cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc27gf16
title: CCDC 2069437: Experimental Crystal Structure Determination

description: Related Article: Nataliya V. Glinyanaya, Gennady F. Rayenko, Nikolai I. Korotkikh, Eduard B. Rusanov, Alexey B. Ryabitsky, Oles P. Shvaika|2021|RSC Advances|11|30841|doi:10.1039/D1RA04337D
DOI: 10.5517/ccdc.csd.cc27gf27
title: CCDC 2069438: Experimental Crystal Structure Determination

description: Related Article: Nataliya V. Glinyanaya, Gennady F. Rayenko, Nikolai I. Korotkikh, Eduard B. Rusanov, Alexey B. Ryabitsky, Oles P. Shvaika|2021|RSC Advances|11|30841|doi:10.1039/D1RA04337D
DOI: 10.5517/ccdc.csd.cc27gstb
title: CCDC 2069802: Experimental Crystal Structure Determination

description: Related Article: Matthew C. Davis, Kamran B. Ghiassi|2021|Results in Chemistry|3|100192|doi:10.1016/j.rechem.2021.100192
DOI: 10.5517/ccdc.csd.cc27hjlw
title: CCDC 2070508: Experimental Crystal Structure Determination

description: Related Article: Matthew C. Davis, Kamran B. Ghiassi|2021|Results in Chemistry|3|100192|doi:10.1016/j.rechem.2021.100192
DOI: 10.5517/ccdc.csd.cc27hjmx
title: CCDC 2070509: Experimental Crystal Structure Determination

description: Related Article: Matthew C. Davis, Kamran B. Ghiassi|2021|Results in Chemistry|3|100192|doi:10.1016/j.rechem.2021.100192
DOI: 10.5517/ccdc.csd.cc27zg7y
title: CCDC 2084850: Experimental Crystal Structure Determination

description: Related Article: Pierre Thuéry, Jack Harrowfield|2021|Eur.J.Inorg.Chem.|2021|3699|doi:10.1002/ejic.202100476
DOI: 10.5517/ccdc.csd.cc27zg8z
title: CCDC 2084851: Experimental Crystal Structure Determination

description: Related Article: Pierre Thuéry, Jack Harrowfield|2021|Eur.J.Inorg.Chem.|2021|3699|doi:10.1002/ejic.202100476
DOI: 10.5517/ccdc.csd.cc27zg90
title: CCDC 2084852: Experimental Crystal Structure Determination

description: Related Article: Pierre Thuéry, Jack Harrowfield|2021|Eur.J.Inorg.Chem.|2021|3699|doi:10.1002/ejic.202100476
DOI: 10.5517/ccdc.csd.cc280fc3
title: CCDC 2085784: Experimental Crystal Structure Determination

description: Related Article: Idir Benaissa, Katarzyna Gajda, Laure Vendier, Noël Lugan, Anna Kajetanowicz, Karol Grela, Véronique Michelet, Vincent César, Stéphanie Bastin|2021|Organometallics|40|3223|doi:10.1021/acs.organomet.1c00458
DOI: 10.17632/td4k9jhd2b.1
title: Data for: China's local governments breaking the land use planning quota: A strategic interaction perspective

description: This database includes data on 262 prefecture-level cities from 2007-2016. The data on status quo newly increased construction land were collected from the China Land Market website (www.landchina.com); the data on the binding targets of construction land were collected from the general land use planning of each prefecture-level city (2006-2020); the variable data for land law enforcement are derived from the China Land and Resources Statistics Yearbook. Other variables, such as fiscal deficit, foreign direct investment, GDP, nonagricultural population, and road area, are derived from the China Urban Statistical Yearbook. The data concerning the tenure and age of the Party secretary are derived from local governments' official websites, Xinhuanet (www.xinhuanet.com), People network (www.people.com.cn) and Baidu Encyclopedia (www.baike.baidu.com).
DOI: 10.17632/7m6dsgk62t.2
title: Assessing success and failure factors in increasing drought resilience for smallholders in SSA through the lens of transformation

description: Through our research question "what are the main challenges and incentives for increasing resilience to drought in rural communities?”, we seek to understand eventual transformations through already conducted projects and other studies (primary research outcomes) the underlying factors to success or failure related to resilience to drought through water management in smallholder farming in Sub-Saharan Africa. The goal is to synthesize and critically analyze outcomes from previous studies to understand key issues connected to challenges and incentives related to agricultural water management practices and technologies in rural communities affected by droughts. 

