cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24921954.v1
title: Copy of 2420095_01-.xlsx

description: As a type of land degradation, desertification is a crucial phenomenon that negatively impacts the environment and threatens life on Earth. The desertification processes effectively influence the arid and semiarid regions and decrease the land's efficiency with increment speeds. Therefore, recognizing this phenomenon is very important. This study was conducted in The Sistan Plain, Iran, to monitor and forecast desertification using remote sensing-based indices, including land use and land cover (LULC) map, normalized differential vegetation index (NDVI), improved vegetation index (EVI), vegetation condition index (VCI), surface temperature condition index (TCI), modified normalized differential water level index (MNDWI) and salinity index (SI). In addition to satellite data, environmental indices, including standardized precipitation index (SPI) and streamflow drought index (SDI), were used. Satellite data of the study consisted of Landsat 5, and 8 images were taken in June each year over the ten years (1990-2020). Helping to comprehend the ecosystem's mechanisms and alterations before it gets irreversible, monitoring desertification is the most effective way to assess desertification processes. Because using different classification methods with the same data will yield different results, selecting an appropriate method for classifying remote sensing indices for monitoring desertification is necessary. Therefore, correct scientific assessment is required to classify the obtained results. On the other hand, the random forest (RF) method and the mixed model of automated cells and Markov chain (CA-Markov) were used to monitor desertification quantitatively and to predict the desertification condition in 2030, respectively.
DOI: 10.5066/p93f9zby
title: Digital location of monoclines and structural uplifts of the Colorado Plateau, Arizona, Colorado, New Mexico, and Utah

description: The Colorado Plateau structural province features long monoclinal flexures between uplifts and basins that are the major lines of deformation within and marginal to the Plateau. These folds are named, well-known, and have been described as part of several previous tectonic syntheses of the Colorado Plateau (Kelley, 1955; Davis, 1978; 1999). However, no digital data have ever been created that locate these folds in digital map space. This digital dataset compiles mapped locations of monoclinal folds from several geologic maps from the Colorado Plateau, most released only in “paper”, non-vector format. Fold names and their general map trace were guided by regional-scale maps that synthesize the tectonic elements of the Colorado Plateau, but specific location of the fold axes relied on detailed geologic maps, augmented by inspection of imagery and digital elevation models. Structural Uplifts on the Colorado Plateau are doubly plunging, asymmetric, elliptical shaped anticlinal features often bounded on one side by a monoclinal fold. Locations and shapes of the major structural uplifts on the Colorado Plateau were modified from regional tectonic maps (Kelley, 1955; Davis, 1978; 1999) and are included with the digital dataset primarily for reference and context. The dataset includes a geographic information system geodatabase that contains a polyline feature class of the monocline fold axes and a polygon feature class showing the general location of structural uplifts. Vector data are attributed according to the USGS National Cooperative Geologic Mapping Program’s GeMS digital geologic map schema; monoclines are attributed within a GeologicLines feature class and uplifts are attributed within an OverlayPolys feature class. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, and a Data Dictionary that duplicates the Entity and Attribute information contained in the metadata file. To maximize usability, spatial data are also distributed as shapefiles and tabular data are distributed as ascii text files in comma separated values (CSV) format.
DOI: 10.34740/kaggle/dsv/7324788
title: ML Prediction of Poverty and Malnutrition Dataset
DOI: 10.26207/gbrh-v505
title: DATASET - 	 Perceptions of Nicotine and Tobacco-Related Harms Among Healthcare Providers
DOI: 10.5066/p90dza9m
title: Data and model archive used to model and map lithium concentrations in groundwater sources of drinking water in the conterminous United States

