preprint - arXiv) We exclusively collected the reviews written in English from the restaurants of each city. The tabular data is comprised of a set of six different CSV files, containing numerical, categorical and text features: <strong>parse_count</strong>: numerical (integer), corresponding number of extracted review by the web scraper (auto-incremental) <strong>author_id: </strong>categorical (string), univocal, incremental and anonymous identifier of the user (UID_XXXXXXXXXX) <strong>restaurant_name: </strong>categorical (string), name of the restaurant matching the review <strong>rating_review: </strong>numerical (integer), review score in the range 1-5 <strong>sample: </strong>categorical (string), indicating “positive” sample for scores 4-5 and “negative” for scores 1-3 <strong>review_id: </strong>categorical (string), univocal and internal identifier of the review (review_XXXXXXXXX) <strong>title_review: </strong>text, review title <strong>review_preview: </strong>text, preview of the review, truncated in the website when the text is very long <strong>review_full: </strong>text, complete review <strong>date: </strong>timestamp, publication date of the review in the format (day, month, year) <strong>city</strong>: categorical (string), city of the restaurant which the review was written for <strong>url_restaurant: </strong>text, restaurant url
description: This research has been financially supported in part by the Spanish Government [grant number PID2019-109238GB-C22]; by the Xunta de Galicia [grant number ED431G 2019/01 - Research Center on Information and Communication Technologies (CITIC)]; and by European Union ERDF Funds. Special recognition goes to the Spanish Ministerio de Universidades for the predoctoral FPU funds [grant number FPU19/01457]. Please notice that these data is under a CC-BY-NC 4.0 International license. You must NOT use the material for commercial purposes. For the data collection, we designed our own web scraper, selecting a mix of Scrapy python framework and Selenium web driver testing tool. Participants data have been anonymized. We added the field "author_id" as the incremental, univocal and anonymous identifier of each user (UID_XXXXXXXXXX).
DOI: 10.5281/zenodo.6583421
title: A TripAdvisor Dataset for Dyadic Context Analysis

description: There are many contexts where dyadic data are present. In social networks, users are linked to a variety of items, defining interactions. In the social platform of TripAdvisor, users are linked to restaurants by means of reviews posted by them. Using the information of these interactions, we can get valuable insights for forecasting, proposing tasks related to recommender systems, sentiment analysis, text-based personalisation or text summarisation, among others. Furthermore, in the context of TripAdvisor there is a scarcity of public datasets and lack of well-known benchmarks for model assessment. We present six new TripAdvisor datasets from the restaurants of six different cities: London, New York, New Delhi, Paris, Barcelona and Madrid. <strong>If you use this data, please cite the following paper under submission process</strong> (preprint - arXiv) We exclusively collected the reviews written in English from the restaurants of each city. The tabular data is comprised of a set of six different CSV files, containing numerical, categorical and text features: <strong>parse_count</strong>: numerical (integer), corresponding number of extracted review by the web scraper (auto-incremental) <strong>author_id: </strong>categorical (string), univocal, incremental and anonymous identifier of the user (UID_XXXXXXXXXX) <strong>restaurant_name: </strong>categorical (string), name of the restaurant matching the review <strong>rating_review: </strong>numerical (integer), review score in the range 1-5 <strong>sample: </strong>categorical (string), indicating “positive” sample for scores 4-5 and “negative” for scores 1-3 <strong>review_id: </strong>categorical (string), univocal and internal identifier of the review (review_XXXXXXXXX) <strong>title_review: </strong>text, review title <strong>review_preview: </strong>text, preview of the review, truncated in the website when the text is very long <strong>review_full: </strong>text, complete review <strong>date: </strong>timestamp, publication date of the review in the format (day, month, year) <strong>city</strong>: categorical (string), city of the restaurant which the review was written for <strong>url_restaurant: </strong>text, restaurant url
description: This research has been financially supported in part by the Spanish Government [grant number PID2019-109238GB-C22]; by the Xunta de Galicia [grant number ED431G 2019/01 - Research Center on Information and Communication Technologies (CITIC)]; and by European Union ERDF Funds. Special recognition goes to the Spanish Ministerio de Universidades for the predoctoral FPU funds [grant number FPU19/01457]. Please notice that these data is under a CC-BY-NC 4.0 International license. You must NOT use the material for commercial purposes. For the data collection, we designed our own web scraper, selecting a mix of Scrapy python framework and Selenium web driver testing tool. Participants data have been anonymized. We added the field "author_id" as the incremental, univocal and anonymous identifier of each user (UID_XXXXXXXXXX).
DOI: 10.5519/0018095
title: Beetle Drawer Scans

description: Drawer scans of beetles from the Natural History Museums (NHM) collections. These scans are a mix of work produced by volunteers at the NHM as part of the VFactor program as well as our digitisers and curators working as part of the NHM collections move program. To see the high-resolution scans visit: [https://data.nhm.ac.uk/beetle_iiif](https://data.nhm.ac.uk/beetle_iiif).
DOI: 10.5281/zenodo.6545898
title: [Tm2(Cpttt)3{C6H5(C4O6)}]2