I. Criteria for inclusion or exclusion:
Articles included in the review shall be in line with the goal and focus of the review, namely:
(1)	Written in English and published between 2007- 2019
(3)	Focusing on water management in smallholder farming 
(4)	Assessing performance, success, failure, barriers, challenges and lessons learnt in relation to increasing resilience to drought by applying agricultural water management technologies and practices in rural areas, with focus on both the practical, personal and political spheres. 
(5)	Discussed actual reasons for success or failure with focus on one or several of the three spheres

II. Data collection procedure
a.	Running the main code to perform advanced searches in electronic databases  
•	Key words: success*, fail*, agriculture* water, management, semi, dry, arid, land, drought, farm, livelihood, resilience and adaptation.
•	Conduct an online search in Web of science, Scopus, Agraria and Google Scholar
•	Systematically select journal articles, technical papers, working documents and published thesis related to agricultural water management technologies and practices focusing on successful and failure factors in Sub-Saharan Africa 

b.	Searching codes to be used in the Web of Science: 
TS= (( Agricultural water* management AND ("water captur* conservation$") AND ("smallhold* farm*") OR ("rural irrigation") OR (limiting factor$) OR ("dry land$") OR semi-arid OR ("rural communit*") OR ("rain deficit*") OR reservoir$ OR "rain water harvest*" OR storage* OR ("drought* resilience") OR pond$ OR rainwater AND harvesting)) AND SU=(Water Resources AND Agriculture OR Rural Development OR Irrigation) AND TS= (success$ OR limitation$ AND factor$ OR water conservation) AND LA=(English) AND DT=(Article OR BOOK CHAPTER OR Book Review OR Book OR Discussion OR Letter OR Note OR Reprint) AND PY=(2007-2019)

III. Relevance of the paper
The paper is relevant if it meets the following requirements:
•	The study should be about agricultural water management, water capture, storage and use as supplemental irrigation in Sub-Saharan Africa
•	The study highlights the enabling policies, institutional arrangements and other capitals leading to success/failure in rural settings.    
•	The study should NOT be about high scale irrigation.
DOI: 10.17632/k69cc5ykvv.2
title: Literature on Dalit Activism: Excerpts, Review and Notes: 2014-2018

description: Indentity Politics and Dalit Assertions in Sindh, Pakistan
Literature Review Notes (Extended coarse analysis)
Table of Contents
Indentity Politics and Dalit Assertions in Sindh, Pakistan	1
Literature Review Notes (Extended coarse analysis)	1
Chapter 1: Literature Review-I	1
1.1 Introdcution	1
1.2 Making sense of local literature	6
1.3 Academic literature	6
1.4 Literature on Dalits in Sindh	9
1.5 Peripheral nature of literature on caste	11
16 Epistemic skipping: Denial of casteism in academic literature	13
1.7 Making sense of Sindhi writings, and non-academic Dalit literature and Sindhi	22
1.8 Literature on caste in Sindh	25
1.8 Denial of Casteism in Pakistan	36
Chapter 2: Litererature on Theoretical Framework, Interseectionality and Graded Inequality	40
2.1 Dalit-Marxist lens	40
2.2 Marxist-Dalit perspective	44
2.3 Islamic capitalism and Islamic socialism (taimur rahaman)	48
2.4 Brahminism of Marxists and the caste apartheid	54
2.5 Marxist and  nationalist argument	60
2.6 Marxists focus on resolving nationalist problem as a preconction to step forward in capitalist economy.	61
2.7 Class Relations in Agriculture	66
2.8 Marxist interpretation of Bradari and Village Community	68
2.9 Caste and Class	73
Caste vs. Class	74
2.10 Marxist Revolution, Civil Society and Anthropology of Sindhi Peasantry	75
2.11 Marxist stance and the Internal Divisions within Sindhi Civil Society	78
2.12 Marxist stance and the Internal Divisions within Sindhi Civil Society	81
2.13 Civil Society’s efforts for Land Reforms and Macro-level structural Changes	83
2.14 Intersecting nodes of oppression, suppression and humiliation	86
2.15 Contexulasing Dalit Oppression in Pakistan	89
2.16 Typological presentation of hierarchies of oppression and intersectionality in the Pakistan	90
2.17 Redefining intersectional Dalit condition of oppression and humiliation	94
218 Hierchichal relationship & embededness of Caste-Class-Religion	96
2.19   Caste as a class	99
2.20 Graded Caste Inequalities in Sindh and Dalit Oppression	101
2.21 Graded Dalit exclusion and oppresion	104
2.22 Social and political status of Kolhi, Bheel, Meghwar and Baghri in Pakistan	106
2.23 Level of suppression and oppression of Scheduled castes and other Dalit and non-Dalit communities	113
2.24 Caste structure of Sindh Assembly	120
2.25 Caste legacy dermining political contituencies	121
2.27 Caste hierarchies in government jobs	130
2.28 Dalit Critique of Caste in Pakistan	135
2.29 Political Disparity between Scheduled Castes and caste Hindus	152
2.30 Dalit exclusion from Sindhi diaspora politics, and Dalit transantional and translocal alternatives	158
2.32 Operationalising\politicising Dalit Oppression	162
2.34 Role of PDSN during about discrimination with Dalit in relief work	163
Routine Issues faced by Dalits	163
Forms of  discrimination	164
Dalit Women	164
DOI: 10.17632/f9rw5x58zs.1
title: Łuców, D., Küttim, M., Słowiński, M., Kołaczek, P., Karpińska-Kołaczek, M., Küttim, L., Salme, M., & Lamentowicz, M. (2021). Searching for an ecological baseline: Long-term ecology of a post-extraction restored bog in Northern Estonia. QI 10.1016.