description: This data release contains data used to develop models and maps that estimate the occurrence of lithium in groundwater used as drinking water throughout the conterminous United States. An extreme gradient boosting model was developed to estimate the most probable lithium concentration category (≤4, >4 to ≤10, >10 to ≤30, or >30 µg/L). The model uses lithium concentration data from wells located throughout the conterminous United States and predictor variables that are available as geospatial data. The model is included in this data release in the zipped folder named Model_Archive and was used to produce maps that are also included in this data release. The model input data (predictor variables) that were used to make the maps consist of a Tagged Image File (TIF) raster for each of the 20 model predictor variables and are presented in a zipped folder (Map_Input_Data.zip). The model output consists of 5 TIF raster files for public supply well depths and 5 for domestic supply well depths (presented in the zipped folder Map_Output_Data.zip), which is a probability estimate map for each of the lithium concentration categories plus a map showing the category with the highest probability within each well type.
DOI: 10.5281/zenodo.10433646
title: Revisiting the time to publication in medical education: An analysis of publication timelines between 2019-2022

description: We conducted a bibliometric study that replicated and expanded upon a prior study on publication timelines in medical education, which had heavily drawn upon an earlier study that had investigated this topic broadly across scientific disciplines. To collect our data, we referenced The History of Publication Delays by Himmelstein ( https://blog.dhimmel.com/history-of-delays/.) - an analysis of publication delays up to 2015 utilizing the entire PubMed database. We used the original code that is deposited on Himmelstein's Github account with only minor modifications. The modifications included an update for a depreciated function within a Python package and minor adjustments to account for a smaller dataset. On October 10, 2023 JC queried the eSearch e-utility, an API interface, using the updated code to download summary metadata from the National Library of Medicine (NLM) for all included articles. This dataset represents the results of articles meeting our initial inclusion criteria: published in PubMed on or between January 1, 2018 and December 31, 2022.
DOI: 10.13026/3cmh-xa68
title: Patient-level dataset to study the effect of COVID-19 in people with Multiple Sclerosis

description: Multiple Sclerosis (MS) is an inflammatory autoimmune disease of the central
nervous system, causing increased vulnerability to infections and disability
among young adults. Ever since the coronavirus disease 2019 (COVID-19)
outbreak, caused by severe acute respiratory syndrome coronavirus 2
infections, there have been concerns among people with MS (PwMS) about the
potential interactions between various disease-modifying therapies and
COVID-19. The COVID-19 in MS Global Data Sharing Initiative (GDSI) was
initiated in 2020 to address these concerns. This paper focuses on the
anonymisation and open-sourcing of a GDSI sub-dataset, comprising data entered
by people with MS and clinicians using a fast data entry tool. The dataset
includes demographics, comorbidities, hospital stay, and COVID-19 symptoms of
PwMS. The dataset can be used to perform different statistical analyses to
improve our understanding of COVID-19 in MS. Furthermore, this dataset can
also be used within the context of educational activities to educate different
stakeholders on the complex data science topics that were used within the
GDSI.
DOI: 10.13026/77ta-1866
title: Patient-level dataset to study the effect of COVID-19 in people with Multiple Sclerosis

description: Multiple Sclerosis (MS) is an inflammatory autoimmune disease of the central
nervous system, causing increased vulnerability to infections and disability
among young adults. Ever since the coronavirus disease 2019 (COVID-19)
outbreak, caused by severe acute respiratory syndrome coronavirus 2
infections, there have been concerns among people with MS (PwMS) about the
potential interactions between various disease-modifying therapies and
COVID-19. The COVID-19 in MS Global Data Sharing Initiative (GDSI) was
initiated in 2020 to address these concerns. This paper focuses on the
anonymisation and open-sourcing of a GDSI sub-dataset, comprising data entered
by people with MS and clinicians using a fast data entry tool. The dataset
includes demographics, comorbidities, hospital stay, and COVID-19 symptoms of
PwMS. The dataset can be used to perform different statistical analyses to
improve our understanding of COVID-19 in MS. Furthermore, this dataset can
also be used within the context of educational activities to educate different
stakeholders on the complex data science topics that were used within the
GDSI.
DOI: 10.13026/feem-fn23
title: Patient-level dataset to study the effect of COVID-19 in people with Multiple Sclerosis

