cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15472/ixghoq
title: Algas presentes en la ciénega Caño Negro, Medio Baudó, Chocó. 2021

description: Los humedales comprenden diversos cuerpos de agua, temporales o permanentes, ámbitos que son hábitats para microorganismos, flora y fauna características, sin embargo, a pesar que se ha logrado algunos avances encaminados a la conservación de estos ecosistemas son los primeros que sufren presión antropogénica, por lo que aún queda mucho por hacer en el campo legal. Las lagunas han sido caracterizadas por ser ecosistemas con tiempo de permanencia del agua variable, naturalmente eutróficas y actualmente bajo estrés ambiental manifiesto que incrementa aún más sus contenidos de nutrientes; en estos sistemas lagunares la sucesión de ciclos secos y húmedos alteran sus caracteres bióticas y abióticas. Los estudios sobre lagunas naturales son de urgente necesidad porque se sabe que la destrucción de los hábitats y la extinción de la vida silvestre ocasiona la ruptura de una cadena ecológica, es por ello el interés de realizar el presente trabajo en esta laguna que sirve de refugio, nidificación y alimentación de aves locales que se alimentan del macrozoobentos de este humedal. Con base en lo expuesto, el instituto de investigaciones ambientales del pacifico IIAP, en su marco de generar conocimiento presenta la caracterización de la calidad biológica del agua mediante las comunidades (algas fitoplanctónicas y macroinvertebrados acuáticos) en la Laguna Caño Negro del municipio del Medio Baudó.
DOI: 10.6084/m9.figshare.14138660
title: Validation of the medium and short version of CENSOPAS-COPSOQ: a psychometric study in the Peruvian population

description: <strong>Background: </strong>The presence of psychosocial risks at work is associated with mental and physical health issues in workers. The study aim was to adapt the COPSOQ-ISTAS21 Medium-Version to the Peruvian context and to develop a Short-Version of the instrument. <strong>Method: </strong>Cross-sectional design study. The COPSOQ-ISTAS21 Medium Version was used. A confirmatory factor analysis was performed to determine the internal structure of each subdimension (first-order) and dimension (second-order) using the Robust Maximum Likelihood estimation method, and classic fit indices in the literature (CFI, SRMR, RMSEA). Internal consistency was evaluated using the alpha and omega coefficients. A short version was developed based on the items with the highest factorial load and that reduce the factorial complexity. <strong>Results: </strong>A total of 1707 participants were evaluated. In the confirmatory factor analysis, the goodness-of-fit indices for seventeen of the 20 one-dimensional models (subdimensions) were identified; two subdimensions could not be evaluated because they presented only two items. When conducting a multidimensional analysis, we identified that all second-order models presented optimal goodness-of-fit indices, except “psychological demands at work”. The latter required adding four pairs of correlated errors to reach adequate fit values. Finally, a short version of only 31 items was designed from the items with optimal fit indices. <strong>Conclusions:</strong> The new adapted versions of COPSOQ-ISTAS21 were renamed CENSOPAS-COPSOQ. The CENSOPAS-COPSOQ is an instrument with sufficient evidence of validity and reliability in its medium and short version, which is why its use is recommended in Peruvian work centers to identify the evaluation and prevention of psychosocial risks at work in Peru. <br> <strong>Manuscritpt: </strong>https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-022-13328-0
DOI: 10.6084/m9.figshare.13683955
title: Depression, post-traumatic stress, anxiety, and fear of COVID-19 in the general population and health-care workers: prevalence, relationship, and explicative model in Peru