description: The dataset contains the primary data for [Tm2(Cpttt)3{C6H5(C4O6)}]2
DOI: 10.5281/zenodo.6545897
title: [Tm2(Cpttt)3{C6H5(C4O6)}]2

description: The dataset contains the primary data for [Tm2(Cpttt)3{C6H5(C4O6)}]2
DOI: 10.5281/zenodo.6545875
title: [Tm2(Cpttt)3{C7H7(C4O6)}]2

description: The dataset contains the primary data for [Tm2(Cpttt)3{C7H7(C4O6)}]2
DOI: 10.6084/m9.figshare.19932264
title: Musculoskeletal diseases are the main cause of sick leave among oil industry workers in Brazil: results of a cohort study

description: Abstract Objective: to describe the incidence of sickness absence among workers of an oil industry in Brazil. Methods: retrospective cohort study conducted with 2,028 workers. Study data were obtained from workers’ medical records from 2012 to 2016. Indicators of absenteeism were calculated by sociodemographic characteristics, occupational characteristics, and morbidity. Results: of the participants, 87.6% were men, 49.2% with high school educational level, 46.9% aged 50 years or older, 65.1% worked in non-operational activities. The cumulative incidence of sick leave was 71.5% and its incidence rate, 25.8 per 100 person-years. We found the highest incidence rates amongst women (31.6), workers aged 50 years or older (29.9), lower educational attainment (29.2), work experience spanned 30 years or more (31.9), working in operational activities (27.9), and regular work schedule (26.1). Musculoskeletal (n=2,001), respiratory (n=1,016), and digestive diseases (n=967) were responsible for the largest number of sick leaves. The highest number of absence days was due to musculoskeletal diseases (n=11,640), followed by injuries (n=6,267) and mental disorders (n=5,042). Low back pain diagnostic was responsible for the greatest number of absence days (n=3,632). Conclusions: health programs aimed at controlling the identified morbidities should target women, those with longer work experiences, and those with lower educational attainment.
DOI: 10.6084/m9.figshare.19932264.v1
title: Musculoskeletal diseases are the main cause of sick leave among oil industry workers in Brazil: results of a cohort study

description: Abstract Objective: to describe the incidence of sickness absence among workers of an oil industry in Brazil. Methods: retrospective cohort study conducted with 2,028 workers. Study data were obtained from workers’ medical records from 2012 to 2016. Indicators of absenteeism were calculated by sociodemographic characteristics, occupational characteristics, and morbidity. Results: of the participants, 87.6% were men, 49.2% with high school educational level, 46.9% aged 50 years or older, 65.1% worked in non-operational activities. The cumulative incidence of sick leave was 71.5% and its incidence rate, 25.8 per 100 person-years. We found the highest incidence rates amongst women (31.6), workers aged 50 years or older (29.9), lower educational attainment (29.2), work experience spanned 30 years or more (31.9), working in operational activities (27.9), and regular work schedule (26.1). Musculoskeletal (n=2,001), respiratory (n=1,016), and digestive diseases (n=967) were responsible for the largest number of sick leaves. The highest number of absence days was due to musculoskeletal diseases (n=11,640), followed by injuries (n=6,267) and mental disorders (n=5,042). Low back pain diagnostic was responsible for the greatest number of absence days (n=3,632). Conclusions: health programs aimed at controlling the identified morbidities should target women, those with longer work experiences, and those with lower educational attainment.
DOI: 10.5281/zenodo.6545874
title: [Tm2(Cpttt)3{C7H7(C4O6)}]2

description: The dataset contains the primary data for [Tm2(Cpttt)3{C7H7(C4O6)}]2
DOI: 10.5281/zenodo.6545851
title: [Tm(Cpttt)2OTf]

description: The dataset contains the primary data for [Tm(Cpttt)2OTf]
DOI: 10.5281/zenodo.6545850
title: [Tm(Cpttt)2OTf]

description: The dataset contains the primary data for [Tm(Cpttt)2OTf]
DOI: 10.5281/zenodo.6545826
title: [Tm(Cpttt)2]2{C4O4(SiMe3)2}

description: The dataset contains the primary data for [Tm(Cpttt)2]2{C4O4(SiMe3)2}
DOI: 10.5281/zenodo.6545825
title: [Tm(Cpttt)2]2{C4O4(SiMe3)2}

description: The dataset contains the primary data for [Tm(Cpttt)2]2{C4O4(SiMe3)2}
DOI: 10.5281/zenodo.6545778
title: [Tm(Cpttt)2]3{H3CC3(CO2)3}

description: The dataset contains the primary data for [Tm(Cpttt)2]3{H3CC3(CO2)3}
DOI: 10.5281/zenodo.6545777
title: [Tm(Cpttt)2]3{H3CC3(CO2)3}