description: Multiple Sclerosis (MS) is an inflammatory autoimmune disease of the central
nervous system, causing increased vulnerability to infections and disability
among young adults. Ever since the coronavirus disease 2019 (COVID-19)
outbreak, caused by severe acute respiratory syndrome coronavirus 2
infections, there have been concerns among people with MS (PwMS) about the
potential interactions between various disease-modifying therapies and
COVID-19. The COVID-19 in MS Global Data Sharing Initiative (GDSI) was
initiated in 2020 to address these concerns. This paper focuses on the
anonymisation and open-sourcing of a GDSI sub-dataset, comprising data entered
by people with MS and clinicians using a fast data entry tool. The dataset
includes demographics, comorbidities, hospital stay, and COVID-19 symptoms of
PwMS. The dataset can be used to perform different statistical analyses to
improve our understanding of COVID-19 in MS. Furthermore, this dataset can
also be used within the context of educational activities to educate different
stakeholders on the complex data science topics that were used within the
GDSI.
DOI: 10.34740/kaggle/dsv/7325169
title: ML Prediction of Poverty and Malnutrition Dataset
DOI: 10.34740/kaggle/dsv/7325197
title: ML Prediction of Poverty and Malnutrition Dataset
DOI: 10.25824/redu/zxccj5
title: Coronarchive_database_29.03.23.xlsx

description: :unav
DOI: 10.25824/redu/tkyhcz
title: Coronarchive database

description: The Coronarchive project includes a collaborative database held at the Center of Digital Humanities IFCH-Unicamp which monitors digital archives regarding the COVID-19 pandemic in Latin America. The database has been built collaboratively since 2020, and contains information about memory initiatives, crowdsourcing ventures, social media collections, oral history projects, and scholarship research, among other archives that have been collecting and preserving evidence about life under COVID-19 in Latin America. The Coronarchive provides an analytical taxonomy that increases the usability of the database by researchers and piques the interest of lay citizens. Theoretically, what justifies such a venture is the social need to understand both the transnationality of the pandemic and the agents involved in its archiving. The Coronarchive develops archivistic perspectives in accordance with the historical conditions of its time by incorporating digital technologies and transcending national borders. Consequently, the project advances over relatively unknown territory by monitoring projects that represent innovative and unmapped agents, methods, and resources within the digital age. In addition, the scope of the Coronarchive provides representativeness for Latin America, one of the regions in which the impacts of the Novel Coronavirus have been more intense, not only for the disease but for its political and economic unfolding.
DOI: 10.25824/redu/wctgqm
title: Coronarchive_redu_07.09.23.xlsx

description: version_2.0
DOI: 10.34740/kaggle/dsv/7325201
title: ML Prediction of Poverty and Malnutrition Dataset
DOI: 10.5517/ccdc.csd.cc2d2pw2
title: CCDC 2207134: Experimental Crystal Structure Determination

description: Related Article: Weilong Wu, Niu Zhao, Yiyi Liu, Shenshen Du, Xinxin Wang, Wenzhi Mo, Xianghe Yan, Chunying Xu, Yan Zhou, Baoming Ji|2023|Org.Lett.|25|8845|doi:10.1021/acs.orglett.3c03550
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g0dxt
title: CCDC 2264516: Experimental Crystal Structure Determination

description: Related Article: Sayantan Chongdar, Anirban Ghosh, Rajaram Bal, Asim Bhaumik|2023|J.Mater.Chem.A|12|233|doi:10.1039/D3TA04894B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6073/pasta/bd0ce97d699099c68694a30b1dd13ad6
title: Infilled daily precipitation data for C1 chart recorder, 1952 - ongoing.