description: Background This study has two aims. First, determine the fit of the fear model to COVID-19, anxiety, and post-traumatic stress in the general population and health-care workers. Second, determine which model best explains the relationship between depression and the triad of fear, anxiety, and post-traumatic stress in both groups. Method A cross-sectional study was conducted using self-reported questionnaires for anxiety, fear of COVID-19, depression, and post-traumatic stress. Information was collected from adults living in Lima, the capital and the most populous city in Peru. The explanatory models were evaluated using a structural equation model. Results A total of 830 participants were included, including general population (<em>n</em> = 640) and health-care workers (<em>n</em> = 190). A high overall prevalence of depressive symptoms (16%), anxiety (11.7%), and post-traumatic stress (14.9%) were identified. A higher prevalence of depressive, anxious, or stress symptoms was identified in the general population (28.6%) compared to health-care workers (17.9%). The triad model of fear of COVID-19, anxiety, and stress presented adequate goodness-of-fit indices for both groups. A model was identified that manages to explain depressive symptoms in more than 70% of the general population and health-care workers, based on the variables of the triad (CFI = 0.94; TLI = 0.94; RMSEA = 0.06; SRMR = 0.06). In the general population post-traumatic stress mediated the relationship between anxiety and depression (β = 0.12; 95%CI = 0.06 to 0.18) which was significant, but the indirect effect of post-traumatic stress was not significant in health care workers (β = 0.03; 95%CI = − 0.11 to 0.19). Limitations The prevalence estimates relied on self-reported information. Other variables of interest, such as intolerance to uncertainty or income level, could not be evaluated. Conclusions Our study proposes and tests one model that explains more than 70% of depressive symptoms. This explanatory model can be used in health contexts and populations to determine how emotional factors can affect depressive symptoms.
DOI: 10.6084/m9.figshare.13683955.v2
title: Supplement material

description: Dataset about depression, anxiety, stress, and Fear of COVID.
DOI: 10.6084/m9.figshare.19790509
title: Relationship between job satisfaction, burnout, and depressive symptoms in physicians: A cross-sectional study based on the employment demand-control model

description: Supplement material
DOI: 10.5519/qd.du7wmh2x
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2287 records)
DOI: 10.5281/zenodo.6562369
title: Use of pheromone traps as a monitoring tool for the citrus mealybug (Planococcus citri) on cocoa farms

description: This study developed and optimised a method for the monitoring of populations of <em>P. citri</em> using pheromone-baited traps on cocoa farms and established seasonal activity of <em>P. citri</em> in Cross River State, Nigeria. The effect of time of day, pheromone trap design (Delta and sticky trap), colour (red, blue, yellow, and white) and height placement (0.3. 0.9 and 1.8m) on the capture of adult male <em>P. citri</em> and non-target insects was studied. <em>Planococcus citri</em> was observed to be present year-round with a peak in April, which coincided with the highest air temperatures. By checking traps for diel activity, it was revealed that male flight activity was highest between 1800-0600. Trap design and colour had no significant effect on the number of male <em>P. citri</em> captured, although a smaller number of non-target insects were recorded in Delta traps. Trap height placement had a significant effect on the number of <em>P. citri,</em> and non-target species caught, with the highest number of captures obtained at the lowest trap height (0.3m) and capture rate decreasing as trap height increased. There was a significant positive correlation between the number of trapped male <em>P. citri</em> and air temperature. The results suggest that <em>P. citri</em> is present all year round in the field, that Delta traps are a more suitable trap type, and that 0.3m is an effective height for the monitoring of <em>P. citri</em> on cocoa farms. These findings could be used to improve mealybug monitoring on cocoa farms and could be utilised for the development of an integrated pest management programme.
DOI: 10.5281/zenodo.6562368
title: Use of pheromone traps as a monitoring tool for the citrus mealybug (Planococcus citri) on cocoa farms

description: This study developed and optimised a method for the monitoring of populations of <em>P. citri</em> using pheromone-baited traps on cocoa farms and established seasonal activity of <em>P. citri</em> in Cross River State, Nigeria. The effect of time of day, pheromone trap design (Delta and sticky trap), colour (red, blue, yellow, and white) and height placement (0.3. 0.9 and 1.8m) on the capture of adult male <em>P. citri</em> and non-target insects was studied. <em>Planococcus citri</em> was observed to be present year-round with a peak in April, which coincided with the highest air temperatures. By checking traps for diel activity, it was revealed that male flight activity was highest between 1800-0600. Trap design and colour had no significant effect on the number of male <em>P. citri</em> captured, although a smaller number of non-target insects were recorded in Delta traps. Trap height placement had a significant effect on the number of <em>P. citri,</em> and non-target species caught, with the highest number of captures obtained at the lowest trap height (0.3m) and capture rate decreasing as trap height increased. There was a significant positive correlation between the number of trapped male <em>P. citri</em> and air temperature. The results suggest that <em>P. citri</em> is present all year round in the field, that Delta traps are a more suitable trap type, and that 0.3m is an effective height for the monitoring of <em>P. citri</em> on cocoa farms. These findings could be used to improve mealybug monitoring on cocoa farms and could be utilised for the development of an integrated pest management programme.
DOI: 10.5517/ccdc.csd.cc291tgn
title: CCDC 2116911: Experimental Crystal Structure Determination