description: The dataset contains the primary data for [Tm(Cpttt)2]3{H3CC3(CO2)3}
DOI: 10.5281/zenodo.6545770
title: [Tm(Cpttt)2]2(C4O5)

description: The dataset contains the primary data for [Tm(Cpttt)2]2(C4O5)
DOI: 10.5281/zenodo.6545769
title: [Tm(Cpttt)2]2(C4O5)

description: The dataset contains the primary data for [Tm(Cpttt)2]2(C4O5)
DOI: 10.5281/zenodo.6545760
title: [Tm(Cpttt)2]2(CO3)

description: The dataset contains the primary data for [Tm(Cpttt)2]2(CO3)
DOI: 10.5281/zenodo.6545759
title: [Tm(Cpttt)2]2(CO3)

description: The dataset contains the primary data for [Tm(Cpttt)2]2(CO3)
DOI: 10.5281/zenodo.6545746
title: [Tm(Cpttt)2]2(CO)3

description: The dataset contains the primary data for [Tm(Cpttt)2]2(CO)3
DOI: 10.5281/zenodo.6545745
title: [Tm(Cpttt)2]2(CO)3

description: The dataset contains the primary data for [Tm(Cpttt)2]2(CO)3
DOI: 10.5281/zenodo.6545724
title: [Tm(Cpttt)2]2(CO)2

description: The dataset contains the primary data for [Tm(Cpttt)2]2(CO)2
DOI: 10.5281/zenodo.6545723
title: [Tm(Cpttt)2]2(CO)2

description: The dataset contains the primary data for [Tm(Cpttt)2]2(CO)2
DOI: 10.5281/zenodo.6545548
title: Tm(Cpttt)2, thulium bis(1,2,4-tris(tert-butyl)cyclopentadienyl)

description: The dataset contains the primary data for Tm(Cpttt)2
DOI: 10.5281/zenodo.6545547
title: Tm(Cpttt)2, thulium bis(1,2,4-tris(tert-butyl)cyclopentadienyl)

description: The dataset contains the primary data for Tm(Cpttt)2
DOI: 10.5281/zenodo.6597409
title: Frouin-et-al-Comparative metagenomics of serpentinizing systems: Supplemental Files

description: Supplemental files associated with Frouin et. al. manuscript entitled "Comparative metagenomics highlight a widespread pathway involved in the catabolism of phosphonates in marine and terrestrial serpentinizing ecosystems".
DOI: 10.5281/zenodo.6446121
title: Frouin-et-al-Comparative metagenomics of serpentinizing systems: Supplemental Files

description: Supplemental files associated with Frouin et. al. manuscript entitled "Comparative metagenomics highlight a widespread pathway involved in the catabolism of phosphonates in marine and terrestrial serpentinizing ecosystems".
DOI: 10.5281/zenodo.6595283
title: FSS7B psychophysiological data

description: SCR, PSR and SEBR data as well as data key/info, documentation and anonymizer + custom code. For private use only (not anonymized or standardized).
DOI: 10.25625/eofcki/myyhqj
title: nieschulzeFairDataCBL20220525.pdf

description: :unav
DOI: 10.25625/eofcki
title: Introduction to (FAIR-)DataManagement for CBL students

description: A short introduction about data management along the FAIR principles with outlook to DMP (data management plans)
DOI: 10.23729/4040bf86-451c-498c-9472-0808af5f8024
title: e3power_battery data_eRallyCross car
title: e3power

description: This data was aquired out of the eRallycross car (eRx) developed and constructed by the students in the School of Mechanical engineering at Turku university of applied siences. The eRx is intended to work as a test platform for the powertrains and battery systems. It has an electric motor of 180 kW performance powered by the 400V battery system including 168 lithium titanate cells in series. Acceleration of the car is around 5 seconds to 100 km/h and top speed 160 km/h using front wheel drive with limited slip differential and reduction gear. The controllers of the eRx transmit and receive sensor, safety, status and control information at speed of 0.5Mbit/s via CAN bus. Battery data transmitted by the battery monitoring system (BMS) was logged from the CAN bus and raw data saved in the MF4 format. Decoding of the data to readable format was made by using the asammdf parser and BMS manufacturer's (STW) appropriate CAN database file (DBC). The data set shows what type of parameters from the battery can be monitored. It is recorded while driving the car with a chassis dynamometer.
DOI: 10.15468/dl.w85nkw
title: Occurrence Download

description: A dataset containing 1769 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Limonium legrandii (Gaut. & Timb.-Lagr.) Erben"
 ]
}

 The dataset includes 1769 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046110-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uh386f
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leucocroton pallidus Britton & P.Wilson"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046111-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ubfsbv
title: Occurrence Download

description: A dataset containing 60 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Liparis deistelii Schltr."
 ]
}

 The dataset includes 60 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046109-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.