description: Precipitation data have been collected at the C1 climate station (3022 m asl) almost continuously from 1952 to the present. C1 is on locally level terrain on the southeastern flank of Niwot Ridge, 9.7 km east of the Continental Divide. Surrounding vegetation is closed-canopy subalpine conifer forest. Through 1964, precipitation was recorded using an unshielded U.S. Weather Bureau standard totalizing gauge, with observations manually recorded on an approximately weekly basis. The gauge was located in an open area with sparse tree cover adjacent to the forest proper. Starting in late 1961, daily precipitation has been recorded using a Belfort Universal weighing-bucket gauge with chart recorder in an 8-m diameter clearing in the forest; the forest provides natural shielding for the gauge. The two records were assessed for an impact of this station change on record homogeneity and merged (see Methods). Missing daily data were infilled and multiday records were parsed to dailies (see Methods).
DOI: 10.5281/zenodo.10451299
title: Sherlock RSS-related files backup (Data, glgc2021 Part 2)
DOI: 10.5281/zenodo.10451300
title: Sherlock RSS-related files backup (Data, glgc2021 Part 2)
DOI: 10.6073/pasta/dcb38a81192d7514e8e399eec11caec3
title: Homogenized, gap-filled, daily air temperature data for Saddle, 1986 - ongoing.

description: As part of its long-term climate data core collection, the Niwot Ridge LTER has collected daily air temperature at the Saddle site since 1981. The Saddle station is located at 3525 m.a.s.l. and is an important point location to capture local, ambient meteorological conditions for many biological and environmental datasets collected nearby. The location of the Saddle station has also presented challenges to its operation. Freezing temperatures, snow deposition from strong winds following storms, and exposure to lightning are some elements that have disrupted instrument functionality, affected data quality, and made access for research staff difficult over time, especially in winter months. These interruptions have led to missing or faulty data at times and inconsistent data gap-filling. Additionally, a mixture of mechanical hygrothermograph chart and temperature sensors with electronic data loggers have been used since the inception of the Saddle station to measure and record air temperature. Thus, a close inspection of potential influence from instrument turnover and relevant notes from research staff is required for a quality, daily air temperature time series for Saddle.
 Here we present a quality-controlled, gap-filled, daily time series of maximum, average, minimum, and diurnal air temperatures that accounts for instrument turnover at the Saddle. Methods follow those used to gap-fill long-term daily air temperature at the Niwot Ridge LTER D1 and C1 stations so there is consistency among core collection daily air temperature datasets. Metadata for this data package centralizes the most complete station history for Saddle air temperature and includes notes to data users on aspects and limitations of the dataset to consider when using these data in scientific analyses.
DOI: 10.25318/1210017501-eng
title: International merchandise trade by province, commodity, and Principal Trading Partners

description: International merchandise trade data grouped by province and territory, and Principal Trading Partners. Users have the option of selecting Imports, Domestic Exports, or Re-exports, as well as specifying either totals or any of the 12 sections of the North American Product Classification System (NAPCS). Users also have the option of selecting any of the 27 Principal Trading Partner countries. Data are on a Customs basis and not seasonally adjusted.
DOI: 10.25318/1210017501-fra
title: Commerce international de marchandises par province, par produit et les principaux partenaires commerciaux

description: Données sur le commerce international de marchandises, regroupées par province et territoires et par les principaux partenaires commerciaux. Les utilisateurs ont l’option de choisir les importations, les exportations nationales, ou les réexportations, ainsi que de spécifier soit les totaux ou l'une des 12 sections du Système de classification des produits de l'Amérique du Nord (SCPAN). Les utilisateurs ont également l’option de choisir l’un des 27 principaux partenaires commerciaux. Les données sont sur une base douanière et non désaisonnalisées.
DOI: 10.25345/c5pz51x75
title: MassIVE MSV000093749 - Actinobacteria from the bulk soil of Paullinia cupana
title: Data from actinobacteria isolated from the bulk soil of Paulllinia cupana plants in Brazilian Amazon Rainforest region. Strains were cultured in solid ISP2 media, extracted with methanol.
DOI: 10.25318/3810016601-eng
title: Selected population characteristics of people living in resource-based communities, by resource industry

description: Gender, age, language, income, poverty status, indigenous identity, immigrant status, visible minority, education and labour force status of the population residing in resource-based communities (census subdivisions where a relatively high proportion of employment income comes from fishing, forestry, or agriculture), for 2021.
DOI: 10.25318/3810016601-fra
title: Certaines caractéristiques de population des personnes vivant dans les collectivités dont l'économie est basée sur les ressources, selon l'industrie des ressources