description: Related Article: Eliezer Ortiz, Yu-Hsiang Chang, Jonathan Z. Shezaf, Weijia Shen, Michael J. Krische|2022|J.Am.Chem.Soc.|144|8861|doi:10.1021/jacs.2c03614
DOI: 10.5517/ccdc.csd.cc291thp
title: CCDC 2116912: Experimental Crystal Structure Determination

description: Related Article: Eliezer Ortiz, Yu-Hsiang Chang, Jonathan Z. Shezaf, Weijia Shen, Michael J. Krische|2022|J.Am.Chem.Soc.|144|8861|doi:10.1021/jacs.2c03614
DOI: 10.5517/ccdc.csd.cc293ljk
title: CCDC 2118618: Experimental Crystal Structure Determination

description: Related Article: Junjie Peng, Zewei Liu, Ying Wu, Shikai Xian, Zhong Li|2022|ACS Applied Materials and Interfaces|14|21089|doi:10.1021/acsami.2c04779
DOI: 10.5517/ccdc.csd.cc293lkl
title: CCDC 2118619: Experimental Crystal Structure Determination

description: Related Article: Junjie Peng, Zewei Liu, Ying Wu, Shikai Xian, Zhong Li|2022|ACS Applied Materials and Interfaces|14|21089|doi:10.1021/acsami.2c04779
DOI: 10.5517/ccdc.csd.cc295brl
title: CCDC 2120299: Experimental Crystal Structure Determination

description: Related Article: Po-Min Chuang, Yi-Jung Tu, Jing-Yun Wu|2022|Sens.Actuators,B|366|131967|doi:10.1016/j.snb.2022.131967
DOI: 10.5517/ccdc.csd.cc2965xm
title: CCDC 2121110: Experimental Crystal Structure Determination

description: Related Article: Xusheng Zhang, Xingxing Jiang, Kunjie Liu, Liubing Fan, Jindong Cao, Shihui He, Na Wang, Jing Zhao, ZheShuai Lin, Quanlin Liu|2022|Inorg.Chem.|61|7560|doi:10.1021/acs.inorgchem.2c00711
DOI: 10.5517/ccdc.csd.cc29fnw9
title: CCDC 2128301: Experimental Crystal Structure Determination

description: Related Article: Eliezer Ortiz, Yu-Hsiang Chang, Jonathan Z. Shezaf, Weijia Shen, Michael J. Krische|2022|J.Am.Chem.Soc.|144|8861|doi:10.1021/jacs.2c03614
DOI: 10.5517/ccdc.csd.cc29j8jn
title: CCDC 2130801: Experimental Crystal Structure Determination

description: Related Article: Xusheng Zhang, Xingxing Jiang, Kunjie Liu, Liubing Fan, Jindong Cao, Shihui He, Na Wang, Jing Zhao, ZheShuai Lin, Quanlin Liu|2022|Inorg.Chem.|61|7560|doi:10.1021/acs.inorgchem.2c00711
DOI: 10.5517/ccdc.csd.cc29j8lq
title: CCDC 2130803: Experimental Crystal Structure Determination

description: Related Article: Xusheng Zhang, Xingxing Jiang, Kunjie Liu, Liubing Fan, Jindong Cao, Shihui He, Na Wang, Jing Zhao, ZheShuai Lin, Quanlin Liu|2022|Inorg.Chem.|61|7560|doi:10.1021/acs.inorgchem.2c00711
DOI: 10.5517/ccdc.csd.cc29kh0d
title: CCDC 2131963: Experimental Crystal Structure Determination

description: Related Article: Yixiong Ren, Wenting He, Wenge Chen|2022|Polyhedron|221|115827|doi:10.1016/j.poly.2022.115827
description: Related Article: Yixiong Ren, Wenting He, Wenge Chen|2022|Polyhedron|221|115827|doi:10.1016/j.poly.2022.115827
DOI: 10.5517/ccdc.csd.cc29yw49
title: CCDC 2143871: Experimental Crystal Structure Determination