description: Site Description. The peat extraction area (107.66 ha) of Hara peatland is located in the Kuusalu parish in northern Estonia in Harjumaa county (59°33′N, 25°36′E, 27.5 m a.s.l.). Peat was industrially extracted in the years 1975–1994. The degraded part of the Hara peatland was rewetted in 2011–2013 and its vegetation is currently dominated by scattered hummocks of Eriophorum vaginatum and small patches of Sphagnum. Hara peatland is an ombrotrophic bog which covers an area of 747 ha and is located at the former lagoon, between the coastal formations of the Ancylus Lake stadium of the Baltic Sea. The area site belongs to the Lahemaa National Park.

The proxies analyses are:
- Testate amoebae %
- Diatom %
- Plant macrofossils %
- Pollen %
- Non-pollen palynomorphs %
DOI: 10.6084/m9.figshare.16713448
title: COVID-19 Twitter Dataset

description: The current dataset contains Tweet IDs for tweets mentioning "COVID" (e.g., COVID-19, COVID19) and shared between March and July of 2020.<br><b>Sampling Method</b>: hourly requests sent to Twitter Search API using Social Feed Manager, an open source software that harvests social media data and related content from Twitter and other platforms.<br><b>NOTE:</b> 1) In accordance with Twitter API Terms, only Tweet IDs are provided as part of this dataset. <br>2) To recollect tweets based on the list of Tweet IDs contained in these datasets, you will need to use tweet 'rehydration' programs like Hydrator (https://github.com/DocNow/hydrator) or Python library Twarc (https://github.com/DocNow/twarc). <br>3) This dataset, like most datasets collected via the Twitter Search API, is a sample of the available tweets on this topic and is not meant to be comprehensive. Some COVID-related tweets might not be included in the dataset either because the tweets were collected using a standardized but intermittent (hourly) sampling protocol or because tweets used hashtags/keywords other than COVID (e.g., Coronavirus or #nCoV). <br>4) To broaden this sample, consider comparing/merging this dataset with other COVID-19 related public datasets such as: https://github.com/thepanacealab/covid19_twitter https://ieee-dataport.org/open-access/corona-virus-covid-19-tweets-dataset https://github.com/echen102/COVID-19-TweetIDs
DOI: 10.25493/7qe6-pbe
title: Probabilistic cytoarchitectonic map of Area Id8 (Insula) (v4.0)

description: This dataset contains cytoarchitectonic probability maps of the Area Id8 (Insula) in the single subject template of the MNI (“Colin 27”) and the MNI ICBM 152 reference space, as part of the Julich-Brain atlas. The area was identified using cytoarchitectonic analysis on cell-body-stained histological sections in both hemispheres of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The maps show the probability to belong to the Area Id8 (Insula) for each voxel of the reference spaces. Data is provided in the NifTi format. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create probabilistic brain maps. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Delineations of the Area Id8 (Insula) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2021) [Data set, v2.9] [DOI: 10.25493/VSMK-H94](https://doi.org/10.25493/VSMK-H94)
DOI: 10.21227/tsc2-tk97
title: VTS 2021 UAV Innovation Challenge - Georgia State University