description: Genre, âge, langue, revenu, taux de pauvreté, identité autochtone, statut d'immigrant, minorités visibles, éducation et situation d'activité sur le marché de l'emploi de la population qui réside dans les collectivités dont l'économie est basée sur les ressources (subdivisions de recensement où une proportion relativement élevée du revenu d'emploi provient de la pêche, de la foresterie ou de l'agriculture), pour 2021.
DOI: 10.5281/zenodo.10451934
title: GyrB and 16S reference database

description: These are the curated 16S and gyrB datasets created from the NCBI refseq database. These two datasets have only the sequences of either the 16S gene (16S_refseq.fa) or the gyrB gene (gyr_refseq.fa).
DOI: 10.5281/zenodo.10451935
title: GyrB and 16S reference database

description: These are the curated 16S and gyrB datasets created from the NCBI refseq database. These two datasets have only the sequences of either the 16S gene (16S_refseq.fa) or the gyrB gene (gyr_refseq.fa).
DOI: 10.25318/3810016701-eng
title: Resource-based community status and proportion of income and employment from a resource industry, by resource industry, 2016

description: Proportion of income and employment from a resource industry (agriculture, fishing, forestry), and resource-based community status, for all census subdivisions, 2016.
DOI: 10.25318/3810016701-fra
title: Statut de collectivité dont l’économie est basée sur les ressources et proportions du revenu et de l’emploi provenant d’une industrie des ressources, selon l’industrie des ressources, 2016

description: Proportion du revenu et de l'emploi provenant d'une industrie des ressources (agriculture, foresterie, pêche) et statut de collectivité dont l'économie est basée sur les ressources, pour toutes les subdivisions de recensement, 2016.
DOI: 10.25318/3810016801-eng
title: Resource-based community status and proportion of income and employment from a resource industry, by resource industry, 2021

description: Proportion of income and employment from a resource industry (agriculture, fishing, forestry), and resource-based community status, for all census subdivisions, 2021.
DOI: 10.6073/pasta/c006ccbf72cf5bb194a8bd935dd01ee6
title: Soil respiration dataset from abandoned croplands across China

description: Soil respiration, a critical component of the global carbon cycle, is highly sensitive to warming. Agricultural soils, including abandoned croplands, are large sources of carbon dioxide (CO2) to the atmosphere. Here, we report the responses of soil respiration and its components to warming from abandoned croplands spanning a large range in latitude (22.3 to 46.6°N) and elevation (2 to 3734 m) across China from three-year (2019 to 2021) in situ warming experiments. This dataset is a collation of soil respiration, heterotrophic respiration, and autotrophic respiration and their temperature sensitivity along with information on microclimates (e.g., soil temperature), plant biomasses, and soil carbon components (e.g., SOC) and quality under climate warming.
DOI: 10.6073/pasta/78abc0a0b21d581e86ef0b1966e385bf
title: Infilled daily precipitation data for Saddle, 1981 - ongoing.

description: As part of its long-term climate data core collection, the Niwot Ridge LTER has collected daily precipitation at the Saddle site since 1981. The Saddle station is located at 3525 m.a.s.l. and is an important point location to capture local, ambient meteorological conditions for many biological and environmental datasets collected nearby. The location of the Saddle station has also presented challenges to its operation. Freezing temperatures, snow deposition from strong winds following storms, and exposure to lightning are some elements that have disrupted instrument functionality, affected data quality, and made access for research staff difficult over time, especially in winter months.
 Here we present a quality-controlled, gap-filled, daily precipitation time series corrected for blowing snow overcatch at the Saddle station. Methods follow those used to gap-fill long-term daily precipitation at the Niwot Ridge LTER D1 and C1 stations so there is consistency among core collection daily precipitation datasets. Metadata for this data package centralizes the most complete station history for Saddle precipitation and includes notes to data users on aspects and limitations of the dataset to consider when using these data in scientific analyses. Because of unresolved data quality concerns with winter precipitation the first several years of the record, gap-filled data for winter months (October–May) 1981-10-01 through 1987-05-30 are removed.