description: Related Article: Ling Zhang, Jin-Long Yan, Yuan Wang, Xiao-Lei Zhao, Wei-Na Wu, Yun-Chang Fan, Zhi-Hong Xu, Ling-Ling Yan|2022|Spectrochim.Acta,Part A|278|121329|doi:10.1016/j.saa.2022.121329
DOI: 10.5517/ccdc.csd.cc2bcckn
title: CCDC 2155881: Experimental Crystal Structure Determination

description: Related Article: Francoise M. Amombo Noa, Erik Svensson Grape, Michelle Åhlén, William E. Reinholdsson, Christian R. Göb, François-Xavier Coudert, Ocean Cheung, A. Ken Inge, Lars Öhrström|2022|J.Am.Chem.Soc.|144|8725|doi:10.1021/jacs.2c02351
DOI: 10.5517/ccdc.csd.cc2bcclp
title: CCDC 2155882: Experimental Crystal Structure Determination

description: Related Article: Francoise M. Amombo Noa, Erik Svensson Grape, Michelle Åhlén, William E. Reinholdsson, Christian R. Göb, François-Xavier Coudert, Ocean Cheung, A. Ken Inge, Lars Öhrström|2022|J.Am.Chem.Soc.|144|8725|doi:10.1021/jacs.2c02351
DOI: 10.5517/ccdc.csd.cc2bccmq
title: CCDC 2155883: Experimental Crystal Structure Determination

description: Related Article: Francoise M. Amombo Noa, Erik Svensson Grape, Michelle Åhlén, William E. Reinholdsson, Christian R. Göb, François-Xavier Coudert, Ocean Cheung, A. Ken Inge, Lars Öhrström|2022|J.Am.Chem.Soc.|144|8725|doi:10.1021/jacs.2c02351
DOI: 10.5517/ccdc.csd.cc2bccnr
title: CCDC 2155884: Experimental Crystal Structure Determination

description: Related Article: Francoise M. Amombo Noa, Erik Svensson Grape, Michelle Åhlén, William E. Reinholdsson, Christian R. Göb, François-Xavier Coudert, Ocean Cheung, A. Ken Inge, Lars Öhrström|2022|J.Am.Chem.Soc.|144|8725|doi:10.1021/jacs.2c02351
DOI: 10.5517/ccdc.csd.cc2bccqt
title: CCDC 2155886: Experimental Crystal Structure Determination

description: Related Article: Francoise M. Amombo Noa, Erik Svensson Grape, Michelle Åhlén, William E. Reinholdsson, Christian R. Göb, François-Xavier Coudert, Ocean Cheung, A. Ken Inge, Lars Öhrström|2022|J.Am.Chem.Soc.|144|8725|doi:10.1021/jacs.2c02351
DOI: 10.5517/ccdc.csd.cc2bdw3r
title: CCDC 2157324: Experimental Crystal Structure Determination

description: Related Article: Xiaomin Shu, De Zhong, Yanmei Lin, Xiao Qin, Haohua Huo|2022|J.Am.Chem.Soc.|144|8797|doi:10.1021/jacs.2c02795
DOI: 10.5517/ccdc.csd.cc2bdw4s
title: CCDC 2157325: Experimental Crystal Structure Determination

description: Related Article: Xiaomin Shu, De Zhong, Yanmei Lin, Xiao Qin, Haohua Huo|2022|J.Am.Chem.Soc.|144|8797|doi:10.1021/jacs.2c02795
DOI: 10.5517/ccdc.csd.cc29bt0h
title: CCDC 2125546: Experimental Crystal Structure Determination

description: Related Article: Xing Wang, Shi-Qiang Wang, Jia-Nan Chen, Jian-Hua Jia, Cheng Wang, Kevin Paillot, Ivan Breslavetz, La-Sheng Long, Lansun Zheng, Geert L. J. A. Rikken, Cyrille Train, Xiang-Jian Kong, Matteo Atzori|2022|J.Am.Chem.Soc.|144|8837|doi:10.1021/jacs.2c03049
DOI: 10.5517/ccdc.csd.cc29bt1j
title: CCDC 2125547: Experimental Crystal Structure Determination