description: We design a solution to achieve coordinated localization between two unmanned aerial vehicles (UAVs) using radio and camera perception. We achieve the localization between the UAVs in the context of solving the problem of UAV Global Positioning System (GPS) failure or its unavailability. Our approach allows one UAV with a functional GPS unit to coordinate the localization of another UAV with a compromised or missing GPS system. Our solution for localization uses a sensor fusion and coordinated wireless communication approach. In this paper, we present the complete system design including the communication protocol for coordination, implementation and demonstration details of our prototype system, use--cases for our solution, and challenges and limitations in our prototype system.
DOI: 10.5066/p97wd6ah
title: Mojave Desert Tortoise (Gopherus agassizii) Morphometrics and Egg Data from Seven Sites across the Mojave, (1997-2002)

description: These data were acquired from 7 study sites distributed across the range of Gopherus agassizii. Data were collected from 1997 to 2002 as part of three separate studies, although data were not collected at all sites in each year. Radio-transmitters were attached to the carapace of 151 females and VHF radio-telemetry was used to relocate animals to assess reproductive status. Egg production was determined from X-radiographs taken weekly/biweekly intervals (depending on the study) using a portable X-ray machine between April and July or August of each year. In addition, the mean carapace length (MCL) of each tortoise was measured at each time of capture or recapture using calipers (mm). A nesting event was recorded if a female previously observed with eggs was observed without eggs during a subsequent X-ray session. These data support the following publication: Mitchell, C.I., Friend, D.A., Phillips, L.T., Hunter, E.A., Lovich, J.E., Agha, M., Puffer, S.R., Cummings, K.L., Medica, P.A., Esque, T.C. and Nussear, K.E., 2021. 'Unscrambling' the drivers of egg production in Agassiz's desert tortoise: climate and individual attributes predict reproductive output. Endangered Species Research, 44, pp.217-230.
DOI: 10.5281/zenodo.5140105
title: Crowdsourcing vibration data stemming from different transportation usages

description: Crowdsourcing vibration data stemming from different activities and transportation usages (by trains, by buses, by bicycles by walking). We present a comprehensive dataset that provides the pattern of five activities walking, cycling, taking a train, a bus or a taxi. The measurements are carried out by embedded sensor accelerometer in smartphones. The dataset offers dynamic responses of subjects carrying smartphones in varied styles as they performing the five activities through vibrations acquired by accelerometers. The dataset contains corresponding time stamps and vibrations in three directions longitudinal, horizontal, and vertical stored in an Excel Macro-enabled Workbook (xlsm) format can be used to train an AI model in a smartphone which has potentials to collect people’s vibration data and decides what movement is being conducted. Besides, with more data are received, the database can be updated and it can be fed to train the model with a larger dataset. The prevalent of the smartphone opens the door of crowdsensing which leads to the pattern of people talking public transports can be understood. Furthermore, the time consumed in each activity is available in the dataset. Therefore, with a better understanding of people using public transports, the service and schedule can be planned perceptively. Activities to obtain the dataset are jointly funded by H2020 and Hitachi Europe.
description: {"references": ["http://edata.bham.ac.uk/655/"]}
DOI: 10.5281/zenodo.4976858
title: Crowdsourcing vibration data stemming from different transportation usages

description: Crowdsourcing vibration data stemming from different activities and transportation usages (by trains, by buses, by bicycles by walking). We present a comprehensive dataset that provides the pattern of five activities walking, cycling, taking a train, a bus or a taxi. The measurements are carried out by embedded sensor accelerometer in smartphones. The dataset offers dynamic responses of subjects carrying smartphones in varied styles as they performing the five activities through vibrations acquired by accelerometers. The dataset contains corresponding time stamps and vibrations in three directions longitudinal, horizontal, and vertical stored in an Excel Macro-enabled Workbook (xlsm) format can be used to train an AI model in a smartphone which has potentials to collect people’s vibration data and decides what movement is being conducted. Besides, with more data are received, the database can be updated and it can be fed to train the model with a larger dataset. The prevalent of the smartphone opens the door of crowdsensing which leads to the pattern of people talking public transports can be understood. Furthermore, the time consumed in each activity is available in the dataset. Therefore, with a better understanding of people using public transports, the service and schedule can be planned perceptively. Activities to obtain the dataset are jointly funded by H2020 and Hitachi Europe.
description: {"references": ["http://edata.bham.ac.uk/655/"]}
DOI: 10.5517/ccdc.csd.cc283gql
title: CCDC 2088709: Experimental Crystal Structure Determination

description: Related Article: Roman Schowner, Janis V. Musso, Wolfgang Frey, Michael R. Buchmeiser|2021|Organometallics|40|3145|doi:10.1021/acs.organomet.1c00373
DOI: 10.5517/ccdc.csd.cc283grm
title: CCDC 2088710: Experimental Crystal Structure Determination