description: Related Article: Xing Wang, Shi-Qiang Wang, Jia-Nan Chen, Jian-Hua Jia, Cheng Wang, Kevin Paillot, Ivan Breslavetz, La-Sheng Long, Lansun Zheng, Geert L. J. A. Rikken, Cyrille Train, Xiang-Jian Kong, Matteo Atzori|2022|J.Am.Chem.Soc.|144|8837|doi:10.1021/jacs.2c03049
DOI: 10.5517/ccdc.csd.cc29bt2k
title: CCDC 2125548: Experimental Crystal Structure Determination

description: Related Article: Xing Wang, Shi-Qiang Wang, Jia-Nan Chen, Jian-Hua Jia, Cheng Wang, Kevin Paillot, Ivan Breslavetz, La-Sheng Long, Lansun Zheng, Geert L. J. A. Rikken, Cyrille Train, Xiang-Jian Kong, Matteo Atzori|2022|J.Am.Chem.Soc.|144|8837|doi:10.1021/jacs.2c03049
DOI: 10.5517/ccdc.csd.cc29bt3l
title: CCDC 2125549: Experimental Crystal Structure Determination

description: Related Article: Xing Wang, Shi-Qiang Wang, Jia-Nan Chen, Jian-Hua Jia, Cheng Wang, Kevin Paillot, Ivan Breslavetz, La-Sheng Long, Lansun Zheng, Geert L. J. A. Rikken, Cyrille Train, Xiang-Jian Kong, Matteo Atzori|2022|J.Am.Chem.Soc.|144|8837|doi:10.1021/jacs.2c03049
DOI: 10.5517/ccdc.csd.cc2bklxf
title: CCDC 2161875: Experimental Crystal Structure Determination

description: Related Article: Jun Shi, Rui-An Wang, Wei Wu, Jun-Rong Song, Qin Chi, Wei-Dong Pan, Hai Ren|2022|Org.Lett.|24|3358|doi:10.1021/acs.orglett.2c01059
DOI: 10.5517/ccdc.csd.cc2bmzrq
title: CCDC 2164164: Experimental Crystal Structure Determination

description: Related Article: Anand Babu Velappan, Ali Husain, Narendran Rajendran, Basma Ghazal, Saad Makhseed|2022|J.Mol.Liq.|359|119273|doi:10.1016/j.molliq.2022.119273
DOI: 10.6084/m9.figshare.19758631.v3
title: Alba Mons Lava Tube Morphometry Data

description: <strong>Description</strong> The data archived here include specific results from geologic mapping studies of the Martian volcano Alba Mons associated with the publication 'Distribution and Morphology of Lava Tube Systems on the Western Flank of Alba Mons, Mars,' submitted to the JGR/ESS Special Issue on Planetary Caves: Exploring Planetary Caves as Windows into Subsurface Geology, Habitability, and Astrobiology. <br> These results were generated from analyses of imaging and topographic datasets acquired from spacecraft orbiting Mars, and were used to provide descriptions and quantitative characteristics of lava tube systems on the western flank of Alba Mons. Included here are two tables containing data associated with Figure 10 (map of lava tube segments) and Figure 11 and Tables 1 and 2 (associated morphologic and morphometric parameters) of the publication. More details of the study can be found in the publication. <br> <strong>Data Files and Types</strong> Lava_Tube_Segments_Morphometry_A: ascii table, csv Lava_Tube_Segments_Morphometry_B: ascii table, csv <br> <strong>Data Spatial Reference</strong> Mars_2000_(Sphere) WKID: 104971 Authority: Esri Angular Unit: Degree (0.0174532925199433) Prime Meridian: Reference_Meridian (0.0) Datum: Mars_2000_(Sphere) Spheroid: Mars_2000_(Sphere) Semimajor Axis: 3396190.0 Semiminor Axis: 3396190.0 Inverse Flattening: 0.0 <br> <strong>Bounding Box</strong> West: -130.0 deg East: -115.0 deg North: 47.5 deg South: 37.5 deg <br> <strong>Header Fields (Line 1) and Description </strong> <strong>Lava_Tube_Segments_Morphometry_A.csv</strong> ID: Feature ID of lava tube segment location Morphology: Morphology Type Start_Long: Longitude of Line Feature Start Start_Lat: Latitude of Line Feature Start Start_Z: Elevation of Line Feature Start End_Longitude: Longitude of Line Feature End End_Latitude: Latitude of Line Feature End End_Z: Elevation of Line Feature End Center_Longitude: Longitude of Line Feature Midpoint Center_Latitude: Latitude of Line Feature Midpoint Length_km: Length of Line Feature in kilometers <br> <strong>Lava_Tube_Segments_Morphometry_B.csv</strong> ID: Feature ID of lava tube segment location Slope_Deg: Surface slope of line segment, degrees Reg_Slope: Regional slope from 50 km MOLA grid, degrees Reg_Aspect: Regional slope aspect from 50 km MOLA grid, degrees Slope_diff: Difference between line feature and regional slope, degrees Bearing: Orientation of line feature, degrees Bearing_diff: Difference between line feature orientation and aspect of regional slope, degrees Longitude: Longitude of Line Feature Midpoint Latitude: Latitude of Line Feature Midpoint <br> <br>
DOI: 10.6084/m9.figshare.19758631
title: Characteristics of Alba Mons lava tubes: western flank study area

description: <strong>Description</strong> The data archived here include specific results from geologic mapping studies of the Martian volcano Alba Mons associated with the publication 'Distribution and Morphology of Lava Tube Systems on the Western Flank of Alba Mons, Mars,' submitted to the JGR/ESS Special Issue on Planetary Caves: Exploring Planetary Caves as Windows into Subsurface Geology, Habitability, and Astrobiology. <br> These results were generated from analyses of imaging and topographic datasets acquired from spacecraft orbiting Mars, and were used to provide descriptions and quantitative characteristics of lava tube systems on the western flank of Alba Mons. Included here are two tables containing data associated with Figure 10 (map of lava tube segments) and Figure 11 and Tables 1 and 2 (associated morphologic and morphometric parameters) of the publication. More details of the study can be found in the publication. <br> <strong>Citation<br> </strong>When using these data, please cite the following with the appropriate version number: <br> <em>Crown, D.A., Scheidt, S.P., and Berman, D.C. (2022). Characteristics of Alba Mons lava tubes: Western flank study area (Version 5). figshare, </em><em>doi.org/10.6084/m9.figshare.19758631.v5</em><em>. </em> <br> <br> <strong>Data Files and Types</strong> Lava_Tube_Segments_Morphometry_A: ascii table, csv Lava_Tube_Segments_Morphometry_B: ascii table, csv <br> <strong>Data Spatial Reference</strong> Mars_2000_(Sphere) WKID: 104971 Authority: Esri Angular Unit: Degree (0.0174532925199433) Prime Meridian: Reference_Meridian (0.0) Datum: Mars_2000_(Sphere) Spheroid: Mars_2000_(Sphere) Semimajor Axis: 3396190.0 Semiminor Axis: 3396190.0 Inverse Flattening: 0.0 <br> <strong>Bounding Box</strong> West: -130.0 deg East: -115.0 deg North: 47.5 deg South: 37.5 deg <br> <strong>Header Fields (Line 1) and Description </strong> <strong>Lava_Tube_Segments_Morphometry_A.csv</strong> ID: Feature ID of lava tube segment location Morphology: Morphology Type Start_Long: Longitude of Line Feature Start Start_Lat: Latitude of Line Feature Start Start_Z: Elevation of Line Feature Start End_Longitude: Longitude of Line Feature End End_Latitude: Latitude of Line Feature End End_Z: Elevation of Line Feature End Center_Longitude: Longitude of Line Feature Midpoint Center_Latitude: Latitude of Line Feature Midpoint Length_km: Length of Line Feature in kilometers <br> <strong>Lava_Tube_Segments_Morphometry_B.csv</strong> ID: Feature ID of lava tube segment location Slope_Deg: Surface slope of line segment, degrees Reg_Slope: Regional slope from 50 km MOLA grid, degrees Reg_Aspect: Regional slope aspect from 50 km MOLA grid, degrees Slope_diff: Difference between line feature and regional slope, degrees Bearing: Orientation of line feature, degrees Bearing_diff: Difference between line feature orientation and aspect of regional slope, degrees Longitude: Longitude of Line Feature Midpoint Latitude: Latitude of Line Feature Midpoint <br> <br>