description: Related Article: Roman Schowner, Janis V. Musso, Wolfgang Frey, Michael R. Buchmeiser|2021|Organometallics|40|3145|doi:10.1021/acs.organomet.1c00373
DOI: 10.5517/ccdc.csd.cc283gsn
title: CCDC 2088711: Experimental Crystal Structure Determination

description: Related Article: Roman Schowner, Janis V. Musso, Wolfgang Frey, Michael R. Buchmeiser|2021|Organometallics|40|3145|doi:10.1021/acs.organomet.1c00373
DOI: 10.5517/ccdc.csd.cc283gtp
title: CCDC 2088712: Experimental Crystal Structure Determination

description: Related Article: Roman Schowner, Janis V. Musso, Wolfgang Frey, Michael R. Buchmeiser|2021|Organometallics|40|3145|doi:10.1021/acs.organomet.1c00373
DOI: 10.5517/ccdc.csd.cc283gvq
title: CCDC 2088713: Experimental Crystal Structure Determination

description: Related Article: Roman Schowner, Janis V. Musso, Wolfgang Frey, Michael R. Buchmeiser|2021|Organometallics|40|3145|doi:10.1021/acs.organomet.1c00373
DOI: 10.5517/ccdc.csd.cc283gwr
title: CCDC 2088714: Experimental Crystal Structure Determination

description: Related Article: Roman Schowner, Janis V. Musso, Wolfgang Frey, Michael R. Buchmeiser|2021|Organometallics|40|3145|doi:10.1021/acs.organomet.1c00373
DOI: 10.5517/ccdc.csd.cc283gxs
title: CCDC 2088715: Experimental Crystal Structure Determination

description: Related Article: Roman Schowner, Janis V. Musso, Wolfgang Frey, Michael R. Buchmeiser|2021|Organometallics|40|3145|doi:10.1021/acs.organomet.1c00373
DOI: 10.5517/ccdc.csd.cc283gyt
title: CCDC 2088716: Experimental Crystal Structure Determination

description: Related Article: Roman Schowner, Janis V. Musso, Wolfgang Frey, Michael R. Buchmeiser|2021|Organometallics|40|3145|doi:10.1021/acs.organomet.1c00373
DOI: 10.5517/ccdc.csd.cc287d20
title: CCDC 2092471: Experimental Crystal Structure Determination

description: Related Article: Pratik Nariya, Falguni Shukla, Hitarthi Vyas, Ranjitsinh Devkar, Sonal Thakore|2022|J.Mol.Struct.|1248|131508|doi:10.1016/j.molstruc.2021.131508
DOI: 10.25493/4n71-6sx
title: Probabilistic cytoarchitectonic map of Area Id10 (Insula) (v4.0)

description: This dataset contains cytoarchitectonic probability maps of the Area Id10 (Insula) in the single subject template of the MNI (“Colin 27”) and the MNI ICBM 152 reference space, as part of the Julich-Brain atlas. The area was identified using cytoarchitectonic analysis on cell-body-stained histological sections in both hemispheres of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The maps show the probability to belong to the Area Id10 (Insula) for each voxel of the reference spaces. Data is provided in the NifTi format. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create probabilistic brain maps. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Delineations of the Area Id10 (Insula) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2021) [Data set, v2.9] [DOI: 10.25493/VSMK-H94](https://doi.org/10.25493/VSMK-H94)
DOI: 10.25493/jmcr-znq
title: Probabilistic cytoarchitectonic map of Area Id9 (Insula) (v4.0)

description: This dataset contains cytoarchitectonic probability maps of the Area Id9 (Insula) in the single subject template of the MNI (“Colin 27”) and the MNI ICBM 152 reference space, as part of the Julich-Brain atlas. The area was identified using cytoarchitectonic analysis on cell-body-stained histological sections in both hemispheres of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The maps show the probability to belong to the Area Id9 (Insula) for each voxel of the reference spaces. Data is provided in the NifTi format. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create probabilistic brain maps. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Delineations of the Area Id9 (Insula) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2021) [Data set, v2.9] [DOI: 10.25493/VSMK-H94](https://doi.org/10.25493/VSMK-H94)
DOI: 10.5281/zenodo.5544966
title: CoVaxxy Tweet IDs data set

description: A collection of Tweet IDs related to Covid-19 Vaccines, gathered from Twitter since Jan 4, 2021. Please see https://arxiv.org/abs/2101.07694 for more information.